A comprehensive network and pathway analysis of candidate genes in major depressive disorder

<p>Abstract</p> <p>Background</p> <p>Numerous genetic and genomic datasets related to complex diseases have been made available during the last decade. It is now a great challenge to assess such heterogeneous datasets to prioritize disease genes and perform follow up functional analysis and validation. Among complex disease studies, psychiatric disorders such as major depressive disorder (MDD) are especially in need of robust integrative analysis because these diseases are more complex than others, with weak genetic factors at various levels, including genetic markers, transcription (gene expression), epigenetics (methylation), protein, pathways and networks.</p> <p>Results</p> <p>In this study, we proposed a comprehensive analysis framework at the systems level and demonstrated it in MDD using a set of candidate genes that have recently been prioritized based on multiple lines of evidence including association, linkage, gene expression (both human and animal studies), regulatory pathway, and literature search. In the network analysis, we explored the topological characteristics of these genes in the context of the human interactome and compared them with two other complex diseases. The network topological features indicated that MDD is similar to schizophrenia compared to cancer. In the functional analysis, we performed the gene set enrichment analysis for both Gene Ontology categories and canonical pathways. Moreover, we proposed a unique pathway crosstalk approach to examine the dynamic interactions among biological pathways. Our pathway enrichment and crosstalk analyses revealed two unique pathway interaction modules that were significantly enriched with MDD genes. These two modules are neuro-transmission and immune system related, supporting the neuropathology hypothesis of MDD. Finally, we constructed a MDD-specific subnetwork, which recruited novel candidate genes with association signals from a major MDD GWAS dataset.</p> <p>Conclusions</p> <p>This study is the first systematic network and pathway analysis of candidate genes in MDD, providing abundant important information about gene interaction and regulation in a major psychiatric disease. The results suggest potential functional components underlying the molecular mechanisms of MDD and, thus, facilitate generation of novel hypotheses in this disease. The systems biology based strategy in this study can be applied to many other complex diseases.</p

GABA-modulating bacteria of the human gut microbiota.

The gut microbiota affects many important host functions, including the immune response and the nervous system1. However, while substantial progress has been made in growing diverse microorganisms of the microbiota2, 23-65% of species residing in the human gut remain uncultured3,4, which is an obstacle for understanding their biological roles. A likely reason for this unculturability is the absence in artificial media of key growth factors that are provided by neighbouring bacteria in situ5,6. In the present study, we used co-culture to isolate KLE1738, which required the presence of Bacteroides fragilis to grow. Bioassay-driven purification of B. fragilis supernatant led to the isolation of the growth factor, which, surprisingly, is the major inhibitory neurotransmitter GABA (γ-aminobutyric acid). GABA was the only tested nutrient that supported the growth of KLE1738, and a genome analysis supported a GABA-dependent metabolism mechanism. Using growth of KLE1738 as an indicator, we isolated a variety of GABA-producing bacteria, and found that Bacteroides ssp. produced large quantities of GABA. Genome-based metabolic modelling of the human gut microbiota revealed multiple genera with the predicted capability to produce or consume GABA. A transcriptome analysis of human stool samples from healthy individuals showed that GABA-producing pathways are actively expressed by Bacteroides, Parabacteroides and Escherichia species. By coupling 16S ribosmal RNA sequencing with functional magentic resonance imaging in patients with major depressive disorder, a disease associated with an altered GABA-mediated response, we found that the relative abundance levels of faecal Bacteroides are negatively correlated with brain signatures associated with depression

Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

Shared Genetics of Psychiatric Disorders

Until recently, advances in understanding the genetic architecture of psychiatric disorders have been impeded by a historic, and often mandated, commitment to the use of traditional, and unvalidated, categorical diagnoses in isolation as the relevant phenotype. Such studies typically required lengthy structured interviews to delineate differences in the character and duration of behavioral symptomatology amongst disorders that were thought to be etiologic, and they were often underpowered as a result. Increasing acceptance of the fact that co-morbidity in psychiatric disorders is the rule rather than the exception has led to alternative designs in which shared dimensional symptomatology is analyzed as a quantitative trait and to association analyses in which combined polygenic risk scores are computationally compared across multiple traditional categorical diagnoses to identify both distinct and unique genetic and environmental elements. Increasing evidence that most mental disorders share many common genetic risk variants and environmental risk modifiers suggests that the broad spectrum of psychiatric pathology represents the pleiotropic display of a more limited series of pathologic events in neuronal development than was originally believed, regulated by many common risk variants and a smaller number of rare ones

Prioritization and Evaluation of Depression Candidate Genes by Combining Multidimensional Data Resources

Large scale and individual genetic studies have suggested numerous susceptible genes for depression in the past decade without conclusive results. There is a strong need to review and integrate multi-dimensional data for follow up validation. The present study aimed to apply prioritization procedures to build-up an evidence-based candidate genes dataset for depression.Depression candidate genes were collected in human and animal studies across various data resources. Each gene was scored according to its magnitude of evidence related to depression and was multiplied by a source-specific weight to form a combined score measure. All genes were evaluated through a prioritization system to obtain an optimal weight matrix to rank their relative importance with depression using the combined scores. The resulting candidate gene list for depression (DEPgenes) was further evaluated by a genome-wide association (GWA) dataset and microarray gene expression in human tissues.A total of 5,055 candidate genes (4,850 genes from human and 387 genes from animal studies with 182 being overlapped) were included from seven data sources. Through the prioritization procedures, we identified 169 DEPgenes, which exhibited high chance to be associated with depression in GWA dataset (Wilcoxon rank-sum test, p = 0.00005). Additionally, the DEPgenes had a higher percentage to express in human brain or nerve related tissues than non-DEPgenes, supporting the neurotransmitter and neuroplasticity theories in depression.With comprehensive data collection and curation and an application of integrative approach, we successfully generated DEPgenes through an effective gene prioritization system. The prioritized DEPgenes are promising for future biological experiments or replication efforts to discover the underlying molecular mechanisms for depression

