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

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.

Possible oxytocin-related biomarkers in anxiety and mood disorders

Anxiety and mood disorders are prevalent, disabling, and frequently difficult to treat. Such disorders are often comorbid and share similar characteristics. For more accurate diagnosis and improved treatment, a deeper understanding of the pathophysiology of anxiety and mood disorders is important. Oxytocin, a neuropeptide synthesized in the hypothalamus, affects human psychology and behaviors such as social and affiliative behaviors, fear and emotion processing, and stress regulation. Thus, oxytocin is believed to exert anxiolytic and antidepressant-like effects. This review article provides an overview of clinical studies on relationships between the oxytocin system and anxiety and mood disorders, focusing on oxytocin-related biomarker findings. Biomarkers used in such studies include central and peripheral oxytocin levels, analysis of oxytocin-related genes, and expression levels of oxytocin and oxytocin receptor genes in postmortem brains. Although a growing number of studies support the presence of oxytocinergic effects on anxiety and mood disorders, study results are heterogeneous and inconclusive. Moderating factors such as the characteristics of study populations, including sex, age, context, early life adversity, and attachment styles in patient cohorts, might affect the heterogeneity of the study results. Limitations in existing research such as small sample sizes, large dependence on peripheral sources of oxytocin, and inconsistent results between immunoassay methods complicate the interpretation of existing findings.

Early maternal deprivation impairs learning and memory and alters hippocampal gene expression in adult male rats

Maternal deprivation (MD) in early life severely disrupts hippocampal development, leading to persistent cognitive and behavior deficits. The current study uncovered that early MD (P1-P21) impaired spatial learning and memory capacity detected by Morris water maze (MWM) tests from juvenile (P31) to adult (P81) rats compared to age-matched controls. And the protein expression in hippocampus were detected by two-dimensional gel electrophoresis (2-DE) before MWM, respectively. Protein changes in hippocampal were examined to identify the molecular pathways underlying MD-induced hippocampal dysfunction. There were 11 differentially expressed proteins analyzed between adult MD and control male rats, while the 8 proteins were then identified by UPLC-ESI-Q-TOF-MS. Gene Ontology (GO) annotations of the identified proteins were related to neuronal and glial cytoskeletal dynamics, membrane signaling, stress responses, biosynthesis, and metabolism. The different expression proteins spectrin alpha chain, non-erythrocytic 1 (Sptan1), ATP-citrate synthase (Acly), and heat shock protein 90-alpha (Hsp90aa1) have been verified by western blot analysis, and their expression levels showed consistent with 2-DE analysis. In addition, glial fibrillary acidic protein (GFAP) was also found reduced in adult hippocampus of MD rats. This study identifies candidate proteins encompassing a range of functional categories that may contribute to persistent learning and memory deficits due to early life MD.

Independent replications and integrative analyses confirm TRANK1 as a susceptibility gene for bipolar disorder

Genetic analyses for bipolar disorder (BD) have achieved prominent success in Europeans in recent years, whereas its genetic basis in other populations remains relatively less understood. We herein report that the leading risk locus for BD in European genome-wide association studies (GWAS), the single-nucleotide polymorphism (SNP) rs9834970 near TRANK1 at 3p22 region, is also genome-wide significantly associated with BD in a meta-analysis of four independent East Asian samples including 5748 cases and 65,361 controls (p = 2.27 × 10^-8, odds ratio = 1.136). Expression quantitative trait loci (eQTL) analyses and summary data-based Mendelian randomization (SMR) analyses in multiple human brain samples suggest that lower TRANK1 mRNA expression is a principal BD risk factor explaining its genetic risk signals at 3p22. We also identified another SNP rs4789 in the 3' untranslated region (3'UTR) of TRANK1 showing stronger eQTL associations as well as genome-wide significant association with BD. Despite the relatively unclear neuronal function of TRANK1, our mRNA expression analyses in the human brains and in rat primary cortical neurons reveal that genes highly correlated with TRANK1 are significantly enriched in the biological processes related to dendritic spine, synaptic plasticity, axon guidance and circadian entrainment, and are also more likely to exhibit strong associations in psychiatric GWAS (e.g., the CACNA1C gene). Overall, our results support that TRANK1 is a potential BD risk gene. Further studies elucidating its roles in this illness are needed.

