Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Suicide candidate genes associated with bipolar disorder and schizophrenia: An exploratory gene expression profiling analysis of post-mortem prefrontal cortex

Suicide candidate genes associated with bipolar disorder and schizophrenia: An exploratory gene... Background: Suicide is an important and potentially preventable consequence of serious mental disorders of unknown etiology. Gene expression profiling technology provides an unbiased approach to identifying candidate genes for mental disorders. Microarray studies with post-mortem prefrontal cortex (Brodmann's Area 46/10) tissue require larger sample sizes. This study poses the question: to what extent are differentially expressed genes for suicide a diagnostic specific set of genes (bipolar disorder vs. schizophrenia) vs. a shared common pathway? Results: In a reanalysis of a large set of Affymetrix Human Genome U133A microarray data, gene expression levels were compared between suicide completers vs. non-suicide groups within a diagnostic group, namely Bipolar disorder (N = 45; 22 suicide completers; 23 non-suicide) or Schizophrenia (N = 45; 10 suicide completers ; 35 non-suicide). Among bipolar samples, 13 genes were found and among schizophrenia samples, 70 genes were found as differentially expressed. Two genes, PLSCR4 (phospholipid scramblase 4) and EMX2 (empty spiracles homolog 2 (Drosophila)) were differentially expressed in suicide groups of both diagnostic groups by microarray analysis. By qRT-PCR, PLSCR4 and EMX2 were significantly down-regulated in the schizophrenia suicide completers, but could not be confirmed in bipolar disorder. Conclusion: This molecular level analysis suggests that diagnostic specific genes predominate to shared genes in common among suicide vs. non-suicide groups. These differentially expressed, candidate genes are neural correlates of suicide, not necessarily causal. While suicide is a complex endpoint with many pathways, these candidate genes provide entry points for future studies of molecular mechanisms and genetic association studies to test causality. Page 1 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 This database contains clinical information and microar- Background Suicide is the eleventh leading cause of death for all Amer- ray data from 12 independent studies with post-mortem icans with an age-adjusted annual rate of 10.5 per brain tissues of depression, bipolar disorder, schizophre- 100,000 in 2003 [1]. More than 90% of suicide compl- nia, and unaffected control cohorts [15]. In this study, we eters have a psychiatric disorder and mood related disor- reanalyzed this large microarray data set of bipolar disor- ders are the most common disease associated with suicide der and schizophrenia patients. The question posed is, "to [2,3]. Patients suffering with bipolar disorder and schizo- what extent are differentially expressed genes for suicide phrenia have greatly increased rates of suicide with specific to diagnosis (bipolar disorder vs. schizophrenia) approximately 10% of patients dying of suicide [4,5]. vs. a shared common pathway?" Bipolar disorder and schizophrenia share common risk factors for suicide completion such as depression, previ- Results ous suicide attempts, hopelessness, substance abuse, agi- Suicide candidate genes in bipolar disorder and tation, and poor adherence to treatment [4,5]. Suicide is a schizophrenia complex endpoint with many factors and pathways lead- Chi square tests of association indicated no difference in ing to death [6]. The hypothesis of a shared causation for demographic variables between suicide vs. non-suicide suicide suggests common pathways and genes may func- subgroups within bipolar disorder. In contrast, age and tion as susceptibility factors in both disorders. Alterna- smoking showed significant differences with suicide vs. tively, there could be specific distinct pathways within a non-suicide in the schizophrenia subgroup. Also, brain diagnostic group. pH and sex significantly affected the expression levels of the differentially expressed genes between suicide vs. non- Microarray technology provides an unbiased approach to suicide groups within schizophrenia (Table 1). As shown the molecular causes of psychiatric disorders by examin- in Table 1, none of these variables met the two criteria for ing the gene expression profile of cases vs. controls [7,8]. inclusion as covariates in the two disorders (see meth- Recent microarray studies identified differentially ods). Therefore, we used no covariates in the omnibus expressed genes between suicide and depression patients model to provide a generalizable, single, same model for vs. normal controls [9-11]. However, due to the small both schizophrenia and bipolar disorder. Between the sui- magnitude of the differential gene expression, the genetic cide vs. non-suicide groups within bipolar disorder, a heterogeneity of these mental disorders, and the mixed total of 13 genes were differentially expressed (Table 2). cellular nature of the brain tissue available [12], microar- Among these genes, 10 genes were down-regulated and 3 ray studies with small sample sizes are prone to generate genes including gamma-amino butyric acid A receptor, α5 many false positive results [13]. Analysis of larger data sets subunit (GABRA5) were up-regulated. pooled from independent studies increase the statistical power to find differentially expressed genes with small Between the suicide vs. non-suicide group within schizo- effect sizes in microarray studies. Recently, a large micro- phrenia, 70 genes were differentially expressed (Table 3). array data set generated by the Stanley Medical Research Most of these genes were down-regulated. From the above Institute has become available online (SMRIDB) [14]. lists of differentially expressed genes, within diagnostic Table 1: Demographic factors of the suicide group and the non-suicide group in bipolar disorder and schizophrenia Bipolar disorder (n = 45) Schizophrenia (n = 45) 1 2 1 2 Suicide n = 22 Non-suicide n = 23 vs suicide vs expression Suicide n = 10 Non-suicide n = 35 vs suicide vs expression Age 44.3 ± 10.9 45.4 ± 11.3 n.s n.s 34.5 ± 7.6 44.3 ± 9.1 0.003 n.s Sex (M/F) 11/11 12/11 n.s n.s 6/4 28/7 n.s < 0.001 PMI 36.8 ± 19.3 36.8 ± 16.9 n.s n.s 35.3 ± 17.9 31.4 ± 15.0 n.s n.s Brain pH 6.4 ± 0.3 6.4 ± 0.3 n.s < 0.0001 6.4 ± 0.3 6.4 ± 0.3 n.s 0.005 Smoking 8/9/5 13/6/4 n.s n.s 3/2/5 25/5/5 0.03 n.s Alcohol 2/7/4/2/5/2/0 4/4/2/3/5/4/1 n.s n.s 3/2/1/0/2/2 9/7/4/4/3/8 n.s n.s Drug abuse 7/3/4/4/1/3 9/1/2/2/2/7 n.s n.s 4/1/1/0/2/2/0 17/3/2/3/3/5/2 n.s n.s P-values less than 0.05 were considered significant. P-values listed represent the lowest. FDR P-values less than 0.05 were considered significant. Smoking; Yes/No/Unknown Alcohol; Little or none/Social/Moderate drinking in past/Moderate drinking in present/Heavy drinking in past/Heavy drinking in present. Drug abuse; Little or none/Social/Moderate drug use in past/Moderate drug use in present/Heavy drug use in past/Heavy drug use in present/ Unknown. The continuous variables represent the mean ± S.D. n.s., not significant. Page 2 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 Table 2: Summary of differentially expressed genes between the suicide completer group vs. non-suicide group in bipolar disorder with fold change relative to non-suicide group. 1 2 3 Gene symbol Description FC P-value P/A call TM4SF1 transmembrane 4 L six family member 1 -1.6 0.07 P CHI3L1 chitinase 3-like 1 (cartilage glycoprotein-39) -1.5 0.05 P EMX2 empty spiracles homolog 2 (Drosophila) -1.4 0.09 P LDLR low density lipoprotein receptor -1.4 0.05 P ZIC1 Zic family member 1 (odd-paired homolog, Drosophila) -1.4 0.05 P PLSCR4 phospholipid scramblase 4 -1.4 0.05 P ZHX2 zinc fingers and homeoboxes 2 -1.4 0.07 P MRNA; cDNA DKFZp586B211 (from clone DKFZp586B211) -1.4 0.09 P TIMP1 TIMP metallopeptidase inhibitor 1 -1.3 0.05 A NAV2 neuron navigator 2 -1.3 0.05 P TRIM23 tripartite motif-containing 23 1.4 0.09 P STCH stress 70 protein chaperone, microsome-associated, 60 kDa 1.3 0.09 P GABRA5 gamma-aminobutyric acid (GABA) A receptor, alpha 5 1.3 0.09 P FC represents ratio of geometric means between suicides vs. non-suicide. P-values were adjusted by FDR. Absent (A) or Present (P) call by MAS 5 groups, two genes overlapped (Fig. 1A). Specifically, the commonly over-represented biological process in our sui- phospholipid scramblase 4 (PLSCR4) and empty spiracles cide candidate gene list for schizophrenia, including the homolog 2, Drosophila (EMX2) genes were down-regu- glial, high affinity, glutamate transporter (SLC1A3). In lated in both suicide groups compared to the non-suicide contrast, no significantly over-represented biological groups. As negative controls, the normalization control process group emerged with suicide candidate genes in probe set of 100 genes were tested by the same ANOVA the bipolar disorder group. model, and no genes met our statistical criteria (Fold Change ≥ |1.3| and FDR < 0.1) between the suicide group Additionally, we analyzed the list of differentially vs. non-suicide group within bipolar or schizophrenia expressed genes for each diagnosis by the Ingenuity Path- diagnostic categories. ways Analysis (IPA) software to identify biological path- ways and networks. We identified distinct signaling Real time-PCR (RT-PCR) tested the validity of these two networks from suicide candidate genes that included the shared, differentially expressed genes in the available sub- EMX2 gene in both disorders (Fig. 3). In bipolar disorder, set of the microarrayed samples. In the schizophrenia sui- the pathway perspective suggested a signaling network cide group, the EMX2 and PLSCR4 expression levels were related to both cellular movement and cell to cell signal- significantly down-regulated by comparison of mean ing, with interactions encompassing 10 differentially expression levels (EMX2 t(9) = 2.42, p = 0.02; PLSCR4 expressed, suicide candidate genes (Fig. 3A). By contrast, t(18) = 3.77, p = 0.0005) in the suicide group compared in schizophrenia patients, the differentially expressed to the non-suicide group (Fig 1B). The estimated fold genes were related in a cell death signaling network (Fig. changes in the suicide group were -1.51 for EMX2 and - 3B). 