Analysis of Major Depression Risk Genes Reveals Evolutionary Conservation, Shared Phenotypes, and Extensive Genetic Interactions

Evolutionary conservation of putative suicidality-related risk genes that produce diminished motivation corrected by clozapine, lithium and antidepressants

Background

Genome wide association studies (GWAS) and candidate gene analyses have identified genetic variants and genes that may increase the risk for suicidal thoughts and behaviors (STBs). Important unresolved issues surround these tentative risk variants such as the characteristics of the associated genes and how they might elicit STBs.

Methods

Putative suicidality-related risk genes (PSRGs) were identified by comprehensive literature search and were characterized with respect to evolutionary conservation, participation in gene interaction networks and associated phenotypes. Evolutionary conservation was established with database searches and BLASTP queries, whereas gene-gene interactions were ascertained with GeneMANIA. We then examined whether mutations in risk-gene counterparts in C. elegans produced a diminished motivation phenotype previously connected to suicide risk factors.

Results and conclusions

From the analysis, 105 risk-gene candidates were identified and found to be: 1) highly conserved during evolution, 2) enriched for essential genes, 3) involved in significant gene-gene interactions, and 4) associated with psychiatric disorders, metabolic disturbances and asthma/allergy. Evaluation of 17 mutant strains with loss-of-function/deletion mutations in PSRG orthologs revealed that 11 mutants showed significant evidence of diminished motivation that manifested as immobility in a foraging assay. Immobility was corrected in some or all of the mutants with clozapine, lithium and tricyclic antidepressant drugs. In addition, 5-HT2 receptor and muscarinic receptor antagonists restored goal-directed behavior in most or all of the mutants. These studies increase confidence in the validity of the PSRGs and provide initial clues about possible mechanisms that mediate STBs.

Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes

Genomic studies of molecular traits have provided mechanistic insights into complex disease, though these lag behind for brain-related traits due to the inaccessibility of brain tissue. We leveraged cerebrospinal fluid (CSF) to study neurobiological mechanisms in vivo , measuring 5,543 CSF metabolites, the largest panel in CSF to date, in 977 individuals of European ancestry. Individuals originated from two separate cohorts including cognitively healthy subjects (n=490) and a well-characterized memory clinic sample, the Amsterdam Dementia Cohort (ADC, n=487). We performed metabolite quantitative trait loci (mQTL) mapping on CSF metabolomics and found 126 significant mQTLs, representing 65 unique CSF metabolites across 51 independent loci. To better understand the role of CSF mQTLs in brain-related disorders, we performed a metabolome-wide association study (MWAS), identifying 40 associations between CSF metabolites and brain traits. Similarly, over 90% of significant mQTLs demonstrated colocalized associations with brain-specific gene expression, unveiling potential neurobiological pathways.

Systems biology approach discovers comorbidity interaction of Parkinson's disease with psychiatric disorders utilizing brain transcriptome

Several studies found that most patients with Parkinson's disorder (PD) appear to have psychiatric symptoms such as depression, anxiety, hallucination, delusion, and cognitive dysfunction. Therefore, recognizing these psychiatrically symptoms of PD patients is crucial for both symptomatic therapy and better knowledge of the pathophysiology of PD. In order to address this issue, we created a bioinformatics framework to determine the effects of PD mRNA expression on understanding its relationship with psychiatric symptoms in PD patients. We have discovered a significant overlap between the sets of differentially expressed genes from PD exposed tissue and psychiatric disordered tissues using RNA-seq datasets. We have chosen Bipolar disorder and Schizophrenia as psychiatric disorders in our study. A number of significant correlations between PD and the occurrence of psychiatric diseases were also found by gene set enrichment analysis, investigations of the protein-protein interaction network, gene regulatory network, and protein-chemical agent interaction network. We anticipate that the results of this pathogenetic study will provide crucial information for understanding the intricate relationship between PD and psychiatric diseases.

Multi-context genetic modeling of transcriptional regulation resolves novel disease loci

A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT-a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.

