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The Monoamine Oxidase-A (MAOA) EcoRV polymorphism (rs1137070) is a unique synonymous mutation (c.1409 T > C) within the MAOA gene, which plays a crucial role in Maoa gene expression and function. This study aimed to explore the relationship between the mouse Maoa rs1137070 genotype and differences in MAOA gene expression. Mice carrying the CC genotype of rs1137070 exhibited a significantly lower Maoa expression level, with an odds ratio of 2.44 compared to the T carriers. Moreover, the wild-type TT genotype of MAOA demonstrated elevated mRNA expression and a longer half-life. We also delved into the significant expression and structural disparities among genotypes. Furthermore, it was evident that different aspartic acid synonymous codons within Maoa influenced both MAOA expression and enzyme activity, highlighting the association between rs1137070 and MAOA. To substantiate these findings, a dual-luciferase reporter assay confirmed that GAC was more efficient than GAT binding. Conversely, the synonymous mutation altered Maoa gene expression in individual mice. An RNA pull-down assay suggested that this alteration could impact the interaction with RNA-binding proteins. In summary, our results illustrate that synonymous mutations can indeed regulate the downregulation of gene expression, leading to changes in MAOA function and their potential association with neurological-related diseases.

BackgroundSmoking is a risk factor for a wide range of diseases. Previous research has confirmed over 30 Smoking-Associated Diseases in diverse systems. There is limited research exploring the correlation among multiple diseases, with an absence of comprehensive investigations. Few studies concentrate on diseases exhibiting a negative correlation with smoking, wherein smokers demonstrate a lower prevalence.ObjectiveThis study aimed to detect the correlation between smoking and other diseases using data from National Health and Nutrition Examination Surveys (NHANES) and construct a Smoking-Diseases Correlation Database (SDCD). The second aim is to obtain an extensive screening test for diseases that may be linked to smoking.Methods39,126 subjects’ data from the NHANES 2013–2018 dataset were extracted. The baseline information, difference in blood routine and blood chemistry indicators between smokers and non-smokers, and diseases’ correlation with smoking in four different models were analyzed by R. The data and statistics were aggregated into an online SDCD.ResultsOur study reported 46 Smoking-Associated Diseases (SAD), including 29 Smoking Positively Associated Diseases (SPAD) and 17 Smoking Negatively Associated Diseases (SNAD). The SDCD of 422 diseases was constructed and can be accessed at https://chatgptmodel.shinyapps.io/sdcd/.ConclusionOur findings revealed 46 SADs including 29 SPADs and 17 SNADs. We aggregated the statistics and developed online SDCD, advancing our understanding of the correlation between smoking and diseases.