MK4MDD: a multi-level knowledge base and analysis platform for major depressive disorder

ICBrainDB: An Integrated Database for Finding Associations between Genetic Factors and EEG Markers of Depressive Disorders

In this study, we collected and systemized diverse information related to depressive and anxiety disorders as the first step on the way to investigate the associations between molecular genetics, electrophysiological, behavioral, and psychological characteristics of people. Keeping that in mind, we developed an internet resource including a database and tools for primary presentation of the collected data of genetic factors, the results of electroencephalography (EEG) tests, and psychological questionnaires. The sample of our study was 1010 people from different regions of Russia. We created the integrated ICBrainDB database that enables users to easily access, download, and further process information about individual behavioral characteristics and psychophysiological responses along with inherited trait data. The data obtained can be useful in training neural networks and in machine learning construction processes in Big Data analysis. We believe that the existence of such a resource will play an important role in the further search for associations of genetic factors and EEG markers of depression.

An integrative network analysis framework for identifying molecular functions in complex disorders examining major depressive disorder as a test case

In addition to the psychological depressive phenotype, major depressive disorder (MDD) patients are also associated with underlying immune dysregulation that correlates with metabolic syndrome prevalent in depressive patients. A robust integrative analysis of biological pathways underlying the dysregulated neural connectivity and systemic inflammatory response will provide implications in the development of effective strategies for the diagnosis, management and the alleviation of associated comorbidities. In the current study, focusing on MDD, we explored an integrative network analysis methodology to analyze transcriptomic data combined with the meta-analysis of biomarker data available throughout public databases and published scientific peer-reviewed articles. Detailed gene set enrichment analysis and complex protein–protein, gene regulatory and biochemical pathway analysis has been undertaken to identify the functional significance and potential biomarker utility of differentially regulated genes, proteins and metabolite markers. This integrative analysis method provides insights into the molecular mechanisms along with key glycosylation dysregulation underlying altered neutrophil-platelet activation and dysregulated neuronal survival maintenance and synaptic functioning. Highlighting the significant gap that exists in the current literature, the network analysis framework proposed reduces the impact of data gaps and permits the identification of key molecular signatures underlying complex disorders with multiple etiologies such as within MDD and presents multiple treatment options to address their molecular dysfunction.

Sex differences in the genetic architecture of depression

The prevalence and clinical characteristics of depressive disorders differ between women and men; however, the genetic contribution to sex differences in depressive disorders has not been elucidated. To evaluate sex-specific differences in the genetic architecture of depression, whole exome sequencing of samples from 1000 patients (70.7% female) with depressive disorder was conducted. Control data from healthy individuals with no psychiatric disorder (n = 72, 26.4% female) and East-Asian subpopulation 1000 Genome Project data (n = 207, 50.7% female) were included. The genetic variation between men and women was directly compared using both qualitative and quantitative research designs. Qualitative analysis identified five genetic markers potentially associated with increased risk of depressive disorder in females, including three variants (rs201432982 within PDE4A, and rs62640397 and rs79442975 within FDX1L) mapping to chromosome 19p13.2 and two novel variants (rs820182 and rs820148) within MYO15B at the chromosome 17p25.1 locus. Depressed patients homozygous for these variants showed more severe depressive symptoms and higher suicidality than those who were not homozygotes (i.e., heterozygotes and homozygotes for the non-associated allele). Quantitative analysis demonstrated that the genetic burden of protein-truncating and deleterious variants was higher in males than females, even after permutation testing. Our study provides novel genetic evidence that the higher prevalence of depressive disorders in women may be attributable to inherited variants.

Transcriptional consequences of MBD5 disruption in mouse brain and CRISPR-derived neurons

BACKGROUND

MBD5, encoding the methyl-CpG-binding domain 5 protein, has been proposed as a necessary and sufficient driver of the 2q23.1 microdeletion syndrome. De novo missense and protein-truncating variants from exome sequencing studies have directly implicated MBD5 in the etiology of autism spectrum disorder (ASD) and related neurodevelopmental disorders (NDDs). However, little is known concerning the specific function(s) of MBD5.

METHODS

To gain insight into the complex interactions associated with alteration of MBD5 in individuals with ASD and related NDDs, we explored the transcriptional landscape of MBD5 haploinsufficiency across multiple mouse brain regions of a heterozygous hypomorphic Mbd5^+/GT mouse model, and compared these results to CRISPR-mediated mutations of MBD5 in human iPSC-derived neuronal models.

RESULTS

Gene expression analyses across three brain regions from Mbd5^+/GT mice showed subtle transcriptional changes, with cortex displaying the most widespread changes following Mbd5 reduction, indicating context-dependent effects. Comparison with MBD5 reduction in human neuronal cells reinforced the context-dependence of gene expression changes due to MBD5 deficiency. Gene co-expression network analyses revealed gene clusters that were associated with reduced MBD5 expression and enriched for terms related to ciliary function.

LIMITATIONS

These analyses included a limited number of mouse brain regions and neuronal models, and the effects of the gene knockdown are subtle. As such, these results will not reflect the full extent of MBD5 disruption across human brain regions during early neurodevelopment in ASD, or capture the diverse spectrum of cell-type-specific changes associated with MBD5 alterations.