How Variation in Risk Allele Output and Gene Interactions Shape the Genetic Architecture of Schizophrenia

Schizophrenia is a highly heritable polygenic psychiatric disorder. Characterization of its genetic architecture may lead to a better understanding of the overall burden of risk variants and how they determine susceptibility to disease. A major goal of this project is to develop a modeling approach to compare and quantify the relative effects of single nucleotide polymorphisms (SNPs), copy number variants (CNVs) and other factors. We derived a mathematical model for the various genetic contributions based on the probability of expressing a combination of risk variants at a frequency that matched disease prevalence. The model included estimated risk variant allele outputs (VAOs) adjusted for population allele frequency. We hypothesized that schizophrenia risk genes would be more interactive than random genes and we confirmed this relationship. Gene–gene interactions may cause network ripple effects that spread and amplify small individual effects of risk variants. The modeling revealed that the number of risk alleles required to achieve the threshold for susceptibility will be determined by the average functional locus output (FLO) associated with a risk allele, the risk allele frequency (RAF), the number of protective variants present and the extent of gene interactions within and between risk loci. The model can account for the quantitative impact of protective variants as well as CNVs on disease susceptibility. The fact that non-affected individuals must carry a non-trivial burden of risk alleles suggests that genetic susceptibility will inevitably reach the threshold for schizophrenia at a recurring frequency in the population.

Catalytic Reaction Model of Suicide

Suicide is a devastating outcome of unresolved issues that affect mental health, general wellbeing and socioeconomic stress. The biology of suicidal behavior is still poorly understood, although progress has been made. Suicidal behavior runs in families and genetic studies have provided initial glimpses into potential genes that contribute to suicide risk. Here, we attempt to unify the biology and behavioral dimensions into a model that can guide research in this area. The proposed model envisions suicidal behavior as a catalytic reaction that may result in suicide depending on the conditions, analogously to enzyme catalysis of chemical reactions. A wide array of substrates or reactants, such as hopelessness, depression, debilitating illnesses and diminished motivation can mobilize suicidal thoughts and behaviors (STBs), which can then catalyze the final step/act of suicide. Here, we focus on three biological substrates in particular: threat assessment, motivation to engage in life and impulsivity. Genetic risk factors can affect each of these processes and tilt the balance toward suicidal behavior when existential crises (real or perceived) emerge such as loss of a loved one, sudden changes in social status or serious health issues. Although suicide is a uniquely human behavior, many of the fundamental biological processes are evolutionarily conserved. Insights from animal models may help to shape our understanding of suicidal behavior in man. By examining counterparts of the major biological processes in other organisms, new ideas about the role of genetic risk factors may emerge along with possible therapeutic interventions or preventive measures.

Protein Receptors Evolved from Homologous Cohesion Modules That Self-Associated and Are Encoded by Interactive Networked Genes

Previously, it was proposed that protein receptors evolved from self-binding peptides that were encoded by self-interacting gene segments (inverted repeats) widely dispersed in the genome. In addition, self-association of the peptides was thought to be mediated by regions of amino acid sequence similarity. To extend these ideas, special features of receptors have been explored, such as their degree of homology to other proteins, and the arrangement of their genes for clues about their evolutionary origins and dynamics in the genome. As predicted, BLASTP searches for homologous proteins detected a greater number of unique hits for queries with receptor sequences than for sequences of randomly-selected, non-receptor proteins. This suggested that the building blocks (cohesion modules) for receptors were duplicated, dispersed, and maintained in the genome, due to structure/function relationships discussed here. Furthermore, the genes coding for a representative panel of receptors participated in a larger number of gene-gene interactions than for randomly-selected genes. This could conceivably reflect a greater evolutionary conservation of the receptor genes, with their more extensive integration into networks along with inherent properties of the genes themselves. In support of the latter possibility, some receptor genes were located in active areas of adaptive gene relocation/amalgamation to form functional blocks of related genes. It is suggested that adaptive relocation might allow for their joint regulation by common promoters and enhancers, and affect local chromatin structural domains to facilitate or repress gene expression. Speculation is included about the nature of the coordinated communication between receptors and the genes that encode them.