Convergent Functional Genomics of bipolar disorder: from animal model pharmacogenomics to human genetics and biomarkers

Interactome Analyses implicated CAMK2A in the genetic predisposition and pharmacological mechanism of Bipolar Disorder

Bipolar disorder (BPD) is a severe mental illness characterized by fluctuations in mood states, behaviors and energy levels. Growing evidence suggests that genes associated with specific illnesses tend to interact together and encode a tight protein-protein interaction (PPI) network, providing valuable information for understanding their pathogenesis. To gain insights into the genetic and physiological foundation of BPD, we conduct the physical PPI analysis of 184 BPD risk genes distilled from genome-wide association studies and exome sequencing studies. We have identified several hub genes (CAMK2A, HSP90AA1 and PLCG1) among those risk genes, and observed significant enrichment of the BPD risk genes in certain pathways such as calcium signaling, oxytocin signaling and circadian entrainment. Furthermore, while none of the 184 genetic risk genes are "well established" BPD drug targets, our PPI analysis showed that αCaMKII (encoded by CAMK2A) had direct physical PPIs with targets (HRH1, SCN5A and CACNA1E) of clinically used anti-manic BPD drugs, such as carbamazepine. We thus speculated that αCaMKII might be involved in the cellular pharmacological actions of those drugs. Using cultured rat primary cortical neurons, we found that carbamazepine treatment induced phosphorylation of αCaMKII in dose-dependent manners. Intriguingly, previous study showed that CAMK2A heterozygous knockout (CAMK2A^+/-) mice exhibited infradian oscillation of locomotor activities that can be rescued by carbamazepine. Our data, in combination with previous studies, provide convergent evidence for the involvement of CAMK2A in the risk of BPD.

The Gene Encoding Protocadherin 9 (PCDH9), a Novel Risk Factor for Major Depressive Disorder

Genomic analyses have identified only a handful of robust risk loci for major depressive disorder (MDD). In addition to the published genome-wide significant genes, it is believed that there are undiscovered 'treasures' underlying the current MDD genome-wide association studies (GWASs) and gene expression data sets, and digging into these data will allow better understanding of the illness and development of new therapeutic approaches. For this purpose, we performed a meta-analytic study combining three MDD GWAS data sets (23andMe, CONVERGE, and PGC), and then conducted independent replications of significant loci in two additional samples. The genome-wide significant variants then underwent explorative analyses on MDD-related phenotypes, cognitive function alterations, and gene expression in brains. In the discovery meta-analysis, a previously unidentified single-nucleotide polymorphism (SNP) rs9540720 in the PCDH9 gene was genome-wide significantly associated with MDD (p=1.69 × 10^-8 in a total of 89 610 cases and 246 603 controls), and the association was further strengthened when additional replication samples were included (p=1.20 × 10^-8 in a total of 136 115 cases and 355 275 controls). The risk SNP was also associated with multiple MDD-related phenotypes and cognitive function impairment in diverse samples. Intriguingly, the risk allele of rs9540720 predicted lower PCDH9 expression, consistent with the diagnostic analysis results that PCDH9 mRNA expression levels in the brain and peripheral blood tissues were reduced in MDD patients compared with healthy controls. These convergent lines of evidence suggest that PCDH9 is likely a novel risk gene for MDD. Our study highlights the necessity and importance of excavating the public data sets to explore risk genes for MDD, and this approach is also applicable to other complex diseases.

Neuro-psychopharmacological perspective of Orphan receptors of Rhodopsin (class A) family of G protein-coupled receptors

BACKGROUND

In the central nervous system (CNS), G protein-coupled receptors (GPCRs) are the most fruitful targets for neuropsychopharmacological drug development. Rhodopsin (class A) is the most studied class of GPCR and includes orphan receptors for which the endogenous ligand is not known or is unclear. Characterization of orphan GPCRs has proven to be challenging, and the production pace of GPCR-based drugs has been incredibly slow.

OBJECTIVE

Determination of the functions of these receptors may provide unexpected insight into physiological and neuropathological processes. Advances in various methods and techniques to investigate orphan receptors including in situ hybridization and knockdown/knockout (KD/KO) showed extensive expression of these receptors in the mammalian brain and unmasked their physiological and neuropathological roles. Due to these rapid progress and development, orphan GPCRs are rising as a new and promising class of drug targets for neurodegenerative diseases and psychiatric disorders.

CONCLUSION

This review presents a neuropsychopharmacological perspective of 26 orphan receptors of rhodopsin (class A) family, namely GPR3, GPR6, GPR12, GPR17, GPR26, GPR35, GPR39, GPR48, GPR49, GPR50, GPR52, GPR55, GPR61, GPR62, GPR63, GPR68, GPR75, GPR78, GPR83, GPR84, GPR85, GPR88, GPR153, GPR162, GPR171, and TAAR6. We discussed the expression of these receptors in mammalian brain and their physiological roles. Furthermore, we have briefly highlighted their roles in neurodegenerative diseases and psychiatric disorders including Alzheimer's disease, Parkinson's disease, neuroinflammation, inflammatory pain, bipolar and schizophrenic disorders, epilepsy, anxiety, and depression.

Discovery of serum protein biomarkers in drug-free patients with major depressive disorder

OBJECTIVE

Major depressive disorder (MDD) is a systemic and multifactorial disorder involving complex interactions between genetic predisposition and disturbances of various molecular pathways. Its underlying molecular pathophysiology remains unclear, and no valid and objective diagnostic tools for the condition are available.

METHODS

We performed large-scale proteomic profiling to identify novel peripheral biomarkers implicated in the pathophysiology of MDD in 25 drug-free female MDD patients and 25 healthy controls. First, quantitative serum proteome profiles were obtained and analyzed by liquid chromatography-tandem mass spectrometry using serum samples from 10 MDD patients and 10 healthy controls. Next, candidate biomarker sets, including differentially expressed proteins from the profiling experiment and those identified in the literature, were verified using multiple-reaction monitoring in 25 patients and 25 healthy controls. The final panel of potential biomarkers was selected using multiparametric statistical analysis.

RESULTS

We identified a serum biomarker panel consisting of six proteins: apolipoprotein D, apolipoprotein B, vitamin D-binding protein, ceruloplasmin, hornerin, and profilin 1, which could be used to distinguish MDD patients from controls with 68% diagnostic accuracy. Our results suggest that modulation of the immune and inflammatory systems and lipid metabolism are involved in the pathophysiology of MDD.