The Establishment of the GENEQOL Consortium to Investigate the Genetic Disposition of Patient-Reported Quality-of-Life Outcomes

To our knowledge, no comprehensive, interdisciplinary initiatives have been taken to examine the role of genetic variants on patient-reported quality-of-life outcomes. The overall objective of this paper is to describe the establishment of an international and interdisciplinary consortium, the GENEQOL Consortium, which intends to investigate the genetic disposition of patient-reported quality-of-life outcomes. We have identified five primary patient-reported quality-of-life outcomes as initial targets: negative psychological affect, positive psychological affect, self-rated physical health, pain, and fatigue. The first tangible objective of the GENEQOL Consortium is to develop a list of potential biological pathways, genes and genetic variants involved in these quality-of-life outcomes, by reviewing current genetic knowledge. The second objective is to design a research agenda to investigate and validate those genes and genetic variants of patient-reported quality-of-life outcomes, by creating large datasets. During its first meeting, the Consortium has discussed draft summary documents addressing these questions for each patient-reported quality-of-life outcome. A summary of the primary pathways and robust findings of the genetic variants involved is presented here. The research agenda outlines possible research objectives and approaches to examine these and new quality-of-life domains. Intriguing questions arising from this endeavor are discussed. Insight into the genetic versus environmental components of patient-reported quality-of-life outcomes will ultimately allow us to explore new pathways for improving patient care. If we can identify patients who are susceptible to poor quality of life, we will be able to better target specific clinical interventions to enhance their quality of life and treatment outcomes.quality of life, self-rated health, pain, fatigue, genetic disposition, Patient-Reported Quality-of-Life Outcomes

Association Signals Unveiled by a Comprehensive Gene Set Enrichment Analysis of Dental Caries Genome-Wide Association Studies

Gene set-based analysis of genome-wide association study (GWAS) data has recently emerged as a useful approach to examine the joint effects of multiple risk loci in complex human diseases or phenotypes. Dental caries is a common, chronic, and complex disease leading to a decrease in quality of life worldwide. In this study, we applied the approaches of gene set enrichment analysis to a major dental caries GWAS dataset, which consists of 537 cases and 605 controls. Using four complementary gene set analysis methods, we analyzed 1331 Gene Ontology (GO) terms collected from the Molecular Signatures Database (MSigDB). Setting false discovery rate (FDR) threshold as 0.05, we identified 13 significantly associated GO terms. Additionally, 17 terms were further included as marginally associated because they were top ranked by each method, although their FDR is higher than 0.05. In total, we identified 30 promising GO terms, including 'Sphingoid metabolic process,' 'Ubiquitin protein ligase activity,' 'Regulation of cytokine secretion,' and 'Ceramide metabolic process.' These GO terms encompass broad functions that potentially interact and contribute to the oral immune response related to caries development, which have not been reported in the standard single marker based analysis. Collectively, our gene set enrichment analysis provided complementary insights into the molecular mechanisms and polygenic interactions in dental caries, revealing promising association signals that could not be detected through single marker analysis of GWAS data. © 2013 Wang et al

The genetic overlap between mood disorders and cardiometabolic diseases: a systematic review of genome wide and candidate gene studies

© The Author(s) 2017 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.Meta-analyses of genome-wide association studies (meta-GWASs) and candidate gene studies have identified genetic variants associated with cardiovascular diseases, metabolic diseases and mood disorders. Although previous efforts were successful for individual disease conditions (single disease), limited information exists on shared genetic risk between these disorders. This article presents a detailed review and analysis of cardiometabolic diseases risk (CMD-R) genes that are also associated with mood disorders. First, we reviewed meta-GWASs published until January 2016, for the diseases ‘type 2 diabetes, coronary artery disease, hypertension’ and/or for the risk factors ‘blood pressure, obesity, plasma lipid levels, insulin and glucose related traits’. We then searched the literature for published associations of these CMD-R genes with mood disorders. We considered studies that reported a significant association of at least one of the CMD-R genes and ‘depression’ or ‘depressive disorder’ or ‘depressive symptoms’ or ‘bipolar disorder’ or ‘lithium treatment response in bipolar disorder’, or ‘serotonin reuptake inhibitors treatment response in major depression’. Our review revealed 24 potential pleiotropic genes that are likely to be shared between mood disorders and CMD-Rs. These genes include MTHFR, CACNA1D, CACNB2, GNAS, ADRB1, NCAN, REST, FTO, POMC, BDNF, CREB, ITIH4, LEP, GSK3B, SLC18A1, TLR4, PPP1R1B, APOE, CRY2, HTR1A, ADRA2A, TCF7L2, MTNR1B and IGF1. A pathway analysis of these genes revealed significant pathways: corticotrophin-releasing hormone signaling, AMPK signaling, cAMP-mediated or G-protein coupled receptor signaling, axonal guidance signaling, serotonin or dopamine receptors signaling, dopamine-DARPP32 feedback in cAMP signaling, circadian rhythm signaling and leptin signaling. Our review provides insights into the shared biological mechanisms of mood disorders and cardiometabolic diseases