Whole genome analyses reveal significant convergence in obsessive-compulsive disorder between humans and dogs

Obsessive-compulsive disorder (OCD) represents a heterogeneous collection of diseases with diverse levels of phenotypic, genetic, and etiologic variability, making it difficult to identify the underlying genetic and biological mechanisms in humans. Domestic dogs exhibit several OCD-like behaviors. Using continuous circling as a representative phenotype for OCD, we screened two independent dog breeds, the Belgian Malinois and Kunming Dog and subsequently sequenced ten circling dogs and ten unaffected dogs for each breed. Using population differentiation analyses, we identified 11 candidate genes in the extreme tail of the differentiated regions between cases and controls. These genes overlap significantly with genes identified in a genome wide association study (GWAS) of human OCD, indicating strong convergence between humans and dogs. Through gene expressional analysis and functional exploration, we found that two candidate OCD risk genes, PPP2R2B and ADAMTSL3, affected the density and morphology of dendritic spines. Therefore, changes in dendritic spine may underlie some common biological and physiological pathways shared between humans and dogs. Our study revealed an unprecedented level of convergence in OCD shared between humans and dogs, and highlighted the importance of using domestic dogs as a model species for many human diseases including OCD.

Investigating aberrantly expressed microRNAs in peripheral blood mononuclear cells from patients with treatment‑resistant schizophrenia using miRNA sequencing and integrated bioinformatics

Treatment-resistant schizophrenia (TRS) is a common phenotype of schizophrenia that places a considerable burden on patients as well as on society. TRS is known for its tendency to relapse and uncontrollable nature, with a poor response to antipsychotics other than clozapine. Therefore, it is urgent to identify objective biological markers, so as to guide its treatment and associated clinical work. In the present study, the peripheral blood mononuclear cells (PBMCs) of patients with TRS and a healthy control group, which were gender-, age- and ethnicity-matched, were subjected to microRNA (miRNA/miR) sequencing to screen out the top three miRNAs with the highest fold change values. These were then validated in the TRS (n=34) and healthy control (n=31) groups by reverse transcription-quantitative PCR. For two of the top three miRNAs, the PCR results were in accordance with the sequencing result (P<0.01), while the third miRNA exhibited the opposite trend (P<0.01). To elucidate the functions of these two miRNAs, Homo sapiens (hsa)-miR-218-5p and hsa-miR-1262 and their regulatory network, target gene prediction was first performed using online TargetScan and Diana-micro T software. Bioinformatics analysis was then performed using functional enrichment analysis to determine the Gene Ontology terms in the category biological process and the Kyoto Encyclopedia of Genes and Genomes pathways. It was revealed that these target genes were markedly associated with the nervous system and brain function, and it was obvious that the differentially expressed miRNAs most likely participated in the pathogenesis of TRS. A receiver operating characteristic curve was generated to confirm the distinct diagnostic value of these two miRNAs. It was concluded that aberrantly expressed miRNAs in PMBCs may be implicated in the pathogenesis of TRS and may serve as specific peripheral blood-based biomarkers for the early diagnosis of TRS.

Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology

BACKGROUND

Bipolar disorder (BD), a common kind of mood disorder with frequent recurrence, high rates of additional comorbid conditions and poor compliance, has an unclear pathogenesis. The Gene Expression Omnibus (GEO) database is a gene expression database created and maintained by the National Center for Biotechnology Information. Researchers can download expression data online for bioinformatics analysis, especially for cancer research. However, there is little research on the use of such bioinformatics analysis methodologies for mental illness by downloading differential expression data from the GEO database.

METHODS

Publicly available data were downloaded from the GEO database (GSE12649, GSE5388 and GSE5389), and differentially expressed genes (DEGs) were extracted by using the online tool GEO2R. A Venn diagram was used to screen out common DEGs between postmortem brain tissues and normal tissues. Functional annotation and pathway enrichment analysis of DEGs were performed by using Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses, respectively. Furthermore, a protein-protein interaction network was constructed to identify hub genes.

RESULTS

A total of 289 DEGs were found, among which 5 of 10 hub genes [HSP90AA1, HSP90AB 1, UBE2N, UBE3A, and CUL1] were identified as susceptibility genes whose expression was downregulated. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that variations in these 5 hub genes were obviously enriched in protein folding, protein polyubiquitination, apoptotic process, protein binding, the ubiquitin-mediated proteolysis pathway, and protein processing in the endoplasmic reticulum pathway. These findings strongly suggested that HSP90AA1, UBE3A, and CUL 1, which had large areas under the curve in receiver operator curves (P < .05), were potential diagnostic markers for BD.

CONCLUSION

Although there are 3 hub genes [HSP90AA1, UBE3A, and CUL 1] that are tightly correlated with the occurrence of BD, mainly based on routine bioinformatics methods for cancer-related disease, the feasibility of applying this single GEO bioinformatics approach for mental illness is questionable, given the significant differences between mental illness and cancer-related diseases.