2.16 for PLSCR4 relative to the non-suicide group. These differences were consistent with our microarray data. Discussion However, in bipolar disorder, these two genes could not In this re-analysis study, we explored suicide candidate be validated in a subset of tissues available from the orig- genes associated with bipolar disorder and schizophrenia inal microarray study patients (Fig. 1B). These results using an unbiased genome-wide expression profiling highlight the overall findings that few common differen- strategy. To identify suicide specific effects on the expres- tially genes for suicide vs. non-suicide exist between diag- sion level of genes, we compared a suicide completers nostic groups. group to a non-suicide group within the same diagnostic category. The most important finding of this study is the Biological process in the differentially expressed genes identification of suicide candidate gene lists for bipolar Functional annotation of the differentially expressed disorder and schizophrenia with only two differentially genes by Gene Ontology indicated that 9 biological proc- expressed (suicide vs. non-suicide) genes in both bipolar esses were significantly overrepresented (at level 4; P < and schizophrenia cohorts namely PLSCR4 and EMX2 by 0.05) among the suicide candidate genes in the schizo- microarray analysis. The overlap of the two gene lists is phrenia cohort (Fig. 2). The transport genes were the most small, suggesting few common, shared genes. For schizo- Page 3 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 Table 3: Summary of differentially expressed genes between the suicide completer group vs. non-suicide group in schizophrenia with fold change relative to non-suicide group. 1 2 3 Gene symbol Description FC FDR P/A call GJA1 gap junction protein, alpha 1, 43 kDa (connexin 43) -2.0 0.02 P MT1X metallothionein 1X -1.9 0.04 P MT1M metallothionein 1M -1.9 0.06 P AGXT2L1 alanine-glyoxylate aminotransferase 2-like 1 -1.9 0.03 P SDC4 syndecan 4 (amphiglycan, ryudocan) -1.7 0.01 P SOX9 SRY (sex determining region Y)-box 9 -1.7 0.02 P NTRK2 neurotrophic tyrosine kinase, receptor, type 2 -1.7 0.02 P HSPB1 heat shock 27 kDa protein 1 -1.7 0.04 P EIF5A eukaryotic translation initiation factor 5A -1.7 0.06 P EFEMP1 EGF-containing fibulin-like extracellular matrix protein 1 -1.6 0.04 P ID4 inhibitor of DNA binding 4, dominant negative helix-loop-helix protein -1.6 0.02 P APOE apolipoprotein E -1.6 0.03 P PLSCR4 phospholipid scramblase 4 -1.6 0.03 P AGT angiotensinogen (serpin peptidase inhibitor, clade A, member 8) -1.6 0.02 P TUBB2B tubulin, beta 2B -1.5 0.04 P MT1E metallothionein 1E (functional) -1.5 0.03 P FAM107A family with sequence similarity 107, member A -1.5 0.03 P SLC4A4 solute carrier family 4, sodium bicarbonate cotransporter, member 4 -1.5 0.05 P GLUL glutamate-ammonia ligase (glutamine synthetase) -1.5 0.04 P ALDH1L1 aldehyde dehydrogenase 1 family, member L1 -1.5 0.02 P DDIT4 DNA-damage-inducible transcript 4 -1.5 0.04 P MT1G metallothionein 1G -1.5 0.04 P SLC1A3 solute carrier family 1 (glial high affinity glutamate transporter), member 3 -1.5 0.08 P PPAP2B phosphatidic acid phosphatase type 2B -1.5 0.05 P EMX2 empty spiracles homolog 2 (Drosophila) -1.5 0.05 P TP53BP2 tumor protein p53 binding protein, 2 -1.5 0.03 P MT1H metallothionein 1H -1.5 0.02 P APOLD1 apolipoprotein L domain containing 1 -1.4 0.08 P FGFR3 fibroblast growth factor receptor 3 (achondroplasia, thanatophoric dwarfism) -1.4 0.03 P FXYD1 FXYD domain containing ion transport regulator 1 (phospholemman) -1.4 0.00 P NOTCH2 Notch homolog 2 (Drosophila) -1.4 0.03 P METTL7A methyltransferase like 7A -1.4 0.05 P SLC14A1 solute carrier family 14 (urea transporter), member 1 (Kidd blood group) -1.4 0.06 P BBOX1 butyrobetaine (gamma), 2-oxoglutarate dioxygenase 1 -1.4 0.03 P MT1F metallothionein 1F (functional) -1.4 0.05 P EPHX1 epoxide hydrolase 1, microsomal (xenobiotic) -1.4 0.02 P SLC7A11 solute carrier family 7, (cationic amino acid transporter, y+ system) member 11 -1.4 0.05 P TPD52L1 tumor protein D52-like 1 -1.4 0.02 P RHOBTB3 Rho-related BTB domain containing 3 -1.4 0.05 P VIL2 villin 2 (ezrin) -1.4 0.03 P NDRG2 NDRG family member 2 -1.4 0.06 P CLDN10 claudin 10 -1.4 0.06 P NTSR2 neurotensin receptor 2 -1.4 0.03 A ITPKB inositol 1,4,5-trisphosphate 3-kinase B -1.4 0.10 P SDC2 syndecan 2 (heparan sulfate proteoglycan 1, cell surface-associated, fibroglycan) -1.4 0.03 P IL17RB interleukin 17 receptor B -1.3 0.03 P SPON1 spondin 1, extracellular matrix protein -1.3 0.02 P LOC645745 metallothionein 1H-like protein -1.3 0.03 P ITGB4 integrin, beta 4 -1.3 0.03 P MLC1 megalencephalic leukoencephalopathy with subcortical cysts 1 -1.3 0.05 P ATP1A2 ATPase, Na+/K+ transporting, alpha 2 (+) polypeptide -1.3 0.05 P RAB31 RAB31, member RAS oncogene family -1.3 0.05 P ATP1B2 ATPase, Na+/K+ transporting, beta 2 polypeptide -1.3 0.05 P AHCYL1 S-adenosylhomocysteine hydrolase-like 1 -1.3 0.03 P CX3CR1 chemokine (C-X3-C motif) receptor 1 2.1 0.02 P C3 complement component 3 1.6 0.02 P CRH corticotropin releasing hormone 1.6 0.05 P ABCG2 ATP-binding cassette, sub-family G (WHITE), member 2 1.5 0.03 P Page 4 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 Table 3: Summary of differentially expressed genes between the suicide completer group vs. non-suicide group in schizophrenia with fold change relative to non-suicide group. (Continued) DUSP6 dual specificity phosphatase 6 1.5 0.04 P NPY neuropeptide Y 1.4 0.03 P SLC25A23 solute carrier family 25 (mitochondrial carrier; phosphate carrier), member 23 1.4 0.03 A LAPTM5 lysosomal associated multispanning membrane protein 5 1.4 0.03 P TNFSF10 tumor necrosis factor (ligand) superfamily, member 10 1.4 0.01 P TYROBP TYRO protein tyrosine kinase binding protein 1.4 0.03 P CD74 CD74 molecule, major histocompatibility complex, class II invariant chain 1.4 0.03 P P2RY13 purinergic receptor P2Y, G-protein coupled, 13 1.3 0.02 A CSF1R colony stimulating factor 1 receptor 1.3 0.03 P HLA-DRA major histocompatibility complex, class II, DR alpha 1.3 0.04 A HLA-DPA1 major histocompatibility complex, class II, DP alpha 1 1.3 0.05 P A2M alpha-2-macroglobulin 1.3 0.03 P FC represents ratio of geometric means between suicides vs. non-suicide. P-values were adjusted by FDR. Absent (A) or Present (P) detection call by MAS5 statistical algorithm phrenia, but not bipolar disorder, the differential expres- tion of gamma-amino butyric acid A receptor, α5 sion of PLSCR4 and EMX2 was confirmed by RT-PCR. The (GABRA5) subunit gene in suicide associated with bipolar Phospholipid scramblase (PLSCR) is an integral mem- disorder, confirming a previous report [10]. The expres- 2+ brane protein that catalyzes Ca -induced bidirectional sion levels of two glutamate-related genes, Glutamate- movement of phospholipids [16]. Four isoforms have ammonia ligase (GLUL) and glial high affinity glutamate been cloned, and PLSCR4 was the major isoform transporter member 3 (SLC1A3) were decreased in suicide expressed in the brain [17]. However, the biological role completers with schizophrenia. The serotonergic and of the PLSCR4 remains unknown. While speculative, the noradrenergic systems have been suggested to be associ- changes in phospholipids membrane composition might ated with suicide [22]. However, no genes related to these have pleiotropic effects as evidence suggests that mem- two neurotransmitter systems were identified, consistent brane composition can change G protein coupled recep- with a previous report [23]. This negative finding may sug- tors' functioning and downstream effector signalling gest indirect effects on these neurotransmitter systems. [18,19] as well as voltage-dependent K+ channels [20]. EMX2 is a homeodomain containing transcription factor, Genetic linkage studies have identified several loci associ- which plays a crucial role in forebrain patterning and ated with suicidal behaviors in bipolar disorders. Signifi- development in mouse models [21]. This finding suggests cant and suggestive linkages for suicide were mapped on a possible neurodevelopmental process whereby variation chromosome 2, 5, and 10 from 162 bipolar pedigrees in forebrain development may be a risk factor for suicide [24]. Among this study's suicide candidate genes associ- completion behaviour associated with schizophrenia. Of ated with bipolar disorder, the tripartite motif-containing note, these differentially expressed genes are neural corre- 23 (TRIM23) gene is located close to the significantly lates of suicide and not necessarily causal. They could be linked D5S1725 marker on chromosome 5. Another large epiphenomenon. The questions remain of 1) how these scale genetic linkage study for bipolar disorder using 1060 genes function to influence suicide and 2) what interme- individuals identified linkage on chromosome 10q25.3 diate phenotype would be appropriate to demonstrate for suicide attempts [25]. The microarray differentially their possibly causal role. expressed candidate gene EMX2 is included in this region of interest. Therefore, these two genes may be apt for Microarray studies with small sample sizes result in low future genetic association studies for suicide associated statistical power and its attendant "noise discovery". This with bipolar disorder, proving causation. analysis and post-mortem sample size with replicates is noteworthy for an adequately powered sample to detect While this reanalysis study has the strengths of a larger 1.3 fold changes, improving sensitivity, reliability, and sample size, independent replicates, and well character- signal to noise issues. ized patient samples from specific areas of cortex, the find- ing should be interpreted cautiously as this study has Previous smaller microarray studies have suggested that some limitations. First, the mixed cellular nature of the GABA receptor subunits and glutamate-related genes brain samples might lower sensitivity due to dilutional were differentially expressed in bipolar disorder and effects as opposed to pure neuronal cells of a specific cor- schizophrenia as well as in suicide completers associated tical layer [12]. In general, most microarray studies with with these disorders [8,10]. We identified the up-regula- post-mortem brain tissues find fold changes of less than 2 Page 5 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 Di at level 4 non-suicide in schizophrenia Figure 2 stributiof on of differ gene onto entially expr logy gro coh essed oups fo rts genes betw r biological een sui proce cide svs. s Distribution of gene ontology groups for biological process at level 4 of differentially expressed genes between suicide vs. non- suicide in schizophrenia cohorts. should be considered as provisional until confirmed by replications in independent sets of biological samples. Conclusion By reanalyzing a large microarray dataset, a list of differen- tially expressed candidate genes for suicide within bipolar disorder or within schizophrenia have been identified. The overlap of genes in common among these two gene lists is small, with a larger number of disorder specific genes being found. This finding suggests