CONCLUSIONS

Our findings of functional proteomic changes in the peripheral blood of patients with MDD further clarify the molecular biological pathway underlying depression. Further studies using larger, independent cohorts are needed to verify the role of these candidate biomarkers for the diagnosis of MDD.

An integrative in silico system for predicting dysregulated genes in the human epileptic focus: Application to SLC transporters

OBJECTIVE

Many different gene families are currently being investigated for their potential role in epilepsy and in the response to antiepileptic drugs. A common research challenge is identifying the members of a gene family that are most significantly dysregulated within the human epileptic focus, before taking them forward for resource-intensive functional studies. Published data about transcriptomic changes within the human epileptic focus remains incomplete. A need exists for an accurate in silico system for the prediction of dysregulated genes within the epileptic focus. We present such a bioinformatic system. We demonstrate the validity of our approach by applying it to the solute carrier (SLC) gene family. There are >400 known SLCs. SLCs have never been systematically studied in epilepsy.

METHODS

Using our in silico system, we predicted the SLCs likely to be dysregulated in the epileptic focus. We validated our in silico predictions by identifying ex vivo the SLCs dysregulated in epileptic foci, and determining the overlap between our in silico and ex vivo results. For the ex vivo analysis, we used a custom oligonucleotide microarray containing exon probes for all known SLCs to analyze 24 hippocampal samples obtained from surgery for pharmacoresistant mesial temporal lobe epilepsy and 24 hippocampal samples from normal postmortem controls.

RESULTS

There was a highly significant (p < 9.99 × 10(-7) ) overlap between the genes identified by our in silico and ex vivo strategies. The SLCs identified were either metal ion exchangers or neurotransmitter transporters, which are likely to play a part in epilepsy by influencing neuronal excitability.

SIGNIFICANCE

The identified SLCs are most likely to mediate pharmacoresistance in epilepsy by enhancing the intrinsic severity of epilepsy, but further functional work will be needed to fully evaluate their role. Our successful in silico strategy can be adapted in order to prioritize genes relevant to epilepsy from other gene families.

Stress-Related Immune Markers in Depression: Implications for Treatment

Major depression is a serious psychiatric disorder; however, the precise biological basis of depression still remains elusive. A large body of evidence implicates a dysregulated endocrine and inflammatory response system in the pathogenesis of depression. Despite this, given the heterogeneity of depression, not all depressed patients exhibit dysregulation of the inflammatory and endocrine systems. Evidence suggests that inflammation is associated with depression in certain subgroups of patients and that those who have experienced stressful life events such as childhood trauma or bereavement may be at greater risk of developing depression. Consequently, prolonged exposure to stress is thought to be a key trigger for the onset of a depressive episode. This review assesses the relationship between stress and the immune system, with a particular interest in the mechanisms by which stress impacts immune function, and how altered immune functioning, in turn, may lead to a feed forward cascade of multiple systems dysregulation and the subsequent manifestation of depressive symptomology. The identification of stress-related immune markers and potential avenues for advances in therapeutic intervention is vital. Changes in specific biological markers may be used to characterize or differentiate depressive subtypes or specific symptoms and may predict treatment response, in turn facilitating a more effective, targeted, and fast-acting approach to treatment.

Omics-Based Biomarkers: Application of Metabolomics in Neuropsychiatric Disorders

One of the major concerns of modern society is to identify putative biomarkers that serve as a valuable early diagnostic tool to identify a subset of patients with increased risk to develop neuropsychiatric disorders. Biomarker identification in neuropsychiatric disorders is proposed to offer a number of important benefits to patient well-being, including prediction of forthcoming disease, diagnostic precision, and a level of disease description that would guide treatment choice. Nowadays, the metabolomics approach has unlocked new possibilities in diagnostics of devastating disorders like neuropsychiatric disorders. Metabolomics-based technologies have the potential to map early biochemical changes in disease and hence provide an opportunity to develop predictive biomarkers that can be used as indicators of pathological abnormalities prior to development of clinical symptoms of neuropsychiatric disorders. This review highlights different -omics strategies for biomarker discovery in neuropsychiatric disorders. We also highlight initial outcomes from metabolomics studies in psychiatric disorders such as schizophrenia, bipolar disorder, and addictive disorders. This review will also present issues and challenges regarding the implementation of the metabolomics approach as a routine diagnostic tool in the clinical laboratory in context with neuropsychiatric disorders.

Taking the fuel out of the fire: evidence for the use of anti-inflammatory agents in the treatment of bipolar disorders

BACKGROUND

Inflammation has emerged as a potentially important factor - and thus putative pharmacological target - in the pathology of bipolar disorders. However to date no systematic evaluations of the efficacy of add on anti-inflammatory treatment for the depressive and manic episodes have been carried out.

METHODS

Sixteen articles were ultimately identified - by computer searches of databases (including PsycINFO, MEDLINE, and EMBASE), supplemented by hand searches and personal communication - as meeting study inclusion criteria.

RESULTS

Anti-manic effects were evaluated in two trials, one of adjunctive n-acetyl cysteine (NAC), one of omega-3 fatty acids (O3FA), and significant improvements only emerged for NAC. Celecoxib had a rapid but short-lived antidepressant effect. Despite limited effects of O3FA on symptoms, imaging data demonstrated alterations in neuronal functioning that might have longer-term therapeutic effects. Evidence was strongest for adjunctive NAC in bipolar depression though conclusions are limited by small sample sizes.

LIMITATIONS

Definitive conclusions are limited by the paucity of data, small study sizes, and the variability in methodology used.

CONCLUSIONS

Current evidence for aspirin or celecoxib is insufficient though further investigation of the potential of celecoxib in early illness onset is warranted. Variable evidence exists for add-on O3FA though an indication of short-term treatment effects on membrane fluidity and neuronal activity suggest longer follow-up assessment is needed. The strongest evidence emerged for NAC in depression and future studies must address the role of illness duration and patients׳ baseline medications on outcomes. Careful consideration of lithium toxicity in the elderly and renal impaired is essential.

Blood biomarkers of depression track clinical changes during cognitive-behavioral therapy

BACKGROUND

Results from convergent genomics indicated new peripheral biomarkers for mood states. We sought to investigate the clinical utility of the BioM-10 Mood Panel, a peripheral biomarker set of low vs. high mood states, in the diagnosis of major depressive episode and to monitor the effectiveness of cognitive-behavioral therapy (CBT).

