Identification of blood biomarkers for psychosis using convergent functional genomics

TCF4 (e2-2; ITF2): a schizophrenia-associated gene with pleiotropic effects on human disease

Common SNPs in the transcription factor 4 (TCF4; ITF2, E2-2, SEF-2) gene, which encodes a basic Helix-Loop-Helix (bHLH) transcription factor, are associated with schizophrenia, conferring a small increase in risk. Other common SNPs in the gene are associated with the common eye disorder Fuch's corneal dystrophy, while rare, mostly de novo inactivating mutations cause Pitt-Hopkins syndrome. In this review, we present a systematic bioinformatics and literature review of the genomics, biological function and interactome of TCF4 in the context of schizophrenia. The TCF4 gene is present in all vertebrates, and although protein length varies, there is high conservation of primary sequence, including the DNA binding domain. Humans have a unique leucine-rich nuclear export signal. There are two main isoforms (A and B), as well as complex splicing generating many possible N-terminal amino acid sequences. TCF4 is highly expressed in the brain, where plays a role in neurodevelopment, interacting with class II bHLH transcription factors Math1, HASH1, and neuroD2. The Ca(2+) sensor protein calmodulin interacts with the DNA binding domain of TCF4, inhibiting transcriptional activation. It is also the target of microRNAs, including mir137, which is implicated in schizophrenia. The schizophrenia-associated SNPs are in linkage disequilibrium with common variants within putative DNA regulatory elements, suggesting that regulation of expression may underlie association with schizophrenia. Combined gene co-expression analyses and curated protein-protein interaction data provide a network involving TCF4 and other putative schizophrenia susceptibility genes. These findings suggest new opportunities for understanding the molecular basis of schizophrenia and other mental disorders.

Deep brain stimulation induces rapidly reversible transcript changes in Parkinson's leucocytes

Subthalamic deep brain stimulation (DBS) reversibly modulates Parkinson's disease (PD) motor symptoms, providing an unusual opportunity to compare leucocyte transcripts in the same individuals before and after neurosurgery and 1 hr after stimulus cessation (ON- and OFF-stimulus). Here, we report DBS-induced reversibility and OFF-stimulus restoration in 12 of 16 molecular functions and 3 of 4 biological processes shown in exon microarrays to be differentially expressed between PD patients and controls, post-DBS from pre-DBS and OFF from ON states. Intriguingly, 6 of 18 inflammation and immune-related functions exhibited reversibility, and the extent of stimulus-induced changes correlated with the neurological DBS efficacy, suggesting mechanistic implications. A minimal list of 29 transcripts that changed in all three comparisons between states discriminated pre-surgery and OFF states from post-surgery and controls. Six of these transcripts were found to be able to distinguish between PD patients and both healthy controls and patients with other neurological diseases in a previously published whole blood 3’ array data study of early PD patients. Our findings support the future use of this approach for identifying targets for therapeutic intervention and assessing the efficacy of current and new treatments in this and other neurological diseases.

Immune system gene dysregulation in autism and schizophrenia

Gene*environment interactions play critical roles in the emergence of autism and schizophrenia pathophysiology. In both disorders, recent genetic association studies have provided evidence for disease-linked variation in immune system genes and postmortem gene expression studies have shown extensive chronic immune abnormalities in brains of diseased subjects. Furthermore, peripheral biomarker studies revealed that both innate and adaptive immune systems are dysregulated. In both disorders symptoms of the disease correlate with the immune system dysfunction; yet, in autism this process appears to be chronic and sustained, while in schizophrenia it is exacerbated during acute episodes. Furthermore, since immune abnormalities endure into adulthood and anti-inflammatory agents appear to be beneficial, it is likely that these immune changes actively contribute to disease symptoms. Modeling these changes in animals provided further evidence that prenatal maternal immune activation alters neurodevelopment and leads to behavioral changes that are relevant for autism and schizophrenia. The converging evidence strongly argues that neurodevelopmental immune insults and genetic background critically interact and result in increased risk for either autism or schizophrenia. Further research in these areas may improve prenatal health screening in genetically at-risk families and may also lead to new preventive and/or therapeutic strategies.

Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction

We have used a translational convergent functional genomics (CFG) approach to identify and prioritize genes involved in schizophrenia, by gene-level integration of genome-wide association study data with other genetic and gene expression studies in humans and animal models. Using this polyevidence scoring and pathway analyses, we identify top genes (DISC1, TCF4, MBP, MOBP, NCAM1, NRCAM, NDUFV2, RAB18, as well as ADCYAP1, BDNF, CNR1, COMT, DRD2, DTNBP1, GAD1, GRIA1, GRIN2B, HTR2A, NRG1, RELN, SNAP-25, TNIK), brain development, myelination, cell adhesion, glutamate receptor signaling, G-protein-coupled receptor signaling and cAMP-mediated signaling as key to pathophysiology and as targets for therapeutic intervention. Overall, the data are consistent with a model of disrupted connectivity in schizophrenia, resulting from the effects of neurodevelopmental environmental stress on a background of genetic vulnerability. In addition, we show how the top candidate genes identified by CFG can be used to generate a genetic risk prediction score (GRPS) to aid schizophrenia diagnostics, with predictive ability in independent cohorts. The GRPS also differentiates classic age of onset schizophrenia from early onset and late-onset disease. We also show, in three independent cohorts, two European American and one African American, increasing overlap, reproducibility and consistency of findings from single-nucleotide polymorphisms to genes, then genes prioritized by CFG, and ultimately at the level of biological pathways and mechanisms. Finally, we compared our top candidate genes for schizophrenia from this analysis with top candidate genes for bipolar disorder and anxiety disorders from previous CFG analyses conducted by us, as well as findings from the fields of autism and Alzheimer. Overall, our work maps the genomic and biological landscape for schizophrenia, providing leads towards a better understanding of illness, diagnostics and therapeutics. It also reveals the significant genetic overlap with other major psychiatric disorder domains, suggesting the need for improved nosology.

P2RX7: expression responds to sleep deprivation and associates with rapid cycling in bipolar disorder type 1

Context

Rapid cycling is a severe form of bipolar disorder with an increased rate of episodes that is particularly treatment-responsive to chronotherapy and stable sleep-wake cycles. We hypothesized that the P2RX7 gene would be affected by sleep deprivation and be implicated in rapid cycling.

Objectives

To assess whether P2RX7 expression is affected by total sleep deprivation and if variation in P2RX7 is associated with rapid cycling in bipolar patients.

Design

Gene expression analysis in peripheral blood mononuclear cells (PBMCs) from healthy volunteers and case-case and case-control SNP/haplotype association analyses in patients.

Participants

Healthy volunteers at the sleep research center, University of California, Irvine Medical Center (UCIMC), USA (n = 8) and Swedish outpatients recruited from specialized psychiatric clinics for bipolar disorder, diagnosed with bipolar disorder type 1 (n = 569; rapid cycling: n = 121) and anonymous blood donor controls (n = 1,044).

Results

P2RX7 RNA levels were significantly increased during sleep deprivation in PBMCs from healthy volunteers (p = 2.3*10⁻⁹). The P2RX7 rs2230912 _A allele was more common (OR = 2.2, p = 0.002) and the ACGTTT haplotype in P2RX7 (rs1718119 to rs1621388) containing the protective rs2230912_G allele (OR = 0.45–0.49, p = 0.003–0.005) was less common, among rapid cycling cases compared to non-rapid cycling bipolar patients and blood donor controls.

Conclusions

Sleep deprivation increased P2RX7 expression in healthy persons and the putatively low-activity P2RX7 rs2230912 allele A variant was associated with rapid cycling in bipolar disorder. This supports earlier findings of P2RX7 associations to affective disorder and is in agreement with that particularly rapid cycling patients have a more vulnerable diurnal system.

DNA methylation analysis of the angiotensin converting enzyme (ACE) gene in major depression

BACKGROUND

The angiotensin converting enzyme (ACE) has been repeatedly discussed as susceptibility factor for major depression (MD) and the bi-directional relation between MD and cardiovascular disorders (CVD). In this context, functional polymorphisms of the ACE gene have been linked to depression, to antidepressant treatment response, to ACE serum concentrations, as well as to hypertension, myocardial infarction and CVD risk markers. The mostly investigated ACE Ins/Del polymorphism accounts for ~40%-50% of the ACE serum concentration variance, the remaining half is probably determined by other genetic, environmental or epigenetic factors, but these are poorly understood.