that disorder- D Figure 1 ifferentially expressed genes between suicide completer vs specific pathways predominate over common pathways at Differentially expressed genes between suicide completer the molecular level. Two novel candidate genes, PLSCR4 vs. non-suicide group in bipolar disorder and schizophre- nia cohorts. (A) Venn diagram of differentially expressed genes and EMX2, were confirmed as differentially expressed in between suicide completer vs. non-suicide groups within bipolar schizophrenia between suicide completers vs. non-suicide and schizophrenia. (B) Mean expression levels of PLSCR4 (phos- groups. pholipid scramblase 4) and EMX2 (empty spiracles homolog 2 (Drosophila)) mRNA transcripts were determined for suicide completers vs. non-suicide groups within both bipolar disorder Methods and schizophrenia in prefrontal cortex by qRT-PCR. The bars rep- Microarray data and Patient Samples resent mean ± SEM. In schizophrenia, the mean expression levels The brain tissues were meticulously collected in a stand- of both genes were significantly down-regulated in the suicide group (n = 5) relative to the non-suicide cases (n = 25) by one- ardized manner via pathologists in the offices of the Med- tailed, t-tests for unequal variances (EMX2 t(9) = 2.42, p = 0.02; ical Examiner in several states with the families' PLSCR4 t(18) = 3.77, p = 0.0005). The estimated fold changes in permission under the aegis of the Stanley Foundation the suicide group were -1.51 for EMX2 and -2.16 for PLSCR4 rela- tive to the non-suicide group, consistent with our microarray Brain Collection (Array Collection plus Consortium Col- data. In the bipolar disorder samples, no significant differences in lection) [26]. The selection of specimens, clinical infor- mean expression levels for either gene were found between the mation, diagnoses of patients, and processing of tissues suicide (n = 14 for EMX2; n = 12 for PLSCR4) vs. non-suicide cases (n = 15 for EMX2 ; n = 11 for PLSCR4) by unequal variance t-tests. were conducted by Stanley Foundation Consortium as * p < .05 ; *** p < .001. described previously [26]. Gene expression profiling uti- lized post-mortem prefrontal cortex (Brodmann's Area 46/10) mRNA and Affymetrix Human Genome U133 Set fold, including this study. Second, although smoking, A (HGU133A) using standardized techniques as alcohol, and drug abuse were measured as confounding described [26,27]. The prefrontal cortex was selected as factors, all possible unmeasured, confounding variables the region of interest due to its role in executive function- for suicide cannot be formally excluded, such as severity ing, impulsivity ("lack of premeditation"), and decision of illness (especially the last episode for suicide compl- making. Disadvantageous decision making and impulsiv- eters), personality traits, hopelessness, agitation, depres- ity have been found to increase the risk of suicide [28,29]. sive symptoms, and stress. Third, these findings are correlational and not causal. Fourth, these gene lists Page 6 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 Ingenuity pathway ni Figure 3 a (B) analysis of EMX2 and differentially expressed suicide candidate genes in bipolar disorder (A) and schizophre- Ingenuity pathway analysis of EMX2 and differentially expressed suicide candidate genes in bipolar disorder (A) and schizophrenia (B). The Stanley Foundation's microarray database is an anon- repeated microarray data from each patient were treated ymous, de-identified dataset without any protected health as technical replicates. information. Patients' demographic variables used in this Statistical analysis of microarray data study are listed in Table 1. Microarray data was analyzed by a statistical method The robust multi-array averages (RMA)-normalized described previously with slight modifications [26]. microarray data from four independent studies were Briefly the following steps were followed within each downloaded from the SMRIDB. Microarray data from the diagnostic group (see Table 1). First, the differentially same platform, Affymetrix Human Genome U133 Set A expressed genes between suicide completers vs. non-sui- (HGU133A), were used to avoid platform-to-platform cide groups were filtered by average fold change (FC ≥ variation. The platform contains 22,215 probe sets. Qual- |1.3|) using the BRB-array tool[30] without covariates. ity control analyses for each chip were described previ- Second, the influence of continuous demographic varia- ously [15] bles (such as age, post-mortem interval (PMI) and brain pH) with the nominal variable suicide was tested using For the bipolar disorder cohort, the total dataset consisted ANOVA. Then, categorical variables such as sex, smoking, of 49 suicide completers' gene chips and 58 non-suicide alcohol and drug abuse were tested using chi square tests gene chips, while for schizophrenia cohort, the total data- of association (Statview software SAS, Cary, NC). In addi- set consisted of 22 suicide completer gene chips and 89 tion, correlation analyses of the demographic factors with non-suicide chips. Among 45 bipolar samples, there were expression levels of the differentially expressed probe sets 22 suicide cases, and 23 non-suicide cases. Among 45 from step 1 were performed. Continuous variables were schizophrenia patients, there were 10 suicide cases, and analyzed by Spearman's rank correlation and categorical 35 non-suicide cases. Two to three microarray chip data- variables were tested by ANOVA. P-values were adjusted sets were generated from the each patient's sample. These by False Discovery Rate (FDR) in both tests [31]. Third, significant confounding factors were tested as possible Page 7 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 covariates for ANCOVA model inclusion with the follow- cohort, there were 5 suicides and 27 non-suicide cases. ing criteria: The variable was required to show both 1) sig- These samples were matched by age, race, gender, PMI, nificant association with suicide as well as 2) significant brain pH, side of the brain and quality of RNA. Reactions correlation with expression levels of the differentially were quantified by the comparative Ct method using expressed genes. However, no variables met the criteria in SDS2.2 software (ABI). This RT-PCR data was also statisti- both disorder groups. Therefore, no covariates were used cally analyzed by the amplification plot method using the in the omnibus ANOVA, using the factor as suicide vs. Data Analysis for Real Time PCR (DART-PCR) approach non-suicide. As an exploratory analysis, a more liberal [34]. This method identified outliers in amplification effi- FDR P-value (< 0.1) of significance was selected for ciency by ANOVA and calculated mean expression levels. expression level differences as previously described Statistical differences in expression levels between groups, [32,33], using the BRB array software tool's FDR default namely suicide completers vs. non-suicides within a diag- setting. For negative controls, we performed statistical nostic group, were tested by one-tail, t-test with unequal analysis with the HGU133A normalization control probe variances (Microsoft Excel) as described [35,36]. To esti- sets using the same ANCOVA models, ensuring the adjust- mate average fold changes between groups, the mean ment did not produce "noise" or aberrant false positives. expression values from the DART-PCR approach were The Microarray Suite, version 5.0 (MAS5) software was used. The alternative 2(-Delta Delta Ct) method [37] for used to filter genes with low expression levels as either estimating fold change (using all the data without exclu- present or absent, applying the detection call statistical sions) verified this fold change estimate. As both methods algorithm. This algorithm suggests whether a gene is gave similar estimates, only the DART-PCR approach esti- present or absent. mates from mean expression levels were reported. A power analysis estimated the sample sizes for detection Functional annotation of a 1.3 fold change in a gene with a significance criterion The differentially expressed genes were functionally anno- of P-value = 0.001 and a power of 0.90 using a previously tated using the DAVID integrated database query tool[38] described method [13]. This analysis estimated a mini- and by the over-representational analysis method [39]. mum sample size of 27 cases per group for comparing sui- Functional annotations were based on biological process cide completers vs. non-suicide groups within bipolar of Gene Ontology (GO) Consortium[40] at level 4. P-val- disorder and 21 samples per group for comparing suicide ues less than 0.05 were considered significant. completers vs. non-suicide completers within schizophre- nia. Pathway Analysis Biologically relevant networks were drawn from the lists Real-time quantitative PCR of genes that were differentially expressed in bipolar dis- Total RNA from the dorsolateral prefrontal cortex (Brod- order and schizophrenia. This data was generated through mann area 46) of the Array Collection was used for this the use of Ingenuity Pathways Analysis (IPA) [41], a web- experiment. Complementary DNA was synthesized from delivered application that enables the visualization and DNA-free RNA with a random hexamer primer and Super- analysis of biologically relevant networks to discover, vis- script III First-Strand Synthesis System according to the ualize, and explore relevant networks. Expression data sets manufacturer's protocol (Invitrogen). Using a 384-well containing gene identifiers (Affymetrix probe set ID) and format with the Prism7900HT real-time detector (ABI), 2 their corresponding expression values as fold changes μl aliquots of (10×) QuantiTect Primer Assay (validated were uploaded as a tab-delimited text file. Each gene iden- primers to the specific gene of interest; Qiagen), 10 μl (2×) tifier was mapped to its corresponding gene object in the QuantiTect SYBR PCR Master mix (Qiagen), and 8 μl Ingenuity Pathways Knowledge Base. These genes, called cDNA were mixed together for 20 μl total reaction volume Focus Genes, were then used as the starting point for gen- and pipetted into single wells of the 384 PCR plate. erating biological networks. To start building networks, Amplification conditions were: (1) 1 cycle for 2 min at the application program queries the Ingenuity Pathways 50°C, (2) 1 cycle for 15 min at 95°C, and (3) 45 cycles for Knowledge Base for interactions between Focus Genes 15 s at 94°C, 30 s at 60°C and 30 s at 72°C and fluores- and all other gene objects stored in the knowledge base, cence was measured during the 72°C step for each cycle as and generates a set of networks. The program then com- recommended by the manufacturer. The β-2 microglobu- putes a score for each network according to the fit of the lin (B2M) was chosen as an endogenous control for the network to the set of focus genes. The score indicates the normalization of target genes as it was consistently likelihood of the Focus Genes in a given network being expressed in microarray samples. A total of 32 samples per found together due to random chance. A score of greater each disorder were used for this experiment and run in than 2 indicates that there is a less than 1 in 100 chance duplicate. In the bipolar disorder cohort, there were 14 that the Focus Genes were assembled randomly into a net- suicides and 18 non-suicide cases. In schizophrenia work due to random chance. The scores of the networks Page 8 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 SSAT by gene expression and genetic variation in suicide and generated from the lists of differentially expressed genes major depression. Arch Gen Psychiatry 2006, 63(1):35-48. were 24 for Bipolar disorder (Fig 3A) and 40 for Schizo- 12. Rossner MJ, Hirrlinger J, Wichert SP, Boehm C, Newrzella D, Hie- phrenia (Fig 3B). misch H, Eisenhardt G, Stuenkel C, von Ahsen O, Nave KA: Global transcriptome analysis of genetically identified neurons in the adult cortex. J Neurosci 2006, 26(39):9956-9966. Authors' contributions 13. Wei C, Li J, Bumgarner RE: Sample size for detecting differen- tially expressed genes in microarray experiments. BMC SK performed the analysis of the microarray data. HG Genomics 2004, 5(1):87. supervised the study. SK and HG wrote the manuscript. 