METHOD

44 patients with a first episode of major depression and 30 healthy control subjects participated in the study. The BioM-10 panel׳s gene expression profile was measured from whole peripheral blood with the Affymetrix Human Genome U133 Plus 2.0 Gene Chips, focusing on 10 top genes related to high mood states (MBP, EDG2, FZD3, ATXN1, and EDNRB) and low mood states (FGFR1, MAG, PMP22, UGT8, and ERBB3). We studied gene expression before and after CBT.

RESULTS

The BioM-10 prediction score discriminated patients and controls with high sensitivity (84%) and specificity (90%). There was an increase in the BioM-10 prediction score after CBT relative to the pretreatment value. Clinical improvement was associated with higher prediction scores reflecting a greater ratio of high mood markers relative to low mood markers.

LIMITATIONS

Sample size was small for a genome-wide microarray study. Convergent genomic studies have not been conducted in major depressive disorder. More evidence is needed from patients with severe, recurrent, and chronic forms of depression.

CONCLUSIONS

The BioM-10 panel is a promising tool as a biomarker setup for the evaluation of low and high mood states across diagnostic categories. The panel includes genes related to growth factor pathways and myelination, which may provide new insights into the pathophysiology of mood dysregulation.

Identification of a QTL in Mus musculus for alcohol preference, withdrawal, and Ap3m2 expression using integrative functional genomics and precision genetics

Extensive genetic and genomic studies of the relationship between alcohol drinking preference and withdrawal severity have been performed using animal models. Data from multiple such publications and public data resources have been incorporated in the GeneWeaver database with >60,000 gene sets including 285 alcohol withdrawal and preference-related gene sets. Among these are evidence for positional candidates regulating these behaviors in overlapping quantitative trait loci (QTL) mapped in distinct mouse populations. Combinatorial integration of functional genomics experimental results revealed a single QTL positional candidate gene in one of the loci common to both preference and withdrawal. Functional validation studies in Ap3m2 knockout mice confirmed these relationships. Genetic validation involves confirming the existence of segregating polymorphisms that could account for the phenotypic effect. By exploiting recent advances in mouse genotyping, sequence, epigenetics, and phylogeny resources, we confirmed that Ap3m2 resides in an appropriately segregating genomic region. We have demonstrated genetic and alcohol-induced regulation of Ap3m2 expression. Although sequence analysis revealed no polymorphisms in the Ap3m2-coding region that could account for all phenotypic differences, there are several upstream SNPs that could. We have identified one of these to be an H3K4me3 site that exhibits strain differences in methylation. Thus, by making cross-species functional genomics readily computable we identified a common QTL candidate for two related bio-behavioral processes via functional evidence and demonstrate sufficiency of the genetic locus as a source of variation underlying two traits.

Discovery and validation of blood biomarkers for suicidality

Suicides are a leading cause of death in psychiatric patients, and in society at large. Developing more quantitative and objective ways (biomarkers) for predicting and tracking suicidal states would have immediate practical applications and positive societal implications. We undertook such an endeavor. First, building on our previous blood biomarker work in mood disorders and psychosis, we decided to identify blood gene expression biomarkers for suicidality, looking at differential expression of genes in the blood of subjects with a major mood disorder (bipolar disorder), a high-risk population prone to suicidality. We compared no suicidal ideation (SI) states and high SI states using a powerful intrasubject design, as well as an intersubject case-case design, to generate a list of differentially expressed genes. Second, we used a comprehensive Convergent Functional Genomics (CFG) approach to identify and prioritize from the list of differentially expressed gene biomarkers of relevance to suicidality. CFG integrates multiple independent lines of evidence-genetic and functional genomic data-as a Bayesian strategy for identifying and prioritizing findings, reducing the false-positives and false-negatives inherent in each individual approach. Third, we examined whether expression levels of the blood biomarkers identified by us in the live bipolar subject cohort are actually altered in the blood in an age-matched cohort of suicide completers collected from the coroner's office, and report that 13 out of the 41 top CFG scoring biomarkers (32%) show step-wise significant change from no SI to high SI states, and then to the suicide completers group. Six out of them (15%) remained significant after strict Bonferroni correction for multiple comparisons. Fourth, we show that the blood levels of SAT1 (spermidine/spermine N1-acetyltransferase 1), the top biomarker identified by us, at the time of testing for this study, differentiated future as well as past hospitalizations with suicidality, in a live cohort of bipolar disorder subjects, and exhibited a similar but weaker pattern in a live cohort of psychosis (schizophrenia/schizoaffective disorder) subjects. Three other (phosphatase and tensin homolog (PTEN), myristoylated alanine-rich protein kinase C substrate (MARCKS), and mitogen-activated protein kinase kinase kinase 3 (MAP3K3)) of the six biomarkers that survived Bonferroni correction showed similar but weaker effects. Taken together, the prospective and retrospective hospitalization data suggests SAT1, PTEN, MARCKS and MAP3K3 might be not only state biomarkers but trait biomarkers as well. Fifth, we show how a multi-dimensional approach using SAT1 blood expression levels and two simple visual-analog scales for anxiety and mood enhances predictions of future hospitalizations for suicidality in the bipolar cohort (receiver-operating characteristic curve with area under the curve of 0.813). Of note, this simple approach does not directly ask about SI, which some individuals may deny or choose not to share with clinicians. Lastly, we conducted bioinformatic analyses to identify biological pathways, mechanisms and medication targets. Overall, suicidality may be underlined, at least in part, by biological mechanisms related to stress, inflammation and apoptosis.

Convergent functional genomics of stem cell-derived cells

Stem cell technologies provide an exciting avenue to directly access the transcriptome of patients in neuronal-like cell types, which might have more direct relevance to brain research than other peripheral tissues (blood, fibroblasts). Enthusiasm should be tempered by concerns that artifacts and noise might be generated as part of the in vitro process of creating and maintaining these cell type. A solution may be to apply a Convergent Functional Genomics approach, where the data from stem cell-derived neuronal cells are integrated, cross-validated and prioritized using independent lines of evidence from other approaches and platforms (human genetic data, human postmortem brain data, animal model data). I provide a brief overview and an example in support of such an approach.

Relationship between frontostriatal morphology and executive function deficits in bipolar I disorder following a first manic episode: data from the Systematic Treatment Optimization Program for Early Mania (STOP-EM)

OBJECTIVES

Executive function impairments are a core feature of bipolar I disorder (BD-I), not only present during acute episodes but also persisting following remission of mood symptoms. Despite advances in knowledge regarding the neural basis of executive functions in healthy subjects, research into morphological abnormalities underlying the deficits in BD-I is lacking.