MATERIALS AND METHODS

The main aim of the present study was the analysis of the DNA methylation pattern in the regulatory region of the ACE gene in peripheral leukocytes of 81 MD patients and 81 healthy controls.

RESULTS

We detected intensive DNA methylation within a recently described, functional important region of the ACE gene promoter including hypermethylation in depressed patients (p = 0.008) and a significant inverse correlation between the ACE serum concentration and ACE promoter methylation frequency in the total sample (p = 0.02). Furthermore, a significant inverse correlation between the concentrations of the inflammatory CVD risk markers ICAM-1, E-selectin and P-selectin and the degree of ACE promoter methylation in MD patients could be demonstrated (p = 0.01 - 0.04).

CONCLUSION

The results of the present study suggest that aberrations in ACE promoter DNA methylation may be an underlying cause of MD and probably a common pathogenic factor for the bi-directional relationship between MD and cardiovascular disorders.

A gene co-expression network in whole blood of schizophrenia patients is independent of antipsychotic-use and enriched for brain-expressed genes

Despite large-scale genome-wide association studies (GWAS), the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co-expression modules associated with schizophrenia. Several of these disease related modules are likely to reflect expression changes due to antipsychotic medication. However, two of the disease modules could be replicated in an independent second data set involving antipsychotic-free patients and controls. One of these robustly defined disease modules is significantly enriched with brain-expressed genes and with genetic variants that were implicated in a GWAS study, which could imply a causal role in schizophrenia etiology. The most highly connected intramodular hub gene in this module (ABCF1), is located in, and regulated by the major histocompatibility (MHC) complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes in this network.

TCF4 sequence variants and mRNA levels are associated with neurodevelopmental characteristics in psychotic disorders

TCF4 is involved in neurodevelopment, and intergenic and intronic variants in or close to the TCF4 gene have been associated with susceptibility to schizophrenia. However, the functional role of TCF4 at the level of gene expression and relationship to severity of core psychotic phenotypes are not known. TCF4 mRNA expression level in peripheral blood was determined in a large sample of patients with psychosis spectrum disorders (n = 596) and healthy controls (n = 385). The previously identified TCF4 risk variants (rs12966547 (G), rs9960767 (C), rs4309482 (A), rs2958182 (T) and rs17512836 (C)) were tested for association with characteristic psychosis phenotypes, including neurocognitive traits, psychotic symptoms and structural magnetic resonance imaging brain morphometric measures, using a linear regression model. Further, we explored the association of additional 59 single nucleotide polymorphisms (SNPs) covering the TCF4 gene to these phenotypes. The rs12966547 and rs4309482 risk variants were associated with poorer verbal fluency in the total sample. There were significant associations of other TCF4 SNPs with negative symptoms, verbal learning, executive functioning and age at onset in psychotic patients and brain abnormalities in total sample. The TCF4 mRNA expression level was significantly increased in psychosis patients compared with controls and positively correlated with positive- and negative-symptom levels. The increase in TCF4 mRNA expression level in psychosis patients and the association of TCF4 SNPs with core psychotic phenotypes across clinical, cognitive and brain morphological domains support that common TCF4 variants are involved in psychosis pathology, probably related to abnormal neurodevelopment.

Dexamethasone stimulated gene expression in peripheral blood is a sensitive marker for glucocorticoid receptor resistance in depressed patients

Although gene expression profiles in peripheral blood in major depression are not likely to identify genes directly involved in the pathomechanism of affective disorders, they may serve as biomarkers for this disorder. As previous studies using baseline gene expression profiles have provided mixed results, our approach was to use an in vivo dexamethasone challenge test and to compare glucocorticoid receptor (GR)-mediated changes in gene expression between depressed patients and healthy controls. Whole genome gene expression data (baseline and following GR-stimulation with 1.5 mg dexamethasone p.o.) from two independent cohorts were analyzed to identify gene expression pattern that would predict case and control status using a training (N=18 cases/18 controls) and a test cohort (N=11/13). Dexamethasone led to reproducible regulation of 2670 genes in controls and 1151 transcripts in cases. Several genes, including FKBP5 and DUSP1, previously associated with the pathophysiology of major depression, were found to be reliable markers of GR-activation. Using random forest analyses for classification, GR-stimulated gene expression outperformed baseline gene expression as a classifier for case and control status with a correct classification of 79.1 vs 41.6% in the test cohort. GR-stimulated gene expression performed best in dexamethasone non-suppressor patients (88.7% correctly classified with 100% sensitivity), but also correctly classified 77.3% of the suppressor patients (76.7% sensitivity), when using a refined set of 19 genes. Our study suggests that in vivo stimulated gene expression in peripheral blood cells could be a promising molecular marker of altered GR-functioning, an important component of the underlying pathology, in patients suffering from depressive episodes.