14. SMRIDB: [https://www.stanleygenomics.org]. KC performed qRT-PCR validation experiment and SK 15. Higgs BW, Elashoff M, Richman S, Barci B: An online database for brain disease research. BMC Genomics 2006, 7:70. performed analysis of the data. AFB provided important 16. Ikeda M, Kihara A, Igarashi Y: Lipid asymmetry of the eukaryotic discussions. All authors read and approved of the final plasma membrane: functions and related enzymes. Biol Pharm version of manuscript. Bull 2006, 29(8):1542-1546. 17. Wiedmer T, Zhou Q, Kwoh DY, Sims PJ: Identification of three new members of the phospholipid scramblase gene family. Acknowledgements Biochim Biophys Acta 2000, 1467(1):244-253. This work is generously supported by NIH (RO1 MH067211) and NAR- 18. Alves ID, Salgado GF, Salamon Z, Brown MF, Tollin G, Hruby VJ: Phosphatidylethanolamine enhances rhodopsin photoactiva- SAD (HG). The post-mortem brain collection was developed over many tion and transducin binding in a solid supported lipid bilayer years through the generous permission of the families and the long-term as determined using plasmon-waveguide resonance spec- support of the Stanley Foundation Brain Collection by the Stanley Medical troscopy. Biophys J 2005, 88(1):198-210. Research Institute. We thank the Stanley Foundation Brain Collection and 19. Niu SL, Mitchell DC, Litman BJ: Optimization of receptor-G pro- tein coupling by bilayer lipid composition II: formation of its Neuropathology Consortium for making its microarray database publicly metarhodopsin II-transducin complex. J Biol Chem 2001, available and their foresight in generating this dataset through the collabo- 276(46):42807-42811. rative spirit and diligence of many investigators. In particular, we acknowl- 20. Schmidt D, Jiang QX, MacKinnon R: Phospholipids and the origin edge The Stanley Medical Research Institute's Investigator's, Drs. E. Fuller of cationic gating charges in voltage sensors. Nature 2006, 444(7120):775-779. Torrey, Robert H. Yolken, Michael B. Knable, Michael Elashoff, and Maree 21. Hamasaki T, Leingartner A, Ringstedt T, O'Leary DD: EMX2 regu- J. Webster along with their many collaborators, who made this work pos- lates sizes and positioning of the primary sensory and motor sible. We acknowledge Drs. C. Claasen and A. Kumar for helpful advice. areas in neocortex by direct specification of cortical progen- itors. Neuron 2004, 43(3):359-372. 22. Mann JJ: Neurobiology of suicidal behaviour. Nat Rev Neurosci References 2003, 4(10):819-828. 1. McKeown RE, Cuffe SP, Schulz RM: US suicide rates by age 23. Sibille E, Arango V, Galfalvy HC, Pavlidis P, Erraji-Benchekroun L, Ellis group, 1970-2002: an examination of recent trends. Am J Pub- SP, John Mann J: Gene expression profiling of depression and lic Health 2006, 96(10):1744-1751. suicide in human prefrontal cortex. Neuropsychopharmacology 2. Chen YW, Dilsaver SC: Lifetime rates of suicide attempts 2004, 29(2):351-361. among subjects with bipolar and unipolar disorders relative 24. Willour VL, Zandi PP, Badner JA, Steele J, Miao K, Lopez V, Mackin- to subjects with other Axis I disorders. Biol Psychiatry 1996, non DF, Mondimore FM, Schweizer B, McInnis MG, Miller EB, Dep- 39(10):896-899. aulo JR Jr., Gershon ES, McMahon FJ, Potash JB: Attempted Suicide 3. Oquendo MA, Currier D, Mann JJ: Prospective studies of suicidal in Bipolar Disorder Pedigrees: Evidence for Linkage to 2p12. behavior in major depressive and bipolar disorders: what is Biol Psychiatry 2007, 61(5):725-727. the evidence for predictive risk factors? Acta Psychiatr Scand 25. Cheng R, Juo SH, Loth JE, Nee J, Iossifov I, Blumenthal R, Sharpe L, 2006, 114(3):151-158. Kanyas K, Lerer B, Lilliston B, Smith M, Trautman K, Gilliam TC, Endi- 4. Hawton K, Sutton L, Haw C, Sinclair J, Deeks JJ: Schizophrenia and cott J, Baron M: Genome-wide linkage scan in a large bipolar suicide: systematic review of risk factors. Br J Psychiatry 2005, disorder sample from the National Institute of Mental 187:9-20. Health genetics initiative suggests putative loci for bipolar 5. Hawton K, Sutton L, Haw C, Sinclair J, Harriss L: Suicide and disorder, psychosis, suicide, and panic disorder. Mol Psychiatry attempted suicide in bipolar disorder: a systematic review of 2006, 11(3):252-260. risk factors. J Clin Psychiatry 2005, 66(6):693-704. 26. Prabakaran S, Swatton JE, Ryan MM, Huffaker SJ, Huang JT, Griffin JL, 6. Maris RW, Berman AL, Maltsberger JT: Summary and conclu- Wayland M, Freeman T, Dudbridge F, Lilley KS, Karp NA, Hester S, sions: What have we learned about suicide assessment and Tkachev D, Mimmack ML, Yolken RH, Webster MJ, Torrey EF, Bahn prediction? In Assessment and prediction of suicide Edited by: Maris S: Mitochondrial dysfunction in schizophrenia: evidence for RW, Berman AL, Maltsberger JT, Yufit RI. New York , Guilford Press; compromised brain metabolism and oxidative stress. Mol 1992:640–672. Psychiatry 2004, 9(7):684-97, 643. 7. Bunney WE, Bunney BG, Vawter MP, Tomita H, Li J, Evans SJ, Chou- 27. Iwamoto K, Kakiuchi C, Bundo M, Ikeda K, Kato T: Molecular char- dary PV, Myers RM, Jones EG, Watson SJ, Akil H: Microarray tech- acterization of bipolar disorder by comparing gene expres- nology: a review of new strategies to discover candidate sion profiles of postmortem brains of major mental vulnerability genes in psychiatric disorders. Am J Psychiatry disorders. Mol Psychiatry 2004, 9(4):406-416. 2003, 160(4):657-666. 28. Raust A, Slama F, Mathieu F, Roy I, Chenu A, Koncke D, Fouques D, 8. Mirnics K, Levitt P, Lewis DA: Critical appraisal of DNA micro- Jollant F, Jouvent E, Courtet P, Leboyer M, Bellivier F: Prefrontal arrays in psychiatric genomics. Biol Psychiatry 2006, cortex dysfunction in patients with suicidal behavior. Psychol 60(2):163-176. Med 2007, 37(3):411-419. 9. Gwadry FG, Sequeira A, Hoke G, Ffrench-Mullen JM, Turecki G: 29. Zermatten A, Van der Linden M, d'Acremont M, Jermann F, Bechara Molecular characterization of suicide by microarray analysis. A: Impulsivity and decision making. J Nerv Ment Dis 2005, Am J Med Genet C Semin Med Genet 2005, 133(1):48-56. 193(10):647-650. 10. Choudary PV, Molnar M, Evans SJ, Tomita H, Li JZ, Vawter MP, Myers 30. BRB array tool: [http://linus.nci.nih.gov/BRB-ArrayTools.html]. RM, Bunney WE Jr., Akil H, Watson SJ, Jones EG: Altered cortical 31. Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: glutamatergic and GABAergic signal transmission with glial a practical and powerful approach to multiple testing. J Royal involvement in depression. Proc Natl Acad Sci U S A 2005, Stat Soc Ser B 1995, 57(1):289-300. 102(43):15653-15658. 32. Hamatani T, Daikoku T, Wang H, Matsumoto H, Carter MG, Ko MS, 11. Sequeira A, Gwadry FG, Ffrench-Mullen JM, Canetti L, Gingras Y, Dey SK: Global gene expression analysis identifies molecular Casero RA Jr., Rouleau G, Benkelfat C, Turecki G: Implication of Page 9 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 pathways distinguishing blastocyst dormancy and activation. Proc Natl Acad Sci U S A 2004, 101(28):10326-10331. 33. Raponi M, Belly RT, Karp JE, Lancet JE, Atkins D, Wang Y: Microar- ray analysis reveals genetic pathways modulated by tipi- farnib in acute myeloid leukemia. BMC Cancer 2004, 4:56. 34. Peirson SN, Butler JN, Foster RG: Experimental validation of novel and conventional approaches to quantitative real-time PCR data analysis. Nucleic Acids Res 2003, 31(14):e73. 35. Yuan JS, Reed A, Chen F, Stewart CN Jr.: Statistical analysis of real-time PCR data. BMC Bioinformatics 2006, 7:85. 36. Ruxton GD: The unequal variance t-test is an underused alter- native to Student's t-test and the Mann-Whitney U test. Behav Ecol 2006, 17(4):688-690. 37. Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001, 25(4):402-408. 38. DAVID integrated database query tool: [http://david.abcc.ncifcrf.gov/ ]. 39. Dennis G Jr., Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 2003, 4(5):P3. 40. Gene Ontology Consortium: [http://www.geneontology.org]. 41. Ingenuity Pathways Analysis: [http://www.Ingenuity.com]. Publish with Bio Med Central and every scientist can read your work free of charge "BioMed Central will be the most significant development for disseminating the results of biomedical researc h in our lifetime." Sir Paul Nurse, Cancer Research UK Your research papers will be: available free of charge to the entire biomedical community peer reviewed and published immediately upon acceptance cited in PubMed and archived on PubMed Central yours — you keep the copyright BioMedcentral Submit your manuscript here: http://www.biomedcentral.com/info/publishing_adv.asp Page 10 of 10 (page number not for citation purposes) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Genomics Springer Journals

Suicide candidate genes associated with bipolar disorder and schizophrenia: An exploratory gene expression profiling analysis of post-mortem prefrontal cortex

Loading next page...
 
/lp/springer-journals/suicide-candidate-genes-associated-with-bipolar-disorder-and-l24qlYkZOU

References (40)

Publisher
Springer Journals
Copyright
Copyright © 2007 by Kim et al; licensee BioMed Central Ltd.