CONCLUSIONS

Our study points to modest and context-dependent transcriptional consequences of Mbd5 disruption in the brain. It also suggests a possible link between MBD5 and perturbations in ciliary function, which is an established pathogenic mechanism in developmental disorders and syndromes.

Analyzing the genes and pathways related to major depressive disorder via a systems biology approach

INTRODUCTION

Major depressive disorder (MDD) is a mental disorder caused by the combination of genetic, environmental, and psychological factors. Over the years, a number of genes potentially associated with MDD have been identified. However, in many cases, the role of these genes and their relationship in the etiology and development of MDD remains unclear. Under such situation, a systems biology approach focusing on the function correlation and interaction of the candidate genes in the context of MDD will provide useful information on exploring the molecular mechanisms underlying the disease.

METHODS

We collected genes potentially related to MDD by screening the human genetic studies deposited in PubMed (https://www.ncbi.nlm.nih.gov/pubmed). The main biological themes within the genes were explored by function and pathway enrichment analysis. Then, the interaction of genes was analyzed in the context of protein-protein interaction network and a MDD-specific network was built by Steiner minimal tree algorithm.

RESULTS

We collected 255 candidate genes reported to be associated with MDD from available publications. Functional analysis revealed that biological processes and biochemical pathways related to neuronal development, endocrine, cell growth and/or survivals, and immunology were enriched in these genes. The pathways could be largely grouped into three modules involved in biological procedures related to nervous system, the immune system, and the endocrine system, respectively. From the MDD-specific network, 35 novel genes potentially associated with the disease were identified.

CONCLUSION

By means of network- and pathway-based methods, we explored the molecular mechanism underlying the pathogenesis of MDD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular features of MDD.

Reconstruction and Analysis of Gene Networks of Human Neurotransmitter Systems Reveal Genes with Contentious Manifestation for Anxiety, Depression, and Intellectual Disabilities

Background: The study of the biological basis of anxiety, depression, and intellectual disabilities in humans is one of the most actual problems of modern neurophysiology. Of particular interest is the study of complex interactions between molecular genetic factors, electrophysiological properties of the nervous system, and the behavioral characteristics of people. The neurobiological understanding of neuropsychiatric disorders requires not only the identification of genes that play a role in the molecular mechanisms of the occurrence and course of diseases, but also the understanding of complex interactions that occur between these genes. A systematic study of such interactions obviously contributes to the development of new methods of diagnosis, prevention, and treatment of disorders, as the orientation to allele variants of individual loci is not reliable enough, because the literature describes a number of genes, the same alleles of which can be associated with different, sometimes extremely different variants of phenotypic traits, depending on the genetic background, of their carriers, habitat, and other factors. Results: In our study, we have reconstructed a series of gene networks (in the form of protein–protein interactions networks, as well as networks of transcription regulation) to build a model of the influence of complex interactions of environmental factors and genetic risk factors for intellectual disability, depression, and other disorders in human behavior. Conclusion: A list of candidate genes whose expression is presumably associated with environmental factors and has potentially contentious manifestation for behavioral and neurological traits is identified for further experimental verification.

A new locus regulating MICALL2 expression was identified for association with executive inhibition in children with attention deficit hyperactivity disorder

Impaired executive inhibition is a core deficit of attention deficit hyperactivity disorder (ADHD), which is a common childhood-onset psychiatric disorder with high heritability. In this study, we performed a two-stage genome-wide association study of executive inhibition in ADHD in Han Chinese. We used the Stroop color-word interference test to evaluate executive inhibition. After quality control, 780 samples with phenotype and covariate data were included in the discovery stage, whereas 922 samples were included in the replication stage. We identified one new significant locus at 7p22.3 for the Stroop word interference time (rs11514810, P=3.42E-09 for discovery, P=0.01176 for replication and combined P=5.249E-09). Regulatory feature analysis and expression quantitative trait loci (eQTL) data showed that this locus contributes to MICALL2 expression in the human brain. Most genes in the network interacting with MICALL2 were associated with psychiatric disorders. Furthermore, hyperactive-impulsive-like behavior was induced by reducing the expression of the zebrafish gene that is homologous to MICALL2, which could be rescued by tomoxetine (atomoxetine), a clinical medication for ADHD. Our results suggested that MICALL2 is a new susceptibility gene for executive inhibition deficiency related to hyperactive-impulsive behavior in ADHD, further emphasizing the possible role of neurodevelopmental genes in the pathogenic mechanism of ADHD.

Antidepressants Improve Negative Regulation of Toll-Like Receptor Signaling in Monocytes from Patients with Major Depression

OBJECTIVE

Changes in the brain's inflammatory status can lead to psychopathological responses, especially depression. Using animal models, recent studies have revealed that this pathology is due, in part, to innate immune responses of monocytes.

METHODS

We focus on the involvement of Toll-like receptors (TLRs) and expression of genes encoding their negative regulators, SOCS1, TOLLIP, SIGIRR, MyD88s, NOD2, and TNFAIP3, in CD14+ monocytes from 34 patients with major depressive disorder (MDD) and 33 healthy controls before and after treatment with antidepressants. We also seek to investigate their association with depression severity, measured by the 17-item Hamilton Depression Rating Scale (HAMD-17).

