Genomic Chaos Begets Psychiatric Disorder

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.

Application of per-Residue Energy Decomposition to Design Peptide Inhibitors of PSD95 GK Domain

Specific interaction between the postsynaptic density protein 95 (PSD95) and synapse-associated protein 90/postsynaptic density 95–associated protein (SAPAP) is crucial for excitatory synaptic development and plasticity. Designing inhibitors that target the guanylate kinase (GK) domain of PSD95, which is responsible for the interaction, is a promising manipulation tool for the investigation of the function of PSD95 GK and the etiology of its related psychiatric disorders. Herein, we designed new peptide inhibitors of PSD95 GK/SAPAP with higher binding affinity by using molecular dynamics simulations. First, the interactions between PSD95 GK and their reported phosphorylated and unphosphorylated peptides were explored by molecular dynamics simulations. Besides the hydrogen bonding interactions mediated by the phospho-serine (p-Ser) or corresponding phosphomimic residue Asp/Glu, the hydrophobic interactions from the other amino acids also contribute to the PSD95 GK/SAPAP interaction. As an unphosphorylated synthetic peptide with moderate binding affinity and relatively lower molecular weight, the QSF inhibitory peptide was selected for further modification. Based on per-residue energy decomposition results of the PSD95 GK/QSF complex, ten peptides were designed to enhance the binding interactions, especially the hydrophobic interactions. The top-ranked five peptides with lower binding energy were eventually synthesized. The binding affinities of the synthesized peptides were determined using fluorescence polarization (FP) assay. As expected, all peptides have higher binding affinity than the QSF peptide (Ki = 5.64 ± 0.51 μM). Among them, F10W was the most potent inhibitor (Ki = 0.75 ± 0.25 μM), suggesting that enhancement of the hydrophobic interactions is an important strategy for the design of new inhibitory peptides targeting PSD95 GK.

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.

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

Major depressive disorder (MDD) affects around 15% of the population at some stage in their lifetime. It can be gravely disabling and it is associated with increased risk of suicide. Genetics play an important role; however, there are additional environmental contributions to the pathogenesis. A number of possible risk genes that increase liability for developing symptoms of MDD have been identified in genome-wide association studies (GWAS). The goal of this study was to characterize the MDD risk genes with respect to the degree of evolutionary conservation in simpler model organisms such as Caenorhabditis elegans and zebrafish, the phenotypes associated with variation in these genes and the extent of network connectivity. The MDD risk genes showed higher conservation in C. elegans and zebrafish than genome-to-genome comparisons. In addition, there were recurring themes among the phenotypes associated with variation of these risk genes in C. elegans. The phenotype analysis revealed enrichment for essential genes with pleiotropic effects. Moreover, the MDD risk genes participated in more interactions with each other than did randomly-selected genes from similar-sized gene sets. Syntenic blocks of risk genes with common functional activities were also identified. By characterizing evolutionarily-conserved counterparts to the MDD risk genes, we have gained new insights into pathogenetic processes relevant to the emergence of depressive symptoms in man.