METHODS

Patients with BD-I within three months of sustained remission from their first manic episode (n = 41) underwent neuropsychological testing and a 3T magnetic resonance imaging scan and were compared to healthy subjects matched for age, sex, and premorbid IQ (n = 30). Group dorsolateral prefrontal cortex (DLPFC; Brodmann areas 9 and 46) and caudate volumes were examined and analyzed for relationships with the average score from three computerized tests of executive function: Spatial Working Memory, Stockings of Cambridge, and Intradimensional/Extradimensional Shift.

RESULTS

Right caudate volumes were enlarged in patients (z = 3.57, p < 0.05 corrected). No differences in DLPFC volumes were found. Patients showed large deficits in executive function relative to healthy subjects (d = -0.92, p < 0.001). While in healthy subjects, a larger right (r = +0.39, p < 0.05) and left (r = +0.44, p < 0.05) caudate was associated with better executive function score, in patients, larger right (r = -0.36, p < 0.05) and left (r = -0.34, p < 0.05) volumes correlated with poorer performance.

CONCLUSIONS

Although the etiology of gray matter changes is unknown, volume increases in the right caudate may be an important factor underlying executive function impairments during remission in patients with BD-I.

The future: biomarkers, biosensors, neuroinformatics, and e-neuropsychiatry

The emergence of molecular biomarkers for psychological, psychiatric, and neurodegenerative disorders is beginning to change current diagnostic paradigms for this debilitating family of mental illnesses. The development of new genomic, proteomic, and metabolomic tools has created the prospect of sensitive and specific biochemical tests to replace traditional pen-and-paper questionnaires. In the future, the realization of biosensor technologies, point-of-care testing, and the fusion of clinical biomarker data, electroencephalogram, and MRI data with the patient's past medical history, biopatterns, and prognosis may create personalized bioprofiles or fingerprints for brain disorders. Further, the application of mobile communications technology and grid computing to support data-, computation- and knowledge-based tasks will assist disease prediction, diagnosis, prognosis, and compliance monitoring. It is anticipated that, ultimately, mobile devices could become the next generation of personalized pharmacies.

Chemocentric informatics approach to drug discovery: identification and experimental validation of selective estrogen receptor modulators as ligands of 5-hydroxytryptamine-6 receptors and as potential cognition enhancers

We have devised a chemocentric informatics methodology for drug discovery integrating independent approaches to mining biomolecular databases. As a proof of concept, we have searched for novel putative cognition enhancers. First, we generated Quantitative Structure-Activity Relationship (QSAR) models of compounds binding to 5-hydroxytryptamine-6 receptor (5-HT(6)R), a known target for cognition enhancers, and employed these models for virtual screening to identify putative 5-HT(6)R actives. Second, we queried chemogenomics data from the Connectivity Map ( http://www.broad.mit.edu/cmap/ ) with the gene expression profile signatures of Alzheimer's disease patients to identify compounds putatively linked to the disease. Thirteen common hits were tested in 5-HT(6)R radioligand binding assays and ten were confirmed as actives. Four of them were known selective estrogen receptor modulators that were never reported as 5-HT(6)R ligands. Furthermore, nine of the confirmed actives were reported elsewhere to have memory-enhancing effects. The approaches discussed herein can be used broadly to identify novel drug-target-disease associations.

A new mouse model for mania shares genetic correlates with human bipolar disorder

Bipolar disorder (BPD) is a debilitating heritable psychiatric disorder. Contemporary rodent models for the manic pole of BPD have primarily utilized either single locus transgenics or treatment with psychostimulants. Our lab recently characterized a mouse strain termed Madison (MSN) that naturally displays a manic phenotype, exhibiting elevated locomotor activity, increased sexual behavior, and higher forced swimming relative to control strains. Lithium chloride and olanzapine treatments attenuate this phenotype. In this study, we replicated our locomotor activity experiment, showing that MSN mice display generationally-stable mania relative to their outbred ancestral strain, hsd:ICR (ICR). We then performed a gene expression microarray experiment to compare hippocampus of MSN and ICR mice. We found dysregulation of multiple transcripts whose human orthologs are associated with BPD and other psychiatric disorders including schizophrenia and ADHD, including: Epor, Smarca4, Cmklr1, Cat, Tac1, Npsr1, Fhit, and P2rx7. RT-qPCR confirmed dysregulation for all of seven transcripts tested. Using a novel genome enrichment algorithm, we found enrichment in genome regions homologous to human loci implicated in BPD in replicated linkage studies including homologs of human cytobands 1p36, 3p14, 3q29, 6p21–22, 12q24, 16q24, and 17q25. Using a functional network analysis, we found dysregulation of a gene system related to chromatin packaging, a result convergent with recent human findings on BPD. Our findings suggest that MSN mice represent a polygenic model for the manic pole of BPD showing much of the genetic systems complexity of the corresponding human disorder. Further, the high degree of convergence between our findings and the human literature on BPD brings up novel questions about evolution by analogy in mammalian genomes.

Using genome-wide expression profiling to define gene networks relevant to the study of complex traits: from RNA integrity to network topology

Postgenomic studies of the function of genes and their role in disease have now become an area of intense study since efforts to define the raw sequence material of the genome have largely been completed. The use of whole-genome approaches such as microarray expression profiling and, more recently, RNA-sequence analysis of transcript abundance has allowed an unprecedented look at the workings of the genome. However, the accurate derivation of such high-throughput data and their analysis in terms of biological function has been critical to truly leveraging the postgenomic revolution. This chapter will describe an approach that focuses on the use of gene networks to both organize and interpret genomic expression data. Such networks, derived from statistical analysis of large genomic datasets and the application of multiple bioinformatics data resources, potentially allow the identification of key control elements for networks associated with human disease, and thus may lead to derivation of novel therapeutic approaches. However, as discussed in this chapter, the leveraging of such networks cannot occur without a thorough understanding of the technical and statistical factors influencing the derivation of genomic expression data. Thus, while the catch phrase may be "it's the network … stupid," the understanding of factors extending from RNA isolation to genomic profiling technique, multivariate statistics, and bioinformatics are all critical to defining fully useful gene networks for study of complex biology.