Subject
Life Sciences; Life Sciences, general; Microarrays; Proteomics; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics
eISSN
1471-2164
DOI
10.1186/1471-2164-8-413
pmid
17997842
Publisher site
See Article on Publisher Site

Abstract

Background: Suicide is an important and potentially preventable consequence of serious mental disorders of unknown etiology. Gene expression profiling technology provides an unbiased approach to identifying candidate genes for mental disorders. Microarray studies with post-mortem prefrontal cortex (Brodmann's Area 46/10) tissue require larger sample sizes. This study poses the question: to what extent are differentially expressed genes for suicide a diagnostic specific set of genes (bipolar disorder vs. schizophrenia) vs. a shared common pathway? Results: In a reanalysis of a large set of Affymetrix Human Genome U133A microarray data, gene expression levels were compared between suicide completers vs. non-suicide groups within a diagnostic group, namely Bipolar disorder (N = 45; 22 suicide completers; 23 non-suicide) or Schizophrenia (N = 45; 10 suicide completers ; 35 non-suicide). Among bipolar samples, 13 genes were found and among schizophrenia samples, 70 genes were found as differentially expressed. Two genes, PLSCR4 (phospholipid scramblase 4) and EMX2 (empty spiracles homolog 2 (Drosophila)) were differentially expressed in suicide groups of both diagnostic groups by microarray analysis. By qRT-PCR, PLSCR4 and EMX2 were significantly down-regulated in the schizophrenia suicide completers, but could not be confirmed in bipolar disorder. Conclusion: This molecular level analysis suggests that diagnostic specific genes predominate to shared genes in common among suicide vs. non-suicide groups. These differentially expressed, candidate genes are neural correlates of suicide, not necessarily causal. While suicide is a complex endpoint with many pathways, these candidate genes provide entry points for future studies of molecular mechanisms and genetic association studies to test causality. Page 1 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 This database contains clinical information and microar- Background Suicide is the eleventh leading cause of death for all Amer- ray data from 12 independent studies with post-mortem icans with an age-adjusted annual rate of 10.5 per brain tissues of depression, bipolar disorder, schizophre- 100,000 in 2003 [1]. More than 90% of suicide compl- nia, and unaffected control cohorts [15]. In this study, we eters have a psychiatric disorder and mood related disor- reanalyzed this large microarray data set of bipolar disor- ders are the most common disease associated with suicide der and schizophrenia patients. The question posed is, "to [2,3]. Patients suffering with bipolar disorder and schizo- what extent are differentially expressed genes for suicide phrenia have greatly increased rates of suicide with specific to diagnosis (bipolar disorder vs. schizophrenia) approximately 10% of patients dying of suicide [4,5]. vs. a shared common pathway?" Bipolar disorder and schizophrenia share common risk factors for suicide completion such as depression, previ- Results ous suicide attempts, hopelessness, substance abuse, agi- Suicide candidate genes in bipolar disorder and tation, and poor adherence to treatment [4,5]. Suicide is a schizophrenia complex endpoint with many factors and pathways lead- Chi square tests of association indicated no difference in ing to death [6]. The hypothesis of a shared causation for demographic variables between suicide vs. non-suicide suicide suggests common pathways and genes may func- subgroups within bipolar disorder. In contrast, age and tion as susceptibility factors in both disorders. Alterna- smoking showed significant differences with suicide vs. tively, there could be specific distinct pathways within a non-suicide in the schizophrenia subgroup. Also, brain diagnostic group. pH and sex significantly affected the expression levels of the differentially expressed genes between suicide vs. non- Microarray technology provides an unbiased approach to suicide groups within schizophrenia (Table 1). As shown the molecular causes of psychiatric disorders by examin- in Table 1, none of these variables met the two criteria for ing the gene expression profile of cases vs. controls [7,8]. inclusion as covariates in the two disorders (see meth- Recent microarray studies identified differentially ods). Therefore, we used no covariates in the omnibus expressed genes between suicide and depression patients model to provide a generalizable, single, same model for vs. normal controls [9-11]. However, due to the small both schizophrenia and bipolar disorder. Between the sui- magnitude of the differential gene expression, the genetic cide vs. non-suicide groups within bipolar disorder, a heterogeneity of these mental disorders, and the mixed total of 13 genes were differentially expressed (Table 2). cellular nature of the brain tissue available [12], microar- Among these genes, 10 genes were down-regulated and 3 ray studies with small sample sizes are prone to generate genes including gamma-amino butyric acid A receptor, α5 many false positive results [13]. Analysis of larger data sets subunit (GABRA5) were up-regulated. pooled from independent studies increase the statistical power to find differentially expressed genes with small Between the suicide vs. non-suicide group within schizo- effect sizes in microarray studies. Recently, a large micro- phrenia, 70 genes were differentially expressed (Table 3). array data set generated by the Stanley Medical Research Most of these genes were down-regulated. From the above Institute has become available online (SMRIDB) [14]. lists of differentially expressed genes, within diagnostic Table 1: Demographic factors of the suicide group and the non-suicide group in bipolar disorder and schizophrenia Bipolar disorder (n = 45) Schizophrenia (n = 45) 1 2 1 2 Suicide n = 22 Non-suicide n = 23 vs suicide vs expression Suicide n = 10 Non-suicide n = 35 vs suicide vs expression Age 44.3 ± 10.9 45.4 ± 11.3 n.s n.s 34.5 ± 7.6 44.3 ± 9.1 0.003 n.s Sex (M/F) 11/11 12/11 n.s n.s 6/4 28/7 n.s < 0.001 PMI 36.8 ± 19.3 36.8 ± 16.9 n.s n.s 35.3 ± 17.9 31.4 ± 15.0 n.s n.s Brain pH 6.4 ± 0.3 6.4 ± 0.3 n.s < 0.0001 6.4 ± 0.3 6.4 ± 0.3 n.s 0.005 Smoking 8/9/5 13/6/4 n.s n.s 3/2/5 25/5/5 0.03 n.s Alcohol 2/7/4/2/5/2/0 4/4/2/3/5/4/1 n.s n.s 3/2/1/0/2/2 9/7/4/4/3/8 n.s n.s Drug abuse 7/3/4/4/1/3 9/1/2/2/2/7 n.s n.s 4/1/1/0/2/2/0 17/3/2/3/3/5/2 n.s n.s P-values less than 0.05 were considered significant. P-values listed represent the lowest. FDR P-values less than 0.05 were considered significant. Smoking; Yes/No/Unknown Alcohol; Little or none/Social/Moderate drinking in past/Moderate drinking in present/Heavy drinking in past/Heavy drinking in present. Drug abuse; Little or none/Social/Moderate drug use in past/Moderate drug use in present/Heavy drug use in past/Heavy drug use in present/ Unknown. The continuous variables represent the mean ± S.D. n.s., not significant. Page 2 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 Table 2: Summary of differentially expressed genes between the suicide completer group vs. non-suicide group in bipolar disorder with fold change relative to non-suicide group. 1 2 3 Gene symbol Description FC P-value P/A call TM4SF1 transmembrane 4 L six family member 1 -1.6 0.07 P CHI3L1 chitinase 3-like 1 (cartilage glycoprotein-39) -1.5 0.05 P EMX2 empty spiracles homolog 2 (Drosophila) -1.4 0.09 P LDLR low density lipoprotein receptor -1.4 0.05 P ZIC1 Zic family member 1 (odd-paired homolog, Drosophila) -1.4 0.05 P PLSCR4 phospholipid scramblase 4 -1.4 0.05 P ZHX2 zinc fingers and homeoboxes 2 -1.4 0.07 P MRNA; cDNA DKFZp586B211 (from clone DKFZp586B211) -1.4 0.09 P TIMP1 TIMP metallopeptidase inhibitor 1 -1.3 0.05 A NAV2 neuron navigator 2 -1.3 0.05 P TRIM23 tripartite motif-containing 23 1.4 0.09 P STCH stress 70 protein chaperone, microsome-associated, 60 kDa 1.3 0.09 P GABRA5 gamma-aminobutyric acid (GABA) A receptor, alpha 5 1.3 0.09 P FC represents ratio of geometric means between suicides vs. non-suicide. P-values were adjusted by FDR. Absent (A) or Present (P) call by MAS 5 groups, two genes overlapped (Fig. 1A). Specifically, the commonly over-represented biological process in our sui- phospholipid scramblase 4 (PLSCR4) and empty spiracles cide candidate gene list for schizophrenia, including the homolog 2, Drosophila (EMX2) genes were down-regu- glial, high affinity, glutamate transporter (SLC1A3). In lated in both suicide groups compared to the non-suicide contrast, no significantly over-represented biological groups. As negative controls, the normalization control process group emerged with suicide candidate genes in probe set of 100 genes were tested by the same ANOVA the bipolar disorder group. model, and no genes met our statistical criteria (Fold Change ≥ |1.3| and FDR < 0.1) between the suicide group Additionally, we analyzed the list of differentially vs. non-suicide group within bipolar or schizophrenia expressed genes for each diagnosis by the Ingenuity Path- diagnostic categories. ways Analysis (IPA) software to identify biological path- ways and networks. We identified distinct signaling Real time-PCR (RT-PCR) tested the validity of these two networks from suicide candidate genes that included the shared, differentially expressed genes in the available sub- EMX2 gene in both disorders (Fig. 3). In bipolar disorder, set of the microarrayed samples. In the schizophrenia sui- the pathway perspective suggested a signaling network cide group, the EMX2 and PLSCR4 expression levels were related to both cellular movement and cell to cell signal- significantly down-regulated by comparison of mean ing, with interactions encompassing 10 differentially expression levels (EMX2 t(9) = 2.42, p = 0.02; PLSCR4 expressed, suicide candidate genes (Fig. 3A). By contrast, t(18) = 3.77, p = 0.0005) in the suicide group compared in schizophrenia patients, the differentially expressed to the non-suicide group (Fig 1B). The estimated fold genes were related in a cell death signaling network (Fig. changes in the suicide group were -1.51 for EMX2 and - 3B). 