Discovery of blood transcriptomic markers for depression in animal models and pilot validation in subjects with early-onset major depression

Early-onset major depressive disorder (MDD) is a serious and prevalent psychiatric illness in adolescents and young adults. Current treatments are not optimally effective. Biological markers of early-onset MDD could increase diagnostic specificity, but no such biomarker exists. Our innovative approach to biomarker discovery for early-onset MDD combined results from genome-wide transcriptomic profiles in the blood of two animal models of depression, representing the genetic and the environmental, stress-related, etiology of MDD. We carried out unbiased analyses of this combined set of 26 candidate blood transcriptomic markers in a sample of 15-19-year-old subjects with MDD (N=14) and subjects with no disorder (ND, N=14). A panel of 11 blood markers differentiated participants with early-onset MDD from the ND group. Additionally, a separate but partially overlapping panel of 18 transcripts distinguished subjects with MDD with or without comorbid anxiety. Four transcripts, discovered from the chronic stress animal model, correlated with maltreatment scores in youths. These pilot data suggest that our approach can lead to clinically valid diagnostic panels of blood transcripts for early-onset MDD, which could reduce diagnostic heterogeneity in this population and has the potential to advance individualized treatment strategies.

Schizophrenia susceptibility alleles are enriched for alleles that affect gene expression in adult human brain

It is widely thought that alleles that influence susceptibility to common diseases, including schizophrenia, will frequently do so through effects on gene expression. As only a small proportion of the genetic variance for schizophrenia has been attributed to specific loci, this remains an unproven hypothesis. The International Schizophrenia Consortium (ISC) recently reported a substantial polygenic contribution to that disorder, and that schizophrenia risk alleles are enriched among single-nucleotide polymorphisms (SNPs) selected for marginal evidence for association (P<0.5) from genome-wide association studies (GWAS). It follows that if schizophrenia susceptibility alleles are enriched for those that affect gene expression, those marginally associated SNPs, which are also expression quantitative trait loci (eQTLs), should carry more true association signals compared with SNPs that are not marginally associated. To test this, we identified marginally associated (P<0.5) SNPs from two of the largest available schizophrenia GWAS data sets. We assigned eQTL status to those SNPs based upon an eQTL data set derived from adult human brain. Using the polygenic score method of analysis reported by the ISC, we observed and replicated the observation that higher probability cis-eQTLs predicted schizophrenia better than those with a lower probability for being a cis-eQTL. Our data support the hypothesis that alleles conferring risk of schizophrenia are enriched among those that affect gene expression. Moreover, our data show that notwithstanding the likely developmental origin of schizophrenia, studies of adult brain tissue can, in principle, allow relevant susceptibility eQTLs to be identified.

Systems biology approach for new target and biomarker identification

The pharmaceutical industry is spending increasingly large amounts of money on the discovery and development of novel medicines, but this investment is not adequately paying off in an increased rate of newly approved drugs by the FDA. The post-genomic era has provided a wealth of novel approaches for generating large, high-dimensional genetic and transcriptomic data sets from large cohorts of preclinical species as well as normal and diseased individuals. This systems biology approach to understanding disease-related biology is revolutionizing our understanding of the cellular pathways and gene networks underlying the onset of disease, and the mechanisms of pharmacological treatments that ameliorate disease phenotypes. In this article, we review a number of approaches being used by pharmaceutical and biotechnology companies, e.g., high-throughput DNA genotyping, sequencing, and genome-wide gene expression profiling, to enable drug discovery and development through the identification of new drug targets and biomarkers of disease progression, drug pharmacodynamics, and predictive markers for selecting the patients most likely to respond to therapy.