RESULTS

mRNA expression of all TLRs, except TLR3 and -5, was significantly higher in monocytes from patients with MDD than in those from controls. Conversely, the "brakes" in TLR signaling, including TOLLIP, MyD88s, NOD2, and TNFAIP3, were downregulated. In clinical findings, the remission group showed higher baseline TLR4 and lower baseline IRAK3 mRNA levels but only baseline elevated SOCS1 mRNA levels, which were inversely correlated with HAMD-17 scores, predicting worsened outcome in MDD patients. In addition, TNFAIP3 mRNA levels were increased by antidepressant treatment.

CONCLUSION

Collectively, our findings suggest a role for dysregulation of TLR signaling in monocytes in MDD and identify a balancing effect of antidepressants on this dysregulation.

Network analysis reveals a stress-affected common gene module among seven stress-related diseases/systems which provides potential targets for mechanism research

Chronic stress (CS) was reported to associate with many complex diseases and stress-related diseases show strong comorbidity; however, molecular analyses have not been performed to date to evaluate common stress-induced biological processes across these diseases. We utilized networks constructed by genes from seven genetic databases of stress-related diseases or systems to explore the common mechanisms. Genes were connected based on the interaction information of proteins they encode. A common sub-network constructed by 561 overlapping genes and 8863 overlapping edges among seven networks was identified and it provides a common gene module among seven stress-related diseases/systems. This module is significantly overlapped with network that constructed by genes from the CS gene database. 36 genes with high connectivity (hub genes) were identified from seven networks as potential key genes in those diseases/systems, 33 of hub genes were included in the common module. Genes in the common module were enriched in 190 interactive gene ontology (GO) functional clusters which provide potential disease mechanism. In conclusion, by analyzing gene networks we revealed a stress-affected common gene module among seven stress-related diseases/systems which provides insight into the process of stress induction of disease and suggests potential gene and pathway candidates for further research.

Applying differentially expressed genes from rodent models of chronic stress to research of stress-related disease: an online database

OBJECTIVE

To systematically collect differentially expressed genes (DEGs) from rodent models of chronic stress (CS) and apply them to research of stress-related disease. CS is an important environmental factor that may affect numerous complex diseases. Its relevant DEGs identified from rodent models provide valuable information for understanding the mechanisms underlying stress-related diseases. Currently, no suitable data tool have been developed to use such data.

METHODS

We systematically searched and reviewed publications in PubMed. CS-DEGs were collected from original studies that reported gene expression statuses in rodent models of CS. CS disease overlapping genes, CS pathways and CS pathway clusters, and CS regulatory elements were analyzed on the basis of CS-DEGs. An online database was developed to store and manage curated CS-DEGs and analyzed data.

RESULTS

A total of 2956 CS-DEGs were collected from 195 articles, among which 815 genes are shared among CS and seven stress-related diseases. Nine hundred twenty-seven CS pathway clusters were identified. Three types of CS regulatory elements are predicted for all CS genes. An online database (CS-DEGs), freely available at http://cs.psych.ac.cn, includes and presents CS-DEGs and all analyzed data.

CONCLUSIONS

CS-DEGs is the first gene database on CS research. It enables researchers to apply rodent expression data in candidate gene and pathway identification for stress-related disease study.

BDgene: a genetic database for bipolar disorder and its overlap with schizophrenia and major depressive disorder

BACKGROUND

Bipolar disorder (BD) is a common psychiatric disorder with complex genetic architecture. It shares overlapping genetic influences with schizophrenia (SZ) and major depressive disorder (MDD). Large numbers of genetic studies of BD and cross-disorder studies between BD and SZ/MDD have accumulated numerous genetic data. There is a growing need to integrate the data to provide a comprehensive data set to facilitate the genetic study of BD and its highly relevant diseases.

METHODS

BDgene database was developed to integrate BD-related genetic factors and shared ones with SZ/MDD from profound literature reading. On the basis of data from the literature, in-depth analyses were performed for further understanding of the data, including gene prioritization, pathway-based analysis, intersection analysis of multidisease candidate genes, and pathway enrichment analysis.

RESULTS

BDgene includes multiple types of literature-reported genetic factors of BD with both positive and negative results, including 797 genes, 3119 single nucleotide polymorphisms, and 789 regions. Shared genetic factors such as single nucleotide polymorphisms, genes, and regions from published cross-disorder studies among BD and SZ/MDD were also presented. In-depth data analyses identified 43 BD core genes; 70 BD candidate pathways; and 127, 79, and 107 new potential cross-disorder genes for BD-SZ, BD-MDD, and BD-SZ-MDD, respectively.

CONCLUSIONS

As a central genetic database for BD and the first cross-disorder database for BD and SZ/MDD, BDgene provides not only a comprehensive review of current genetic research but also high-confidence candidate genes and pathways for understanding of BD mechanism and shared etiology among its relevant diseases. BDgene is freely available at http://bdgene.psych.ac.cn.