Expression profiling in neuropsychiatric disorders: emphasis on glutamate receptors in bipolar disorder

Functional genomics and proteomics approaches are being employed to evaluate gene and encoded protein expression changes with the tacit goal to find novel targets for drug discovery. Genome-wide association studies (GWAS) have attempted to identify valid candidate genes through single nucleotide polymorphism (SNP) analysis. Furthermore, microarray analysis of gene expression in brain regions and discrete cell populations has enabled the simultaneous quantitative assessment of relevant genes. The ability to associate gene expression changes with neuropsychiatric disorders, including bipolar disorder (BP), and their response to therapeutic drugs provides a novel means for pharmacotherapeutic interventions. This review summarizes gene and pathway targets that have been identified in GWAS studies and expression profiling of human postmortem brain in BP, with an emphasis on glutamate receptors (GluRs). Although functional genomic assessment of BP is in its infancy, results to date point towards a dysregulation of GluRs that bear some similarity to schizophrenia (SZ), although the pattern is complex, and likely to be more complementary than overlapping. The importance of single population expression profiling of specific neurons and intrinsic circuits is emphasized, as this approach provides informative gene expression profile data that may be underappreciated in regional studies with admixed neuronal and non-neuronal cell types.

Convergent functional genomics of anxiety disorders: translational identification of genes, biomarkers, pathways and mechanisms

Anxiety disorders are prevalent and disabling yet understudied from a genetic standpoint, compared with other major psychiatric disorders such as bipolar disorder and schizophrenia. The fact that they are more common, diverse and perceived as embedded in normal life may explain this relative oversight. In addition, as for other psychiatric disorders, there are technical challenges related to the identification and validation of candidate genes and peripheral biomarkers. Human studies, particularly genetic ones, are susceptible to the issue of being underpowered, because of genetic heterogeneity, the effect of variable environmental exposure on gene expression, and difficulty of accrual of large, well phenotyped cohorts. Animal model gene expression studies, in a genetically homogeneous and experimentally tractable setting, can avoid artifacts and provide sensitivity of detection. Subsequent translational integration of the animal model datasets with human genetic and gene expression datasets can ensure cross-validatory power and specificity for illness. We have used a pharmacogenomic mouse model (involving treatments with an anxiogenic drug--yohimbine, and an anti-anxiety drug--diazepam) as a discovery engine for identification of anxiety candidate genes as well as potential blood biomarkers. Gene expression changes in key brain regions for anxiety (prefrontal cortex, amygdala and hippocampus) and blood were analyzed using a convergent functional genomics (CFG) approach, which integrates our new data with published human and animal model data, as a translational strategy of cross-matching and prioritizing findings. Our work identifies top candidate genes (such as FOS, GABBR1, NR4A2, DRD1, ADORA2A, QKI, RGS2, PTGDS, HSPA1B, DYNLL2, CCKBR and DBP), brain-blood biomarkers (such as FOS, QKI and HSPA1B), pathways (such as cAMP signaling) and mechanisms for anxiety disorders--notably signal transduction and reactivity to environment, with a prominent role for the hippocampus. Overall, this work complements our previous similar work (on bipolar mood disorders and schizophrenia) conducted over the last decade. It concludes our programmatic first pass mapping of the genomic landscape of the triad of major psychiatric disorder domains using CFG, and permitted us to uncover the significant genetic overlap between anxiety and these other major psychiatric disorders, notably the under-appreciated overlap with schizophrenia. PDE10A, TAC1 and other genes uncovered by our work provide a molecular basis for the frequently observed clinical co-morbidity and interdependence between anxiety and other major psychiatric disorders, and suggest schizo-anxiety as a possible new nosological domain.

Identification of blood biomarkers for psychosis using convergent functional genomics

There are to date no objective clinical laboratory blood tests for psychotic disease states. We provide proof of principle for a convergent functional genomics (CFG) approach to help identify and prioritize blood biomarkers for two key psychotic symptoms, one sensory (hallucinations) and one cognitive (delusions). We used gene expression profiling in whole blood samples from patients with schizophrenia and related disorders, with phenotypic information collected at the time of blood draw, then cross-matched the data with other human and animal model lines of evidence. Topping our list of candidate blood biomarkers for hallucinations, we have four genes decreased in expression in high hallucinations states (Fn1, Rhobtb3, Aldh1l1, Mpp3), and three genes increased in high hallucinations states (Arhgef9, Phlda1, S100a6). All of these genes have prior evidence of differential expression in schizophrenia patients. At the top of our list of candidate blood biomarkers for delusions, we have 15 genes decreased in expression in high delusions states (such as Drd2, Apoe, Scamp1, Fn1, Idh1, Aldh1l1), and 16 genes increased in high delusions states (such as Nrg1, Egr1, Pvalb, Dctn1, Nmt1, Tob2). Twenty-five of these genes have prior evidence of differential expression in schizophrenia patients. Predictive scores, based on panels of top candidate biomarkers, show good sensitivity and negative predictive value for detecting high psychosis states in the original cohort as well as in three additional cohorts. These results have implications for the development of objective laboratory tests to measure illness severity and response to treatment in devastating disorders such as schizophrenia.

Clinical variations modulate patterns of gene expression and define blood biomarkers in major depression

The aim of the study is to compare the expression level of candidate genes between patients suffering from a severe major depressive episode (MDE) and controls, and also among patients during MDE evolution. After a comprehensive review of the biological data related to mood disorders, we initiated a hypothesis-driven exploration of candidate mRNAs. Using RT-qPCR, we analyzed peripheral blood mononuclear cells (PBMCs) mRNA obtained from a homogeneous population of 11 patients who suffered from severe melancholic MDE. To assess the evolution of MDE, we analyzed PBMC mRNAs that were collected on Day 1 and 8 weeks later. Data from these patient samples were analyzed in comparison to age- and sex-matched healthy controls. Among 40 candidate genes consistently transcribed in PBMCs, 10 were differentially expressed in at least one comparison. We found that variations of mRNA levels for NRG1, SORT1 and TPH1 were interesting state-dependent biological markers of the disease. We also observed that variations in other mRNA expression were associated with treatment efficacy or clinical improvement (CREB1, HDAC5, HSPA2, HTR1B, HTR2A, and SLC6A4/5HTT). Significantly, 5HTT exhibited a strong correlation with clinical score evolution. We also found a state-independent marker, IL10. Moreover, the analysis of 2 separate MDEs concerning a same patient revealed comparable results for the expression of CREB1, HSPA2, HTR1B, NRG1 and TPH1. Overall, our results indicate that PBMCs obtained at different time points during MDE progression represent a promising avenue to discover biological markers for depression.