Biomarkers for antipsychotic therapies

Molecular biomarkers for antipsychotic treatments have been conceptually linked to the measurements of dopamine functions, mostly D(2) receptor occupancy, either by imaging using selective PET/SPECT radioactive tracers or by assessing plasma prolactin levels. A quest for novel biomarkers was recently proposed by various academic, health service, and industrial institutions driven by the need for better treatments of psychoses. In this review we conceptualize biomarkers within the Translational Medicine paradigm whose goal was to provide support to critical decision-making in drug discovery. At first we focused on biomarkers as outcome measure of clinical studies by searching into the database clinicaltrial.gov. The results were somewhat disappointing, showing that out of 1,659 antipsychotic trials only 18 used a biomarker as an outcome measure. Several of these trials targeted plasma lipids as sentinel marker for metabolic adverse effects associated with the use of atypical antipsychotics, while only few studies were aimed to new disease specific biological markers. As an example of a mechanistic biomarker, we described the work done to progress the novel class of glycine transporter inhibitors as putative treatment for negative symptoms of schizophrenia. We also review how large-scale multiplex biological assays were applied to samples from tissues of psychiatric patients, so to learn from changes of numerous analytes (metabolic products, lipids, proteins, RNA transcripts) about the substrates involved in the disease. We concluded that a stringent implementation of these techniques could contribute to the endophenotypic characterization of patients, helping in the identification of key biomarkers to drive personalized medicine and new treatment development.

Autism-associated gene expression in peripheral leucocytes commonly observed between subjects with autism and healthy women having autistic children

Autism spectrum disorder (ASD) is a severe neuropsychiatric disorder which has complex pathobiology with profound influences of genetic factors in its development. Although the numerous autism susceptible genes were identified, the etiology of autism is not fully explained. Using DNA microarray, we examined gene expression profiling in peripheral blood from 21 individuals in each of the four groups; young adults with ASD, age- and gender-matched healthy subjects (ASD control), healthy mothers having children with ASD (asdMO), and asdMO control. There was no blood relationship between ASD and asdMO. Comparing the ASD group with control, 19 genes were found to be significantly changed. These genes were mainly involved in cell morphology, cellular assembly and organization, and nerve system development and function. In addition, the asdMO group possessed a unique gene expression signature shown as significant alterations of protein synthesis despite of their nonautistic diagnostic status. Moreover, an ASD-associated gene expression signature was commonly observed in both individuals with ASD and asdMO. This unique gene expression profiling detected in peripheral leukocytes from affected subjects with ASD and unaffected mothers having ASD children suggest that a genetic predisposition to ASD may be detectable even in peripheral cells. Altered expression of several autism candidate genes such as FMR-1 and MECP2, could be detected in leukocytes. Taken together, these findings suggest that the ASD-associated genes identified in leukocytes are informative to explore the genetic, epigenetic, and environmental background of ASD and might become potential tools to assess the crucial factors related to the clinical onset of the disorder.

Convergent genomic studies identify association of GRIK2 and NPAS2 with chronic fatigue syndrome

BACKGROUND

There is no consistent evidence of specific gene(s) or molecular pathways that contribute to the pathogenesis, therapeutic intervention or diagnosis of chronic fatigue syndrome (CFS). While multiple studies support a role for genetic variation in CFS, genome-wide efforts to identify associated loci remain unexplored. We employed a novel convergent functional genomics approach that incorporates the findings from single-nucleotide polymorphism (SNP) and mRNA expression studies to identify associations between CFS and novel candidate genes for further investigation.

METHODS

We evaluated 116,204 SNPs in 40 CFS and 40 nonfatigued control subjects along with mRNA expression of 20,160 genes in a subset of these subjects (35 CFS subjects and 27 controls) derived from a population-based study.

RESULTS

Sixty-five SNPs were nominally associated with CFS (p<0.001), and 165 genes were differentially expressed (≥4-fold; p≤0.05) in peripheral blood mononuclear cells of CFS subjects. Two genes, glutamate receptor, ionotropic, kinase 2 (GRIK2) and neuronal PAS domain protein 2 (NPAS2), were identified by both SNP and gene expression analyses. Subjects with the G allele of rs2247215 (GRIK2) were more likely to have CFS (p=0.0005), and CFS subjects showed decreased GRIK2 expression (10-fold; p=0.015). Subjects with the T allele of rs356653 (NPAS2) were more likely to have CFS (p=0.0007), and NPAS2 expression was increased (10-fold; p=0.027) in those with CFS.