2.16 for PLSCR4 relative to the non-suicide group. These differences were consistent with our microarray data. Discussion However, in bipolar disorder, these two genes could not In this re-analysis study, we explored suicide candidate be validated in a subset of tissues available from the orig- genes associated with bipolar disorder and schizophrenia inal microarray study patients (Fig. 1B). These results using an unbiased genome-wide expression profiling highlight the overall findings that few common differen- strategy. To identify suicide specific effects on the expres- tially genes for suicide vs. non-suicide exist between diag- sion level of genes, we compared a suicide completers nostic groups. group to a non-suicide group within the same diagnostic category. The most important finding of this study is the Biological process in the differentially expressed genes identification of suicide candidate gene lists for bipolar Functional annotation of the differentially expressed disorder and schizophrenia with only two differentially genes by Gene Ontology indicated that 9 biological proc- expressed (suicide vs. non-suicide) genes in both bipolar esses were significantly overrepresented (at level 4; P < and schizophrenia cohorts namely PLSCR4 and EMX2 by 0.05) among the suicide candidate genes in the schizo- microarray analysis. The overlap of the two gene lists is phrenia cohort (Fig. 2). The transport genes were the most small, suggesting few common, shared genes. For schizo- Page 3 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 Table 3: Summary of differentially expressed genes between the suicide completer group vs. non-suicide group in schizophrenia with fold change relative to non-suicide group. 1 2 3 Gene symbol Description FC FDR P/A call GJA1 gap junction protein, alpha 1, 43 kDa (connexin 43) -2.0 0.02 P MT1X metallothionein 1X -1.9 0.04 P MT1M metallothionein 1M -1.9 0.06 P AGXT2L1 alanine-glyoxylate aminotransferase 2-like 1 -1.9 0.03 P SDC4 syndecan 4 (amphiglycan, ryudocan) -1.7 0.01 P SOX9 SRY (sex determining region Y)-box 9 -1.7 0.02 P NTRK2 neurotrophic tyrosine kinase, receptor, type 2 -1.7 0.02 P HSPB1 heat shock 27 kDa protein 1 -1.7 0.04 P EIF5A eukaryotic translation initiation factor 5A -1.7 0.06 P EFEMP1 EGF-containing fibulin-like extracellular matrix protein 1 -1.6 0.04 P ID4 inhibitor of DNA binding 4, dominant negative helix-loop-helix protein -1.6 0.02 P APOE apolipoprotein E -1.6 0.03 P PLSCR4 phospholipid scramblase 4 -1.6 0.03 P AGT angiotensinogen (serpin peptidase inhibitor, clade A, member 8) -1.6 0.02 P TUBB2B tubulin, beta 2B -1.5 0.04 P MT1E metallothionein 1E (functional) -1.5 0.03 P FAM107A family with sequence similarity 107, member A -1.5 0.03 P SLC4A4 solute carrier family 4, sodium bicarbonate cotransporter, member 4 -1.5 0.05 P GLUL glutamate-ammonia ligase (glutamine synthetase) -1.5 0.04 P ALDH1L1 aldehyde dehydrogenase 1 family, member L1 -1.5 0.02 P DDIT4 DNA-damage-inducible transcript 4 -1.5 0.04 P MT1G metallothionein 1G -1.5 0.04 P SLC1A3 solute carrier family 1 (glial high affinity glutamate transporter), member 3 -1.5 0.08 P PPAP2B phosphatidic acid phosphatase type 2B -1.5 0.05 P EMX2 empty spiracles homolog 2 (Drosophila) -1.5 0.05 P TP53BP2 tumor protein p53 binding protein, 2 -1.5 0.03 P MT1H metallothionein 1H -1.5 0.02 P APOLD1 apolipoprotein L domain containing 1 -1.4 0.08 P FGFR3 fibroblast growth factor receptor 3 (achondroplasia, thanatophoric dwarfism) -1.4 0.03 P FXYD1 FXYD domain containing ion transport regulator 1 (phospholemman) -1.4 0.00 P NOTCH2 Notch homolog 2 (Drosophila) -1.4 0.03 P METTL7A methyltransferase like 7A -1.4 0.05 P SLC14A1 solute carrier family 14 (urea transporter), member 1 (Kidd blood group) -1.4 0.06 P BBOX1 butyrobetaine (gamma), 2-oxoglutarate dioxygenase 1 -1.4 0.03 P MT1F metallothionein 1F (functional) -1.4 0.05 P EPHX1 epoxide hydrolase 1, microsomal (xenobiotic) -1.4 0.02 P SLC7A11 solute carrier family 7, (cationic amino acid transporter, y+ system) member 11 -1.4 0.05 P TPD52L1 tumor protein D52-like 1 -1.4 0.02 P RHOBTB3 Rho-related BTB domain containing 3 -1.4 0.05 P VIL2 villin 2 (ezrin) -1.4 0.03 P NDRG2 NDRG family member 2 -1.4 0.06 P CLDN10 claudin 10 -1.4 0.06 P NTSR2 neurotensin receptor 2 -1.4 0.03 A ITPKB inositol 1,4,5-trisphosphate 3-kinase B -1.4 0.10 P SDC2 syndecan 2 (heparan sulfate proteoglycan 1, cell surface-associated, fibroglycan) -1.4 0.03 P IL17RB interleukin 17 receptor B -1.3 0.03 P SPON1 spondin 1, extracellular matrix protein -1.3 0.02 P LOC645745 metallothionein 1H-like protein -1.3 0.03 P ITGB4 integrin, beta 4 -1.3 0.03 P MLC1 megalencephalic leukoencephalopathy with subcortical cysts 1 -1.3 0.05 P ATP1A2 ATPase, Na+/K+ transporting, alpha 2 (+) polypeptide -1.3 0.05 P RAB31 RAB31, member RAS oncogene family -1.3 0.05 P ATP1B2 ATPase, Na+/K+ transporting, beta 2 polypeptide -1.3 0.05 P AHCYL1 S-adenosylhomocysteine hydrolase-like 1 -1.3 0.03 P CX3CR1 chemokine (C-X3-C motif) receptor 1 2.1 0.02 P C3 complement component 3 1.6 0.02 P CRH corticotropin releasing hormone 1.6 0.05 P ABCG2 ATP-binding cassette, sub-family G (WHITE), member 2 1.5 0.03 P Page 4 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 Table 3: Summary of differentially expressed genes between the suicide completer group vs. non-suicide group in schizophrenia with fold change relative to non-suicide group. (Continued) DUSP6 dual specificity phosphatase 6 1.5 0.04 P NPY neuropeptide Y 1.4 0.03 P SLC25A23 solute carrier family 25 (mitochondrial carrier; phosphate carrier), member 23 1.4 0.03 A LAPTM5 lysosomal associated multispanning membrane protein 5 1.4 0.03 P TNFSF10 tumor necrosis factor (ligand) superfamily, member 10 1.4 0.01 P TYROBP TYRO protein tyrosine kinase binding protein 1.4 0.03 P CD74 CD74 molecule, major histocompatibility complex, class II invariant chain 1.4 0.03 P P2RY13 purinergic receptor P2Y, G-protein coupled, 13 1.3 0.02 A CSF1R colony stimulating factor 1 receptor 1.3 0.03 P HLA-DRA major histocompatibility complex, class II, DR alpha 1.3 0.04 A HLA-DPA1 major histocompatibility complex, class II, DP alpha 1 1.3 0.05 P A2M alpha-2-macroglobulin 1.3 0.03 P FC represents ratio of geometric means between suicides vs. non-suicide. P-values were adjusted by FDR. Absent (A) or Present (P) detection call by MAS5 statistical algorithm phrenia, but not bipolar disorder, the differential expres- tion of gamma-amino butyric acid A receptor, α5 sion of PLSCR4 and EMX2 was confirmed by RT-PCR. The (GABRA5) subunit gene in suicide associated with bipolar Phospholipid scramblase (PLSCR) is an integral mem- disorder, confirming a previous report [10]. The expres- 2+ brane protein that catalyzes Ca -induced bidirectional sion levels of two glutamate-related genes, Glutamate- movement of phospholipids [16]. Four isoforms have ammonia ligase (GLUL) and glial high affinity glutamate been cloned, and PLSCR4 was the major isoform transporter member 3 (SLC1A3) were decreased in suicide expressed in the brain [17]. However, the biological role completers with schizophrenia. The serotonergic and of the PLSCR4 remains unknown. While speculative, the noradrenergic systems have been suggested to be associ- changes in phospholipids membrane composition might ated with suicide [22]. However, no genes related to these have pleiotropic effects as evidence suggests that mem- two neurotransmitter systems were identified, consistent brane composition can change G protein coupled recep- with a previous report [23]. This negative finding may sug- tors' functioning and downstream effector signalling gest indirect effects on these neurotransmitter systems. [18,19] as well as voltage-dependent K+ channels [20]. EMX2 is a homeodomain containing transcription factor, Genetic linkage studies have identified several loci associ- which plays a crucial role in forebrain patterning and ated with suicidal behaviors in bipolar disorders. Signifi- development in mouse models [21]. This finding suggests cant and suggestive linkages for suicide were mapped on a possible neurodevelopmental process whereby variation chromosome 2, 5, and 10 from 162 bipolar pedigrees in forebrain development may be a risk factor for suicide [24]. Among this study's suicide candidate genes associ- completion behaviour associated with schizophrenia. Of ated with bipolar disorder, the tripartite motif-containing note, these differentially expressed genes are neural corre- 23 (TRIM23) gene is located close to the significantly lates of suicide and not necessarily causal. They could be linked D5S1725 marker on chromosome 5. Another large epiphenomenon. The questions remain of 1) how these scale genetic linkage study for bipolar disorder using 1060 genes function to influence suicide and 2) what interme- individuals identified linkage on chromosome 10q25.3 diate phenotype would be appropriate to demonstrate for suicide attempts [25]. The microarray differentially their possibly causal role. expressed candidate gene EMX2 is included in this region of interest. Therefore, these two genes may be apt for Microarray studies with small sample sizes result in low future genetic association studies for suicide associated statistical power and its attendant "noise discovery". This with bipolar disorder, proving causation. analysis and post-mortem sample size with replicates is noteworthy for an adequately powered sample to detect While this reanalysis study has the strengths of a larger 1.3 fold changes, improving sensitivity, reliability, and sample size, independent replicates, and well character- signal to noise issues. ized patient samples from specific areas of cortex, the find- ing should be interpreted cautiously as this study has Previous smaller microarray studies have suggested that some limitations. First, the mixed cellular nature of the GABA receptor subunits and glutamate-related genes brain samples might lower sensitivity due to dilutional were differentially expressed in bipolar disorder and effects as opposed to pure neuronal cells of a specific cor- schizophrenia as well as in suicide completers associated tical layer [12]. In general, most microarray studies with with these disorders [8,10]. We identified the up-regula- post-mortem brain tissues find fold changes of less than 2 Page 5 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 Di at level 4 non-suicide in schizophrenia Figure 2 stributiof on of differ gene onto entially expr logy gro coh essed oups fo rts genes betw r biological een sui proce cide svs. s Distribution of gene ontology groups for biological process at level 4 of differentially expressed genes between suicide vs. non- suicide in schizophrenia cohorts. should be considered as provisional until confirmed by replications in independent sets of biological samples. Conclusion By reanalyzing a large microarray dataset, a list of differen- tially expressed candidate genes for suicide within bipolar disorder or within schizophrenia have been identified. The overlap of genes in common