Hippocampal gene profiling: toward a systems biology of the hippocampus

Transcriptomics and proteomics approaches give a unique perspective for understanding brain and hippocampal functions but also pose unique challenges because of the singular complexity of the nervous system. The proliferation of genome-wide expression studies during the last decade has provided important insight into the molecular underpinnings of brain anatomy, neural plasticity, and neurological diseases. Microarray technology has dominated transcriptomics research, but this situation is rapidly changing with the recent technological advances in high-throughput sequencing. The full potential of transcriptomics in the neurosciences will be achieved as a result of its integration with other "-omics" disciplines as well as the development of novel analytical bioinformatics and systems biology tools for meta-analysis. Here, we review some of the most relevant advances in the gene profiling of the hippocampus, its relationship with proteomics approaches, and the promising perspectives for the future.

Coming to grips with complex disorders: genetic risk prediction in bipolar disorder using panels of genes identified through convergent functional genomics

We previously proposed and provided proof of principle for the use of a complementary approach, convergent functional genomics (CFG), combining gene expression and genetic data, from human and animal model studies, as a way of mining the existing GWAS datasets for signals that are there already, but did not reach significance using a genetics-only approach [Le-Niculescu et al., 2009b]. CFG provides a fit-to-disease prioritization of genes that leads to generalizability in independent cohorts, and counterbalances the fit-to-cohort prioritization inherent in classic genetic-only approaches, which have been plagued by poor reproducibility across cohorts. We have now extended our previous work to include more datasets of GWAS, and more recent evidence from other lines of work. In essence our analysis is the most comprehensive integration of genetics and functional genomics to date in the field of bipolar disorder. Biological pathway analyses identified top canonical pathways, and epistatic interaction testing inside these pathways has identified genes that merit future follow-up as direct interactors (intra-pathway epistasis, INPEP). Moreover, we have put together a panel of best P-value single nucleotide polymorphisms (SNPs), based on the top candidate genes we identified. We have developed a genetic risk prediction score (GRPS) based on our panel, and demonstrate how in two independent test cohorts the GRPS differentiates between subjects with bipolar disorder and normal controls, in both European-American and African-American populations. Lastly, we describe a prototype of how such testing could be used to categorize disease risk in individuals and aid personalized medicine approaches, in psychiatry and beyond.

Search for genetic markers and functional variants involved in the development of opiate and cocaine addiction and treatment

Addiction to opiates and illicit use of psychostimulants is a chronic, relapsing brain disease that, if left untreated, can cause major medical, social, and economic problems. This article reviews recent progress in studies of association of gene variants with vulnerability to develop opiate and cocaine addictions, focusing primarily on genes of the opioid and monoaminergic systems. In addition, we provide the first evidence of a cis-acting polymorphism and a functional haplotype in the PDYN gene, of significantly higher DNA methylation rate of the OPRM1 gene in the lymphocytes of heroin addicts, and significant differences in genotype frequencies of three single-nucleotide polymorphisms of the P-glycoprotein gene (ABCB1) between "higher" and "lower" methadone doses in methadone-maintained patients. In genomewide and multigene association studies, we found association of several new genes and new variants of known genes with heroin addiction. Finally, we describe the development and application of a novel technique: molecular haplotyping for studies in genetics of drug addiction.

Biomarkers in psychiatry: drawbacks and potential for misuse

For more than 20 years, researchers have attempted to identify diagnostic and prognostic biomarkers for psychiatric disorders including schizophrenia, major (unipolar) depression, and bipolar disorder. Advocates of this research contend that identifying such biomarkers will aid in the diagnosis of these disorders, as well as the possible development of effective psychiatric medications to treat them. Currently, there are no diagnostic tests available. This is largely due to the multi-factorial nature of psychiatric disorders. Biomarker testing of individuals is also prohibitively expensive because significant expertise is required to conduct tests and follow-up counseling for the patient is often necessary. It is cautioned that widespread biomarker testing could lead to negative consequences such as discrimination in health insurance and employment, as well as selective abortion.

Variants in doublecortin- and calmodulin kinase like 1, a gene up-regulated by BDNF, are associated with memory and general cognitive abilities

BACKGROUND

Human memory and general cognitive abilities are complex functions of high heritability and wide variability in the population. The brain-derived neurotrophic factor (BDNF) plays an important role in mammalian memory formation.

METHODOLOGY / PRINCIPAL FINDING

Based on the identification of genes markedly up-regulated during BDNF-induced synaptic consolidation in the hippocampus, we selected genetic variants that were tested in three independent samples, from Norway and Scotland, of adult individuals examined for cognitive abilities. In all samples, we show that markers in the doublecortin- and calmodulin kinase like 1 (DCLK1) gene, are significantly associated with general cognition (IQ scores) and verbal memory function, resisting multiple testing. DCLK1 is a complex gene with multiple transcripts which vary in expression and function. We show that the short variants are all up-regulated after BDNF treatment in the rat hippocampus, and that they are expressed in the adult human brain (mostly in cortices and hippocampus). We demonstrate that several of the associated variants are located in potential alternative promoter- and cis-regulatory elements of the gene and that they affect BDNF-mediated expression of short DCLK1 transcripts in a reporter system.

CONCLUSION

These data present DCLK1 as a functionally pertinent gene involved in human memory and cognitive functions.

Ontological Discovery Environment: a system for integrating gene-phenotype associations

The wealth of genomic technologies has enabled biologists to rapidly ascribe phenotypic characters to biological substrates. Central to effective biological investigation is the operational definition of the process under investigation. We propose an elucidation of categories of biological characters, including disease relevant traits, based on natural endogenous processes and experimentally observed biological networks, pathways and systems rather than on externally manifested constructs and current semantics such as disease names and processes. The Ontological Discovery Environment (ODE) is an Internet accessible resource for the storage, sharing, retrieval and analysis of phenotype-centered genomic data sets across species and experimental model systems. Any type of data set representing gene-phenotype relationships, such quantitative trait loci (QTL) positional candidates, literature reviews, microarray experiments, ontological or even meta-data, may serve as inputs. To demonstrate a use case leveraging the homology capabilities of ODE and its ability to synthesize diverse data sets, we conducted an analysis of genomic studies related to alcoholism. The core of ODE's gene set similarity, distance and hierarchical analysis is the creation of a bipartite network of gene-phenotype relations, a unique discrete graph approach to analysis that enables set-set matching of non-referential data. Gene sets are annotated with several levels of metadata, including community ontologies, while gene set translations compare models across species. Computationally derived gene sets are integrated into hierarchical trees based on gene-derived phenotype interdependencies. Automated set identifications are augmented by statistical tools which enable users to interpret the confidence of modeled results. This approach allows data integration and hypothesis discovery across multiple experimental contexts, regardless of the face similarity and semantic annotation of the experimental systems or species domain.