CONCLUSION

Using an integrated genomic strategy, this study suggests a possible role for genes involved in glutamatergic neurotransmission and circadian rhythm in CFS and supports further study of novel candidate genes in independent populations of CFS subjects.

Common variants at VRK2 and TCF4 conferring risk of schizophrenia

Common sequence variants have recently joined rare structural polymorphisms as genetic factors with strong evidence for association with schizophrenia. Here we extend our previous genome-wide association study and meta-analysis (totalling 7 946 cases and 19 036 controls) by examining an expanded set of variants using an enlarged follow-up sample (up to 10 260 cases and 23 500 controls). In addition to previously reported alleles in the major histocompatibility complex region, near neurogranin (NRGN) and in an intron of transcription factor 4 (TCF4), we find two novel variants showing genome-wide significant association: rs2312147[C], upstream of vaccinia-related kinase 2 (VRK2) [odds ratio (OR) = 1.09, P = 1.9 × 10(-9)] and rs4309482[A], between coiled-coiled domain containing 68 (CCDC68) and TCF4, about 400 kb from the previously described risk allele, but not accounted for by its association (OR = 1.09, P = 7.8 × 10(-9)).

Advances in multidisciplinary and cross-species approaches to examine the neurobiology of psychiatric disorders

Current approaches to dissect the molecular neurobiology of complex neuropsychiatric disorders such as schizophrenia and major depression have been rightly criticized for failing to provide benefits to patients. Improving the translational potential of our efforts will require the development and refinement of better disease models that consider a wide variety of contributing factors, such as genetic variation, gene-by-environment interactions, endophenotype or intermediate phenotype assessment, cross species analysis, sex differences, and developmental stages. During a targeted expert meeting of the European College of Neuropsychopharmacology (ECNP) in Istanbul, we addressed the opportunities and pitfalls of current translational animal models of psychiatric disorders and agreed on a series of core guidelines and recommendations that we believe will help guiding further research in this area.

Transcriptional profiling to identify biomarkers of disease and drug response

The discovery, biological qualification and analytical validation of genomic biomarkers requires extensive collaborations between individuals with expertise in biology, statistics, bioinformatics, chemistry, clinical medicine, regulatory science and so on. For clinical utility, blood-borne biomarkers (e.g., mRNA and miRNA) of organ damage, drug toxicity and/or response would be preferred to those that are tissue based. Currently used biomarkers such as serum creatinine (indicating renal dysfunction) denote organ damage whether caused by disease, physical injury or drugs. Therefore, it is anticipated that studies of disease will discover biomarkers that can also be used to identify drug-induced injury and vice versa. This article describes transcriptomic blood-borne biomarkers that have been reported to be connected with disease and drug toxicity. Much more qualification and validation needs to be carried out before many of these biomarkers can prove useful. Discussed here are some of the lessons learned and roadblocks to success.

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.

Convergent functional genomic studies of ω-3 fatty acids in stress reactivity, bipolar disorder and alcoholism

Omega-3 fatty acids have been proposed as an adjuvant treatment option in psychiatric disorders. Given their other health benefits and their relative lack of toxicity, teratogenicity and side effects, they may be particularly useful in children and in females of child-bearing age, especially during pregnancy and postpartum. A comprehensive mechanistic understanding of their effects is needed. Here we report translational studies demonstrating the phenotypic normalization and gene expression effects of dietary omega-3 fatty acids, specifically docosahexaenoic acid (DHA), in a stress-reactive knockout mouse model of bipolar disorder and co-morbid alcoholism, using a bioinformatic convergent functional genomics approach integrating animal model and human data to prioritize disease-relevant genes. Additionally, to validate at a behavioral level the novel observed effects on decreasing alcohol consumption, we also tested the effects of DHA in an independent animal model, alcohol-preferring (P) rats, a well-established animal model of alcoholism. Our studies uncover sex differences, brain region-specific effects and blood biomarkers that may underpin the effects of DHA. Of note, DHA modulates some of the same genes targeted by current psychotropic medications, as well as increases myelin-related gene expression. Myelin-related gene expression decrease is a common, if nonspecific, denominator of neuropsychiatric disorders. In conclusion, our work supports the potential utility of omega-3 fatty acids, specifically DHA, for a spectrum of psychiatric disorders such as stress disorders, bipolar disorder, alcoholism and beyond.

