In silico analysis of SNPs and miRNAs of KCTD13, CSDE1, SLC6A1 genes associated with autism spectrum disorder - Kübra Çoruh Kınalı; Ebru Özkan Oktay; Mesut Karahan (Egyptian Journal of Medical Human Genetics)
Abstract Background Single nucleotide polymorphism (SNP) is called changes in a single base sequence in DNA between individuals. Micro-RNAs (miRNAs) are short, non-coding RNA molecules that control gene expression after transcription. Today, SNPs and miRNAs are associated with many diseases, and one of them is autism spectrum disorder (ASD). ASD is a neurodevelopmental condition identified by symptoms that reduce the quality of life such as stereotypical movements, lack of social interaction and communication skills, cognitive and language disorders. The objective of this study is to utilize in silico tools to predict the possible damaging impacts of SNPs (missense) in ASD-related KCTD13, CSDE1, and SLC6A1 genes that cause amino acid substitution on protein function, stability, structure, and miRNA target binding sites. Methods The SNPs and protein amino acid sequences were obtained from the NCBI dbSNP and UniProt databases. This data served as input for predictions, which were carried out using different computational tools like SIFT, PolyPhen-2, SNPs&GO, PROVEAN, MutationAssessor, PhD-SNP, PANTHER, SNAP-2, Meta-SNP, I-Mutant 2.0, MUpro, and Project HOPE. For miRNA analysis, the miRSNP and PolymiRTS tools were utilized. GeneMANIA and STRING were also employed to explore gene–gene and protein–protein interactions. Results A total of 16 variants in these three genes were estimated to be potentially harmful via in silico analysis. As a result of the miRSNP and PolymiRTS analyses, it was found that 407 miRNAs could affect the regulation of target genes through the identified SNP variations. Furthermore, the predictive impact of those SNPs on protein stabilization was examined and three-dimensional protein models were created. Conclusion This study revealed the potential effects of genetic variations on three genes associated with ASD. The findings suggest that computational analysis of miRNA and SNPs on these ASD-related genes could provide valuable insights into the genetic mechanisms underlying ASD. In addition, it is suggested to investigate through experimental research whether the findings can be utilized as potential biomarkers for diagnosing and treating autism.