A functional haplotype implicated in vulnerability to develop cocaine dependence is associated with reduced PDYN expression in human brain

Dynorphin peptides and the kappa-opioid receptor are important in the rewarding properties of cocaine, heroin, and alcohol. We tested polymorphisms of the prodynorphin gene (PDYN) for association with cocaine dependence and cocaine/alcohol codependence. We genotyped six single nucleotide polymorphisms (SNPs), located in the promoter region, exon 4 coding, and 3' untranslated region, in 106 Caucasians and 204 African Americans who were cocaine dependent, cocaine/alcohol codependent, or controls. In Caucasians, we found point-wise significant associations of 3'UTR SNPs (rs910080, rs910079, and rs2235749) with cocaine dependence and cocaine/alcohol codependence. These SNPs are in high linkage disequilibrium, comprising a haplotype block. The haplotype CCT was significantly experiment-wise associated with cocaine dependence and with combined cocaine dependence and cocaine/alcohol codependence (false discovery rate, q=0.04 and 0.03, respectively). We investigated allele-specific gene expression of PDYN, using SNP rs910079 as a reporter, in postmortem human brains from eight heterozygous subjects, using SNaPshot assay. There was significantly lower expression for C allele (rs910079), with ratios ranging from 0.48 to 0.78, indicating lower expression of the CCT haplotype of PDYN in both the caudate and nucleus accumbens. Analysis of total PDYN expression in 43 postmortem brains also showed significantly lower levels of preprodynorphin mRNA in subjects having the risk CCT haplotype. This study provides evidence that a 3'UTR PDYN haplotype, implicated in vulnerability to develop cocaine addiction and/or cocaine/alcohol codependence, is related to lower mRNA expression of the PDYN gene in human dorsal and ventral striatum.

Convergent functional genomics of genome-wide association data for bipolar disorder: comprehensive identification of candidate genes, pathways and mechanisms

Given the mounting convergent evidence implicating many more genes in complex disorders such as bipolar disorder than the small number identified unambiguously by the first-generation Genome-Wide Association studies (GWAS) to date, there is a strong need for improvements in methodology. One strategy is to include in the next generation GWAS larger numbers of subjects, and/or to pool independent studies into meta-analyses. We propose and provide proof of principle for the use of a complementary approach, convergent functional genomics (CFG), as a way of mining the existing GWAS datasets for signals that are there already, but did not reach significance using a genetics-only approach. With the CFG approach, the integration of genetics with genomics, of human and animal model data, and of multiple independent lines of evidence converging on the same genes offers a way of extracting signal from noise and prioritizing candidates. In essence our analysis is the most comprehensive integration of genetics and functional genomics to date in the field of bipolar disorder, yielding a series of novel (such as Klf12, Aldh1a1, A2bp1, Ak3l1, Rorb, Rora) and previously known (such as Bdnf, Arntl, Gsk3b, Disc1, Nrg1, Htr2a) candidate genes, blood biomarkers, as well as a comprehensive identification of pathways and mechanisms. These become prime targets for hypothesis driven follow-up studies, new drug development and personalized medicine approaches.

An E3 ubiquitin ligase, Really Interesting New Gene (RING) Finger 41, is a candidate gene for anxiety-like behavior and beta-carboline-induced seizures

BACKGROUND

Identification of the genes underlying psychiatric illness remains a thorny problem. Previously, Quantitative Trait Loci (QTL) for anxiety-like behaviors and beta-carboline-induced seizure vulnerability have been mapped to the distal portion of mouse chromosome 10, with crosses of A/J and C57BL6 mice.

METHODS

An interval specific congenic strain for this chromosomal 10 region facilitated the genetic dissection of novelty-induced exploratory behaviors.

RESULTS

By microarray studies, an unsuspected E3 ubiquitin ligase, Really Interesting New Gene (RING) Finger 41 (Rnf41) was differentially expressed in the region of interest, being upregulated in the hippocampi of B6 compared with A/J as well as congenic A.B6(chr10) versus A/J. By quantitative real-time polymerase chain reaction (qRT-PCR), Rnf41 expression levels were significantly increased 1.5- and 1.3-fold in the hippocampi of C57BL6/J and A.B6(chr10) mice compared with A/J mice, respectively. Protein levels of Rnf41 were increased in hippocampi of B6 mice compared with A/J mice across postnatal development with a 5.5-fold difference at P56. Yeast two-hybrid studies searching for Rnf41 binding partners in fetal hippocampus identified several potential targets. An interaction between Rnf41 and NogoA was validated by glutathionine-S-transferase-Rnf41 pulldown experiments. Re-analysis of a microarray database of human postmortem prefrontal cortex (Brodmann's Area 46/10) found that RNF41 messenger RNA expression levels were reduced significantly in patients with major depression and bipolar disorder compared with unaffected control subjects and confirmed by qRT-PCR.

CONCLUSIONS

Overall, Rnf41 is nominated as a candidate gene for anxiety-like behaviors, depression, and vulnerability to seizures. The RNF41 and its binding partners suggest molecular pathways underlying behavior, highlighting a potential role for the ubiquitin proteasome system in psychiatric illness.

Identifying blood biomarkers for mood disorders using convergent functional genomics

There are to date no objective clinical laboratory blood tests for mood disorders. The current reliance on patient self-report of symptom severity and on the clinicians' impression is a rate-limiting step in effective treatment and new drug development. We propose, and provide proof of principle for, an approach to help identify blood biomarkers for mood state. We measured whole-genome gene expression differences in blood samples from subjects with bipolar disorder that had low mood vs those that had high mood at the time of the blood draw, and separately, changes in gene expression in brain and blood of a mouse pharmacogenomic model. We then integrated our human blood gene expression data with animal model gene expression data, human genetic linkage/association data and human postmortem brain data, an approach called convergent functional genomics, as a Bayesian strategy for cross-validating and prioritizing findings. Topping our list of candidate blood biomarker genes we have five genes involved in myelination (Mbp, Edg2, Mag, Pmp22 and Ugt8), and six genes involved in growth factor signaling (Fgfr1, Fzd3, Erbb3, Igfbp4, Igfbp6 and Ptprm). All of these genes have prior evidence of differential expression in human postmortem brains from mood disorder subjects. A predictive score developed based on a panel of 10 top candidate biomarkers (five for high mood and five for low mood) shows sensitivity and specificity for high mood and low mood states, in two independent cohorts. Our studies suggest that blood biomarkers may offer an unexpectedly informative window into brain functioning and disease state.