The first decade and beyond of transcriptional profiling in schizophrenia

Gene expression changes in brains of individuals with schizophrenia (SZ) have been hypothesized to reflect possible pathways related to pathophysiology and/or medication. Other factors having robust effects on gene expression profiling in brain and possibly influence the schizophrenia transcriptome such as age and pH are examined. Pathways of curated gene expression or gene correlation networks reported in SZ (white matter, apoptosis, neurogenesis, synaptic plasticity, glutamatergic and GABAergic neurotransmission, immune and stress-response, mitochondrial, and neurodevelopment) are not unique to SZ and have been associated with other psychiatric disorders. Suggestions going forward to improve the next decade of profiling: consider multiple brain regions that are carefully dissected, release large datasets from multiple brain regions in controls to better understand neurocircuitry, integrate genetics and gene expression, measure expression variants on genome wide level, peripheral biomarker studies, and analyze the transcriptome across a developmental series of brains. Gene expression, while an important feature of the genomic landscape, requires further systems biology to advance from control brains to a more precise definition of the schizophrenia interactome.

Prioritization of epilepsy associated candidate genes by convergent analysis

Background

Epilepsy is a severe neurological disorder affecting a large number of individuals, yet the underlying genetic risk factors for epilepsy remain unclear. Recent studies have revealed several recurrent copy number variations (CNVs) that are more likely to be associated with epilepsy. The responsible gene(s) within these regions have yet to be definitively linked to the disorder, and the implications of their interactions are not fully understood. Identification of these genes may contribute to a better pathological understanding of epilepsy, and serve to implicate novel therapeutic targets for further research.

Methodology/Principal Findings

In this study, we examined genes within heterozygous deletion regions identified in a recent large-scale study, encompassing a diverse spectrum of epileptic syndromes. By integrating additional protein-protein interaction data, we constructed subnetworks for these CNV-region genes and also those previously studied for epilepsy. We observed 20 genes common to both networks, primarily concentrated within a small molecular network populated by GABA receptor, BDNF/MAPK signaling, and estrogen receptor genes. From among the hundreds of genes in the initial networks, these were designated by convergent evidence for their likely association with epilepsy. Importantly, the identified molecular network was found to contain complex interrelationships, providing further insight into epilepsy's underlying pathology. We further performed pathway enrichment and crosstalk analysis and revealed a functional map which indicates the significant enrichment of closely related neurological, immune, and kinase regulatory pathways.

Conclusions/Significance

The convergent framework we proposed here provides a unique and powerful approach to screening and identifying promising disease genes out of typically hundreds to thousands of genes in disease-related CNV-regions. Our network and pathway analysis provides important implications for the underlying molecular mechanisms for epilepsy. The strategy can be applied for the study of other complex diseases.

Integrative and comparative genomics analysis of early hepatocellular carcinoma differentiated from liver regeneration in young and old

BACKGROUND

Hepatocellular carcinoma (HCC) is the third-leading cause of cancer-related deaths worldwide. It is often diagnosed at an advanced stage, and hence typically has a poor prognosis. To identify distinct molecular mechanisms for early HCC we developed a rat model of liver regeneration post-hepatectomy, as well as liver cells undergoing malignant transformation and compared them to normal liver using a microarray approach. Subsequently, we performed cross-species comparative analysis coupled with copy number alterations (CNA) of independent early human HCC microarray studies to facilitate the identification of critical regulatory modules conserved across species.

RESULTS

We identified 35 signature genes conserved across species, and shared among different types of early human HCCs. Over 70% of signature genes were cancer-related, and more than 50% of the conserved genes were mapped to human genomic CNA regions. Functional annotation revealed genes already implicated in HCC, as well as novel genes which were not previously reported in liver tumors. A subset of differentially expressed genes was validated using quantitative RT-PCR. Concordance was also confirmed for a significant number of genes and pathways in five independent validation microarray datasets. Our results indicated alterations in a number of cancer related pathways, including p53, p38 MAPK, ERK/MAPK, PI3K/AKT, and TGF-beta signaling pathways, and potential critical regulatory role of MYC, ERBB2, HNF4A, and SMAD3 for early HCC transformation.

CONCLUSIONS

The integrative analysis of transcriptional deregulation, genomic CNA and comparative cross species analysis brings new insights into the molecular profile of early hepatoma formation. This approach may lead to robust biomarkers for the detection of early human HCC.