Further confirmation of netrin 1 receptor (DCC) as a depression risk gene via integrations of multi-omics data

Genome-wide association studies (GWAS) of major depression and its relevant biological phenotypes have been extensively conducted in large samples, and transcriptome-wide analyses in the tissues of brain regions relevant to pathogenesis of depression, e.g., dorsolateral prefrontal cortex (DLPFC), have also been widely performed recently. Integrating these multi-omics data will enable unveiling of depression risk genes and even underlying pathological mechanisms. Here, we employ summary data-based Mendelian randomization (SMR) and integrative risk gene selector (iRIGS) approaches to integrate multi-omics data from GWAS, DLPFC expression quantitative trait loci (eQTL) analyses and enhancer-promoter physical link studies to prioritize high-confidence risk genes for depression, followed by independent replications across distinct populations. These integrative analyses identify multiple high-confidence depression risk genes, and numerous lines of evidence supporting pivotal roles of the netrin 1 receptor (DCC) gene in this illness across different populations. Our subsequent explorative analyses further suggest that DCC significantly predicts neuroticism, well-being spectrum, cognitive function and putamen structure in general populations. Gene expression correlation and pathway analyses in DLPFC further show that DCC potentially participates in the biological processes and pathways underlying synaptic plasticity, axon guidance, circadian entrainment, as well as learning and long-term potentiation. These results are in agreement with the recent findings of this gene in neurodevelopment and psychiatric disorders, and we thus further confirm that DCC is an important susceptibility gene for depression, and might be a potential target for new antidepressants.

The genome-wide risk alleles for psychiatric disorders at 3p21.1 show convergent effects on mRNA expression, cognitive function, and mushroom dendritic spine

Schizophrenia and bipolar disorder (BPD) are believed to share clinical features, etiological factors, and disease pathologies (such as impaired cognitive functions and dendritic spine pathology). Meanwhile, there is growing evidence of shared genetic risk between schizophrenia and BPD, despite that our knowledge of the functional risk variations and biological mechanisms is still limited. Here, we conduct summary data-based Mendelian randomization (SMR) analyses through combining the statistical data from genome-wide association studies (GWAS) of both schizophrenia and BPD and multiple expression quantitative trait loci (eQTL) datasets of the human brain dorsolateral prefrontal cortex (DLPFC) tissues. These integrative investigations identify a lead risk locus at the chromosome 3p21.1 region, which contains numerous single-nucleotide polymorphisms (SNPs) in varied linkage disequilibrium (LD) and encompasses more than 20 genes. Further analyses suggest that many SNPs at 3p21.1 are significantly associated with both schizophrenia and BPD, and even depression, and the psychiatric risk alleles at 3p21.1 are correlated with mRNA expression of multiple genes such as NEK4, GNL3, and PBRM1. We also identify a 335-bp functional Alu polymorphism rs71052682 in significant LD with the psychiatric GWAS risk SNP rs2251219, and confirm the regulatory effects of this Alu polymorphism on transcription activities. We then explore the involvement of the 3p21.1 locus in the common clinical features and etiology of these illnesses. We reveal that psychiatric risk alleles at 3p21.1 in low-to-high LD consistently predict worse cognitive functions in humans, and manipulating the gene expression (NEK4, GNL3, and PBRM1) linked with higher genetic risk could reduce the density of mushroom dendritic spines in rat primary cortical neurons, mirroring the spine pathology in the prefrontal cortex of psychiatric patients. Our results find that, although the risk alleles at 3p21.1 are in low-to-moderate LD spanning a large genomic area, their underlying biological mechanisms in psychiatric disorders likely converge. These results provide essential insights into the neural mechanisms underlying the chromosome 3p21.1 risk locus in the shared pathological and etiological features of both schizophrenia and BPD.

Peripheral blood metabolome predicts mood change-related activity in mouse model of bipolar disorder

Bipolar disorder is a major mental illness characterized by severe swings in mood and activity levels which occur with variable amplitude and frequency. Attempts have been made to identify mood states and biological features associated with mood changes to compensate for current clinical diagnosis, which is mainly based on patients' subjective reports. Here, we used infradian (a cycle > 24 h) cyclic locomotor activity in a mouse model useful for the study of bipolar disorder as a proxy for mood changes. We show that metabolome patterns in peripheral blood could retrospectively predict the locomotor activity levels. We longitudinally monitored locomotor activity in the home cage, and subsequently collected peripheral blood and performed metabolomic analyses. We then constructed cross-validated linear regression models based on blood metabolome patterns to predict locomotor activity levels of individual mice. Our analysis revealed a significant correlation between actual and predicted activity levels, indicative of successful predictions. Pathway analysis of metabolites used for successful predictions showed enrichment in mitochondria metabolism-related terms, such as "Warburg effect" and "citric acid cycle." In addition, we found that peripheral blood metabolome patterns predicted expression levels of genes implicated in bipolar disorder in the hippocampus, a brain region responsible for mood regulation, suggesting that the brain-periphery axis is related to mood-change-associated behaviors. Our results may serve as a basis for predicting individual mood states through blood metabolomics in bipolar disorder and other mood disorders and may provide potential insight into systemic metabolic activity in relation to mood changes.