among these two gene lists is small, with a larger number of disorder specific genes being found. This finding suggests that disorder- D Figure 1 ifferentially expressed genes between suicide completer vs specific pathways predominate over common pathways at Differentially expressed genes between suicide completer the molecular level. Two novel candidate genes, PLSCR4 vs. non-suicide group in bipolar disorder and schizophre- nia cohorts. (A) Venn diagram of differentially expressed genes and EMX2, were confirmed as differentially expressed in between suicide completer vs. non-suicide groups within bipolar schizophrenia between suicide completers vs. non-suicide and schizophrenia. (B) Mean expression levels of PLSCR4 (phos- groups. pholipid scramblase 4) and EMX2 (empty spiracles homolog 2 (Drosophila)) mRNA transcripts were determined for suicide completers vs. non-suicide groups within both bipolar disorder Methods and schizophrenia in prefrontal cortex by qRT-PCR. The bars rep- Microarray data and Patient Samples resent mean ± SEM. In schizophrenia, the mean expression levels The brain tissues were meticulously collected in a stand- of both genes were significantly down-regulated in the suicide group (n = 5) relative to the non-suicide cases (n = 25) by one- ardized manner via pathologists in the offices of the Med- tailed, t-tests for unequal variances (EMX2 t(9) = 2.42, p = 0.02; ical Examiner in several states with the families' PLSCR4 t(18) = 3.77, p = 0.0005). The estimated fold changes in permission under the aegis of the Stanley Foundation the suicide group were -1.51 for EMX2 and -2.16 for PLSCR4 rela- tive to the non-suicide group, consistent with our microarray Brain Collection (Array Collection plus Consortium Col- data. In the bipolar disorder samples, no significant differences in lection) [26]. The selection of specimens, clinical infor- mean expression levels for either gene were found between the mation, diagnoses of patients, and processing of tissues suicide (n = 14 for EMX2; n = 12 for PLSCR4) vs. non-suicide cases (n = 15 for EMX2 ; n = 11 for PLSCR4) by unequal variance t-tests. were conducted by Stanley Foundation Consortium as * p < .05 ; *** p < .001. described previously [26]. Gene expression profiling uti- lized post-mortem prefrontal cortex (Brodmann's Area 46/10) mRNA and Affymetrix Human Genome U133 Set fold, including this study. Second, although smoking, A (HGU133A) using standardized techniques as alcohol, and drug abuse were measured as confounding described [26,27]. The prefrontal cortex was selected as factors, all possible unmeasured, confounding variables the region of interest due to its role in executive function- for suicide cannot be formally excluded, such as severity ing, impulsivity ("lack of premeditation"), and decision of illness (especially the last episode for suicide compl- making. Disadvantageous decision making and impulsiv- eters), personality traits, hopelessness, agitation, depres- ity have been found to increase the risk of suicide [28,29]. sive symptoms, and stress. Third, these findings are correlational and not causal. Fourth, these gene lists Page 6 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 Ingenuity pathway ni Figure 3 a (B) analysis of EMX2 and differentially expressed suicide candidate genes in bipolar disorder (A) and schizophre- Ingenuity pathway analysis of EMX2 and differentially expressed suicide candidate genes in bipolar disorder (A) and schizophrenia (B). The Stanley Foundation's microarray database is an anon- repeated microarray data from each patient were treated ymous, de-identified dataset without any protected health as technical replicates. information. Patients' demographic variables used in this Statistical analysis of microarray data study are listed in Table 1. Microarray data was analyzed by a statistical method The robust multi-array averages (RMA)-normalized described previously with slight modifications [26]. microarray data from four independent studies were Briefly the following steps were followed within each downloaded from the SMRIDB. Microarray data from the diagnostic group (see Table 1). First, the differentially same platform, Affymetrix Human Genome U133 Set A expressed genes between suicide completers vs. non-sui- (HGU133A), were used to avoid platform-to-platform cide groups were filtered by average fold change (FC ≥ variation. The platform contains 22,215 probe sets. Qual- |1.3|) using the BRB-array tool[30] without covariates. ity control analyses for each chip were described previ- Second, the influence of continuous demographic varia- ously [15] bles (such as age, post-mortem interval (PMI) and brain pH) with the nominal variable suicide was tested using For the bipolar disorder cohort, the total dataset consisted ANOVA. Then, categorical variables such as sex, smoking, of 49 suicide completers' gene chips and 58 non-suicide alcohol and drug abuse were tested using chi square tests gene chips, while for schizophrenia cohort, the total data- of association (Statview software SAS, Cary, NC). In addi- set consisted of 22 suicide completer gene chips and 89 tion, correlation analyses of the demographic factors with non-suicide chips. Among 45 bipolar samples, there were expression levels of the differentially expressed probe sets 22 suicide cases, and 23 non-suicide cases. Among 45 from step 1 were performed. Continuous variables were schizophrenia patients, there were 10 suicide cases, and analyzed by Spearman's rank correlation and categorical 35 non-suicide cases. Two to three microarray chip data- variables were tested by ANOVA. P-values were adjusted sets were generated from the each patient's sample. These by False Discovery Rate (FDR) in both tests [31]. Third, significant confounding factors were tested as possible Page 7 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 covariates for ANCOVA model inclusion with the follow- cohort, there were 5 suicides and 27 non-suicide cases. ing criteria: The variable was required to show both 1) sig- These samples were matched by age, race, gender, PMI, nificant association with suicide as well as 2) significant brain pH, side of the brain and quality of RNA. Reactions correlation with expression levels of the differentially were quantified by the comparative Ct method using expressed genes. However, no variables met the criteria in SDS2.2 software (ABI). This RT-PCR data was also statisti- both disorder groups. Therefore, no covariates were used cally analyzed by the amplification plot method using the in the omnibus ANOVA, using the factor as suicide vs. Data Analysis for Real Time PCR (DART-PCR) approach non-suicide. As an exploratory analysis, a more liberal [34]. This method identified outliers in amplification effi- FDR P-value (< 0.1) of significance was selected for ciency by ANOVA and calculated mean expression levels. expression level differences as previously described Statistical differences in expression levels between groups, [32,33], using the BRB array software tool's FDR default namely suicide completers vs. non-suicides within a diag- setting. For negative controls, we performed statistical nostic group, were tested by one-tail, t-test with unequal analysis with the HGU133A normalization control probe variances (Microsoft Excel) as described [35,36]. To esti- sets using the same ANCOVA models, ensuring the adjust- mate average fold changes between groups, the mean ment did not produce "noise" or aberrant false positives. expression values from the DART-PCR approach were The Microarray Suite, version 5.0 (MAS5) software was used. The alternative 2(-Delta Delta Ct) method [37] for used to filter genes with low expression levels as either estimating fold change (using all the data without exclu- present or absent, applying the detection call statistical sions) verified this fold change estimate. As both methods algorithm. This algorithm suggests whether a gene is gave similar estimates, only the DART-PCR approach esti- present or absent. mates from mean expression levels were reported. A power analysis estimated the sample sizes for detection Functional annotation of a 1.3 fold change in a gene with a significance criterion The differentially expressed genes were functionally anno- of P-value = 0.001 and a power of 0.90 using a previously tated using the DAVID integrated database query tool[38] described method [13]. This analysis estimated a mini- and by the over-representational analysis method [39]. mum sample size of 27 cases per group for comparing sui- Functional annotations were based on biological process cide completers vs. non-suicide groups within bipolar of Gene Ontology (GO) Consortium[40] at level 4. P-val- disorder and 21 samples per group for comparing suicide ues less than 0.05 were considered significant. completers vs. non-suicide completers within schizophre- nia. Pathway Analysis Biologically relevant networks were drawn from the lists Real-time quantitative PCR of genes that were differentially expressed in bipolar dis- Total RNA from the dorsolateral prefrontal cortex (Brod- order and schizophrenia. This data was generated through mann area 46) of the Array Collection was used for this the use of Ingenuity Pathways Analysis (IPA) [41], a web- experiment. Complementary DNA was synthesized from delivered application that enables the visualization and DNA-free RNA with a random hexamer primer and Super- analysis of biologically relevant networks to discover, vis- script III First-Strand Synthesis System according to the ualize, and explore relevant networks. Expression data sets manufacturer's protocol (Invitrogen). Using a 384-well containing gene identifiers (Affymetrix probe set ID) and format with the Prism7900HT real-time detector (ABI), 2 their corresponding expression values as fold changes μl aliquots of (10×) QuantiTect Primer Assay (validated were uploaded as a tab-delimited text file. Each gene iden- primers to the specific gene of interest; Qiagen), 10 μl (2×) tifier was mapped to its corresponding gene object in the QuantiTect SYBR PCR Master mix (Qiagen), and 8 μl Ingenuity Pathways Knowledge Base. These genes, called cDNA were mixed together for 20 μl total reaction volume Focus Genes, were then used as the starting point for gen- and pipetted into single wells of the 384 PCR plate. erating biological networks. To start building networks, Amplification conditions were: (1) 1 cycle for 2 min at the application program queries the Ingenuity Pathways 50°C, (2) 1 cycle for 15 min at 95°C, and (3) 45 cycles for Knowledge Base for interactions between Focus Genes 15 s at 94°C, 30 