Target identification for CNS diseases by transcriptional profiling

Gene expression changes in neuropsychiatric and neurodegenerative disorders, and gene responses to therapeutic drugs, provide new ways to identify central nervous system (CNS) targets for drug discovery. This review summarizes gene and pathway targets replicated in expression profiling of human postmortem brain, animal models, and cell culture studies. Analysis of isolated human neurons implicates targets for Alzheimer's disease and the cognitive decline associated with normal aging and mild cognitive impairment. In addition to tau, amyloid-beta precursor protein, and amyloid-beta peptides (Abeta), these targets include all three high-affinity neurotrophin receptors and the fibroblast growth factor (FGF) system, synapse markers, glutamate receptors (GluRs) and transporters, and dopamine (DA) receptors, particularly the D2 subtype. Gene-based candidates for Parkinson's disease (PD) include the ubiquitin-proteosome system, scavengers of reactive oxygen species, brain-derived neurotrophic factor (BDNF), its receptor, TrkB, and downstream target early growth response 1, Nurr-1, and signaling through protein kinase C and RAS pathways. Increasing variability and decreases in brain mRNA production from middle age to old age suggest that cognitive impairments during normal aging may be addressed by drugs that restore antioxidant, DNA repair, and synaptic functions including those of DA to levels of younger adults. Studies in schizophrenia identify robust decreases in genes for GABA function, including glutamic acid decarboxylase, HINT1, glutamate transport and GluRs, BDNF and TrkB, numerous 14-3-3 protein family members, and decreases in genes for CNS synaptic and metabolic functions, particularly glycolysis and ATP generation. Many of these metabolic genes are increased by insulin and muscarinic agonism, both of which are therapeutic in psychosis. Differential genomic signals are relatively sparse in bipolar disorder, but include deficiencies in the expression of 14-3-3 protein members, implicating these chaperone proteins and the neurotransmitter pathways they support as possible drug targets. Brains from persons with major depressive disorder reveal decreased expression for genes in glutamate transport and metabolism, neurotrophic signaling (eg, FGF, BDNF and VGF), and MAP kinase pathways. Increases in these pathways in the brains of animals exposed to electroconvulsive shock and antidepressant treatments identify neurotrophic and angiogenic growth factors and second messenger stimulation as therapeutic approaches for the treatment of depression.

Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission

Despite known heritability, the complex genetic architecture of bipolar disorder (likely including trait, locus and allelic heterogeneity, as well as genetic interactions) has confounded genetic discovery for many years. Even modern day whole genome association studies (WGAS) using over half a million common SNPs have implicated only a handful of genes at the genomewide level. Temporally coincident with this series of WGAS, a host of pathways-based analyses (PBAs) have emerged as novel computational approaches in the examination of large-scale datasets, but thus far rarely have been applied to WGAS data in psychiatric disorders. Here, we report a series of PBAs conducted using exploratory visual analysis, an analytic and visualization software tool for examining genomic data, to examine results from the National Institutes of Mental Health and Wellcome-Trust Case Control Consortium WGAS in bipolar disorder. Consistent with a host of prior linkage findings, some candidate gene association studies, and recent WGAS, our strongest findings suggest involvement of ion channel structural and regulatory genes, including voltage-gated ion channels and the broader ion channel group that comprises both voltage- and ligand-gated channels. Moreover, we found only modest overlap in the particular genes driving the significance of these gene sets across the analyses. This observation strongly suggests that variation in ion channel genes, as a class of genes, may contribute to the susceptibility of bipolar disorder and that heterogeneity may figure prominently in the genetic architecture of this susceptibility.

Phenomic, convergent functional genomic, and biomarker studies in a stress-reactive genetic animal model of bipolar disorder and co-morbid alcoholism

We had previously identified the clock gene D-box binding protein (Dbp) as a potential candidate gene for bipolar disorder and for alcoholism, using a Convergent Functional Genomics (CFG) approach. Here we report that mice with a homozygous deletion of DBP have lower locomotor activity, blunted responses to stimulants, and gain less weight over time. In response to a chronic stress paradigm, these mice exhibit a diametric switch in these phenotypes. DBP knockout mice are also activated by sleep deprivation, similar to bipolar patients, and that activation is prevented by treatment with the mood stabilizer drug valproate. Moreover, these mice show increased alcohol intake following exposure to stress. Microarray studies of brain and blood reveal a pattern of gene expression changes that may explain the observed phenotypes. CFG analysis of the gene expression changes identified a series of novel candidate genes and blood biomarkers for bipolar disorder, alcoholism, and stress reactivity.

Genetics of bipolar disorder

Family and twin studies have consistently documented that bipolar disorder (BPD) is familial and heritable, but efforts to identify specific susceptibility genes have been complicated by the disorder's genetic and phenotypic complexity. Genetic linkage studies have implicated numerous chromosomal regions, but findings have been inconsistent. As with other complex disorders, it has become clear that linkage analysis lacks the power and precision to identify susceptibility loci for BPD. Candidate gene association studies have implicated several specific genes, but these studies have been limited by our incomplete understanding of the disorder's biology, and there have been few robustly replicated results. Within the past 2 years, a major advance in the genetics of complex disease has become feasible in the form of genome-wide association studies. Such studies, which require large sample sizes, have already proven successful in identifying susceptibility variants for a range of common medical disorders. Genome-wide association studies have begun to appear for BPD, and more are in progress. By providing an unbiased approach, this technology may reveal novel biological mechanisms underlying BPD.

Animal models of bipolar disorder and mood stabilizer efficacy: a critical need for improvement

The limited number of suitable animal models of bipolar disorder available for in-depth behavioral, biochemical, histological, and pharmacological analysis is a rate-limiting step in the process of understanding the relevant neurobiology of the disorder, as well as the development of novel medications. In the search for new models, both new and old approaches hold promise for future discoveries. Clinical studies regarding the underlying genetics and pathophysiology of bipolar disorder are providing important clues. In particular, the identification of susceptibility genes for bipolar disorder will help to define specific neurobiological processes, and associated behaviors, that are unquestionably involved in the pathways connecting genes and distal symptoms. These endophenotypes will hold great value in further enhancing the validity of animal models and will strongly complement symptom-based models and models of medication efficacy. Regardless of the path taken by different researchers to develop better models, we believe that this area of work requires additional attention not only from researchers but also from funding agencies.