Genetic Variations of DAOA (rs947267 and rs3918342) and COMT Genes (rs165599 and rs4680) in Schizophrenia and Bipolar I Disorder

Introduction:

Genetic and environmental factors are involved in the incidence of schizophrenia and bipolar disorder. Many reports confirm that several common genes are connected with these two psychotic disorders. Several neurotransmitters may be involved in the molecular mechanisms of schizophrenia and bipolar disorder. We aimed to estimate the role of two talent genes: DAOA in neurotransmission of glutamate and COMT in neurotransmission of dopamine to guide the treatment of schizophrenia and bipolar disorder.

Methods:

Blood samples (n=100 for schizophrenia, n=100 for bipolar I disorder and n=127 for case control) were collected from individuals unrelated in the southwest of Iran. The SNPs (rs947267 and rs3918342 for DAOA gene/rs165599 and rs4680 for COMT gene) were genotyped using the PCR-RFLP method. Our finding was studied by logistic regression and Mantel-Haenszel Chi-square tests.

Results:

We observed an association in rs3918342, rs165599 and rs4680 single nucleotide polymorphisms and schizophrenia and bipolar I disorder. In addition, our data demonstrated that the rs947267 was related to bipolar I disorder but there was no association between this SNP and schizophrenia.

Conclusion:

In conclusion, this result supports the hypothesis that variations in DAOA and COMT genes may play a role in schizophrenia and bipolar disorder.

Combination of G72 Genetic Variation and G72 Protein Level to Detect Schizophrenia: Machine Learning Approaches

The D-amino acid oxidase activator (DAOA, also known as G72) gene is a strong schizophrenia susceptibility gene. Higher G72 protein levels have been implicated in patients with schizophrenia. The current study aimed to differentiate patients with schizophrenia from healthy individuals using G72 single nucleotide polymorphisms (SNPs) and G72 protein levels by leveraging computational artificial intelligence and machine learning tools. A total of 149 subjects with 89 patients with schizophrenia and 60 healthy controls were recruited. Two G72 genotypes (including rs1421292 and rs2391191) and G72 protein levels were measured with the peripheral blood. We utilized three machine learning algorithms (including logistic regression, naive Bayes, and C4.5 decision tree) to build the optimal predictive model for distinguishing schizophrenia patients from healthy controls. The naive Bayes model using two factors, including G72 rs1421292 and G72 protein, appeared to be the best model for disease susceptibility (sensitivity = 0.7969, specificity = 0.9372, area under the receiver operating characteristic curve (AUC) = 0.9356). However, a model integrating G72 rs1421292 only slightly increased the discriminative power than a model with G72 protein alone (sensitivity = 0.7941, specificity = 0.9503, AUC = 0.9324). Among the three models with G72 protein alone, the naive Bayes with G72 protein alone had the best specificity (0.9503), while logistic regression with G72 protein alone was the most sensitive (0.8765). The findings remained similar after adjusting for age and gender. This study suggests that G72 protein alone, without incorporating the two G72 SNPs, may have been suitable enough to identify schizophrenia patients. We also recommend applying both naive Bayes and logistic regression models for the best specificity and sensitivity, respectively. Larger-scale studies are warranted to confirm the findings.

Adaptive evolution and elucidating the potential inhibitor against schizophrenia to target DAOA (G72) isoforms

Schizophrenia (SZ), a chronic mental and heritable disorder characterized by neurophysiological impairment and neuropsychological abnormalities, is strongly associated with D-amino acid oxidase activator (DAOA, G72). Research studies emphasized that overexpression of DAOA may be responsible for improper functioning of neurotransmitters, resulting in neurological disorders like SZ. In the present study, a hybrid approach of comparative modeling and molecular docking followed by inhibitor identification and structure modeling was employed. Screening was performed by two-dimensional similarity search against selected inhibitor, keeping in view the physiochemical properties of the inhibitor. Here, we report an inhibitor compound which showed maximum binding affinity against four selected isoforms of DAOA. Docking studies revealed that Glu-53, Thr-54, Lys-58, Val-85, Ser-86, Tyr-87, Leu-88, Glu-90, Leu-95, Val-98, Ser-100, Glu-112, Tyr-116, Lys-120, Asp-121, and Arg-122 are critical residues for receptor–ligand interaction. The C-terminal of selected isoforms is conserved, and binding was observed on the conserved region of isoforms. We propose that selected inhibitor might be more potent on the basis of binding energy values. Further analysis of this inhibitor through site-directed mutagenesis could be helpful for exploring the details of ligand-binding pockets. Overall, the findings of this study may be helpful in designing novel therapeutic targets to cure SZ.

Genetic analysis of common variants in the CMYA5 (cardiomyopathy-associated 5) gene with schizophrenia

Recently, CMYA5 was suggested as a susceptibility gene for schizophrenia based on two independent studies utilizing different ethnic samples. We designed a case-control study to examine whether 21 SNPs contained within CMYA5 were associated with the disorder in a western Han Chinese sample comprised of 488 schizophrenia patients and 516 healthy control subjects. The allele distribution of SNPs rs7714250, rs16877135 and rs13158477 showed significant association with schizophrenia (Puncorrected=0.008, Puncorrected=0.04, and Puncorrected=0.009, respectively) as well as the genotype distribution in the Cochran-Armitage trend test (Puncorrected=0.008, Puncorrected=0.037 and Puncorrected=0.011, respectively). After Bonferroni correction, rs7714250 showed a trend of association with schizophrenia both in allele distribution (Pcorrected=0.088) and genotype distribution (Pcorrected=0.088). Furthermore, significant associations were found in several two-, three-, four-, and five-SNP tests of haplotype analyses. Replications of the association of CMYA5 with schizophrenia across various studies suggest that it is very likely a potential common schizophrenia-related gene worldwide. Functional studies correlating CMYA5 with DTNBP1 and PKA warrant further investigation of the molecular basis of this gene in relationship to the signal transduction pathway(s) underlying the pathogenesis of schizophrenia.