Unraveling the immunogenetic landscape of autism spectrum disorder: a comprehensive bioinformatics approach.

Background: Autism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present. Methods: The ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene. Results: We found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis. Conclusions: In conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.

Intestinal metabolites and the risk of autistic spectrum disorder: A two-sample Mendelian randomization study.

Background: Observational studies have reported a strong association between autistic spectrum disorder (ASD) and intestinal metabolites. However, it is unclear whether this correlation is causally or violated by confounding or backward causality. Therefore, this study explored the potential causal relationship between intestinal metabolites and dependent metabolites on ASD. Methods: We used a two-sample Mendelian random analysis and selected variants closely related to intestinal flora-dependent metabolites as instrumental variables. MR-Egger, inverse variance weighted (IVW), MR-PRESSO, maximum likelihood, and weighted median were performed to reveal their causal relationships. Ten metabolites were studied, which included trimethylamine-N-oxide, betaine, carnitine, choline, glutamate, kynurenine, phenylalanine, serotonin, tryptophan, and tyrosine. Sensitivity tests were also performed to evaluate the robustness of the MR study. Results: The IVW method revealed that serotonin may increase the ASD risk (OR 1.060, 95% CI: 1.006-1.118), while choline could decrease the ASD risk (OR 0.925, 95% CI: 0.868-0.988). However, no definite causality was observed between other intestinal metabolites (e.g., trimethylamine-N-oxide, betaine, and carnitine) with ASD. Additionally, neither the funnel plot nor the MR-Egger test showed horizontal pleiotropy, and the MR-PRESSO test found no outliers. Cochran's Q test showed no significant heterogeneity among the studies, suggesting the robustness of the study. Conclusion: Our study found potential causality from intestinal metabolites on ASD. Clinicians are encouraged to offer preventive measures to such populations.