s at 60°C and 30 s at 72°C and fluores- and all other gene objects stored in the knowledge base, cence was measured during the 72°C step for each cycle as and generates a set of networks. The program then com- recommended by the manufacturer. The β-2 microglobu- putes a score for each network according to the fit of the lin (B2M) was chosen as an endogenous control for the network to the set of focus genes. The score indicates the normalization of target genes as it was consistently likelihood of the Focus Genes in a given network being expressed in microarray samples. A total of 32 samples per found together due to random chance. A score of greater each disorder were used for this experiment and run in than 2 indicates that there is a less than 1 in 100 chance duplicate. In the bipolar disorder cohort, there were 14 that the Focus Genes were assembled randomly into a net- suicides and 18 non-suicide cases. In schizophrenia work due to random chance. The scores of the networks Page 8 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 SSAT by gene expression and genetic variation in suicide and generated from the lists of differentially expressed genes major depression. Arch Gen Psychiatry 2006, 63(1):35-48. were 24 for Bipolar disorder (Fig 3A) and 40 for Schizo- 12. Rossner MJ, Hirrlinger J, Wichert SP, Boehm C, Newrzella D, Hie- phrenia (Fig 3B). misch H, Eisenhardt G, Stuenkel C, von Ahsen O, Nave KA: Global transcriptome analysis of genetically identified neurons in the adult cortex. J Neurosci 2006, 26(39):9956-9966. Authors' contributions 13. Wei C, Li J, Bumgarner RE: Sample size for detecting differen- tially expressed genes in microarray experiments. BMC SK performed the analysis of the microarray data. HG Genomics 2004, 5(1):87. supervised the study. SK and HG wrote the manuscript. 14. SMRIDB: [https://www.stanleygenomics.org]. KC performed qRT-PCR validation experiment and SK 15. Higgs BW, Elashoff M, Richman S, Barci B: An online database for brain disease research. BMC Genomics 2006, 7:70. performed analysis of the data. AFB provided important 16. Ikeda M, Kihara A, Igarashi Y: Lipid asymmetry of the eukaryotic discussions. All authors read and approved of the final plasma membrane: functions and related enzymes. Biol Pharm version of manuscript. Bull 2006, 29(8):1542-1546. 17. Wiedmer T, Zhou Q, Kwoh DY, Sims PJ: Identification of three new members of the phospholipid scramblase gene family. Acknowledgements Biochim Biophys Acta 2000, 1467(1):244-253. This work is generously supported by NIH (RO1 MH067211) and NAR- 18. Alves ID, Salgado GF, Salamon Z, Brown MF, Tollin G, Hruby VJ: Phosphatidylethanolamine enhances rhodopsin photoactiva- SAD (HG). The post-mortem brain collection was developed over many tion and transducin binding in a solid supported lipid bilayer years through the generous permission of the families and the long-term as determined using plasmon-waveguide resonance spec- support of the Stanley Foundation Brain Collection by the Stanley Medical troscopy. Biophys J 2005, 88(1):198-210. Research Institute. We thank the Stanley Foundation Brain Collection and 19. Niu SL, Mitchell DC, Litman BJ: Optimization of receptor-G pro- tein coupling by bilayer lipid composition II: formation of its Neuropathology Consortium for making its microarray database publicly metarhodopsin II-transducin complex. J Biol Chem 2001, available and their foresight in generating this dataset through the collabo- 276(46):42807-42811. rative spirit and diligence of many investigators. In particular, we acknowl- 20. Schmidt D, Jiang QX, MacKinnon R: Phospholipids and the origin edge The Stanley Medical Research Institute's Investigator's, Drs. E. Fuller of cationic gating charges in voltage sensors. Nature 2006, 444(7120):775-779. Torrey, Robert H. Yolken, Michael B. Knable, Michael Elashoff, and Maree 21. Hamasaki T, Leingartner A, Ringstedt T, O'Leary DD: EMX2 regu- J. Webster along with their many collaborators, who made this work pos- lates sizes and positioning of the primary sensory and motor sible. We acknowledge Drs. C. Claasen and A. Kumar for helpful advice. areas in neocortex by direct specification of cortical progen- itors. Neuron 2004, 43(3):359-372. 22. Mann JJ: Neurobiology of suicidal behaviour. Nat Rev Neurosci References 2003, 4(10):819-828. 1. McKeown RE, Cuffe SP, Schulz RM: US suicide rates by age 23. Sibille E, Arango V, Galfalvy HC, Pavlidis P, Erraji-Benchekroun L, Ellis group, 1970-2002: an examination of recent trends. Am J Pub- SP, John Mann J: Gene expression profiling of depression and lic Health 2006, 96(10):1744-1751. suicide in human prefrontal cortex. Neuropsychopharmacology 2. Chen YW, Dilsaver SC: Lifetime rates of suicide attempts 2004, 29(2):351-361. among subjects with bipolar and unipolar disorders relative 24. Willour VL, Zandi PP, Badner JA, Steele J, Miao K, Lopez V, Mackin- to subjects with other Axis I disorders. Biol Psychiatry 1996, non DF, Mondimore FM, Schweizer B, McInnis MG, Miller EB, Dep- 39(10):896-899. aulo JR Jr., Gershon ES, McMahon FJ, Potash JB: Attempted Suicide 3. Oquendo MA, Currier D, Mann JJ: Prospective studies of suicidal in Bipolar Disorder Pedigrees: Evidence for Linkage to 2p12. behavior in major depressive and bipolar disorders: what is Biol Psychiatry 2007, 61(5):725-727. the evidence for predictive risk factors? Acta Psychiatr Scand 25. Cheng R, Juo SH, Loth JE, Nee J, Iossifov I, Blumenthal R, Sharpe L, 2006, 114(3):151-158. Kanyas K, Lerer B, Lilliston B, Smith M, Trautman K, Gilliam TC, Endi- 4. Hawton K, Sutton L, Haw C, Sinclair J, Deeks JJ: Schizophrenia and cott J, Baron M: Genome-wide linkage scan in a large bipolar suicide: systematic review of risk factors. Br J Psychiatry 2005, disorder sample from the National Institute of Mental 187:9-20. Health genetics initiative suggests putative loci for bipolar 5. Hawton K, Sutton L, Haw C, Sinclair J, Harriss L: Suicide and disorder, psychosis, suicide, and panic disorder. Mol Psychiatry attempted suicide in bipolar disorder: a systematic review of 2006, 11(3):252-260. risk factors. J Clin Psychiatry 2005, 66(6):693-704. 26. Prabakaran S, Swatton JE, Ryan MM, Huffaker SJ, Huang JT, Griffin JL, 6. Maris RW, Berman AL, Maltsberger JT: Summary and conclu- Wayland M, Freeman T, Dudbridge F, Lilley KS, Karp NA, Hester S, sions: What have we learned about suicide assessment and Tkachev D, Mimmack ML, Yolken RH, Webster MJ, Torrey EF, Bahn prediction? In Assessment and prediction of suicide Edited by: Maris S: Mitochondrial dysfunction in schizophrenia: evidence for RW, Berman AL, Maltsberger JT, Yufit RI. New York , Guilford Press; compromised brain metabolism and oxidative stress. Mol 1992:640–672. Psychiatry 2004, 9(7):684-97, 643. 7. Bunney WE, Bunney BG, Vawter MP, Tomita H, Li J, Evans SJ, Chou- 27. Iwamoto K, Kakiuchi C, Bundo M, Ikeda K, Kato T: Molecular char- dary PV, Myers RM, Jones EG, Watson SJ, Akil H: Microarray tech- acterization of bipolar disorder by comparing gene expres- nology: a review of new strategies to discover candidate sion profiles of postmortem brains of major mental vulnerability genes in psychiatric disorders. Am J Psychiatry disorders. Mol Psychiatry 2004, 9(4):406-416. 2003, 160(4):657-666. 28. Raust A, Slama F, Mathieu F, Roy I, Chenu A, Koncke D, Fouques D, 8. Mirnics K, Levitt P, Lewis DA: Critical appraisal of DNA micro- Jollant F, Jouvent E, Courtet P, Leboyer M, Bellivier F: Prefrontal arrays in psychiatric genomics. Biol Psychiatry 2006, cortex dysfunction in patients with suicidal behavior. Psychol 60(2):163-176. Med 2007, 37(3):411-419. 9. Gwadry FG, Sequeira A, Hoke G, Ffrench-Mullen JM, Turecki G: 29. Zermatten A, Van der Linden M, d'Acremont M, Jermann F, Bechara Molecular characterization of suicide by microarray analysis. A: Impulsivity and decision making. J Nerv Ment Dis 2005, Am J Med Genet C Semin Med Genet 2005, 133(1):48-56. 193(10):647-650. 10. Choudary PV, Molnar M, Evans SJ, Tomita H, Li JZ, Vawter MP, Myers 30. BRB array tool: [http://linus.nci.nih.gov/BRB-ArrayTools.html]. RM, Bunney WE Jr., Akil H, Watson SJ, Jones EG: Altered cortical 31. Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: glutamatergic and GABAergic signal transmission with glial a practical and powerful approach to multiple testing. J Royal involvement in depression. Proc Natl Acad Sci U S A 2005, Stat Soc Ser B 1995, 57(1):289-300. 102(43):15653-15658. 32. Hamatani T, Daikoku T, Wang H, Matsumoto H, Carter MG, Ko MS, 11. Sequeira A, Gwadry FG, Ffrench-Mullen JM, Canetti L, Gingras Y, Dey SK: Global gene expression analysis identifies molecular Casero RA Jr., Rouleau G, Benkelfat C, Turecki G: Implication of Page 9 of 10 (page number not for citation purposes) BMC Genomics 2007, 8:413 http://www.biomedcentral.com/1471-2164/8/413 pathways distinguishing blastocyst dormancy and activation. Proc Natl Acad Sci U S A 2004, 101(28):10326-10331. 33. Raponi M, Belly RT, Karp JE, Lancet JE, Atkins D, Wang Y: Microar- ray analysis reveals genetic pathways modulated by tipi- farnib in acute myeloid leukemia. BMC Cancer 2004, 4:56. 34. Peirson SN, Butler JN, Foster RG: Experimental validation of novel and conventional approaches to quantitative real-time PCR data analysis. Nucleic Acids Res 2003, 31(14):e73. 35. Yuan JS, Reed A, Chen F, Stewart CN Jr.: Statistical analysis of real-time PCR data. BMC Bioinformatics 2006, 7:85. 36. Ruxton GD: The unequal variance t-test is an underused alter- native to Student's t-test and the Mann-Whitney U test. Behav Ecol 2006, 17(4):688-690. 37. Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001, 25(4):402-408. 38. DAVID integrated database query tool: [http://david.abcc.ncifcrf.gov/ ]. 39. Dennis G Jr., Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 2003, 4(5):P3. 40. Gene Ontology Consortium: [http://www.geneontology.org]. 41. Ingenuity Pathways Analysis: [http://www.Ingenuity.com]. Publish with Bio Med Central and every scientist can read your work free of charge "BioMed Central will be the most significant development for disseminating the results of biomedical researc h in our lifetime." Sir Paul Nurse, Cancer Research UK Your research papers will be: available free of charge to the entire biomedical community peer reviewed and published immediately upon acceptance cited in PubMed and archived on PubMed Central yours — you keep the copyright BioMedcentral Submit your manuscript here: http://www.biomedcentral.com/info/publishing_adv.asp Page 10 of 10 (page number not for citation purposes)

Journal

BMC GenomicsSpringer Journals

Published: Nov 12, 2007

There are no references for this article.