Genetic Association and Functional Prediction of PTEN and TSC1 3'UTR Variants in Autism Spectrum Disorder Among Tunisian Patients.

Researchers studied genetic variations in two important genes (PTEN and TSC1) in 108 Tunisian children with autism and 184 children without autism. They found that certain genetic changes, particularly in the PTEN gene, were more common in children with autism. These genetic differences may affect how genes are regulated after they're copied, potentially influencing autism development. The findings suggest these genetic variations could be risk factors for autism.

This case-control study examined genetic variants in the 3' untranslated regions of PTEN and TSC1 genes among 108 Tunisian individuals with ASD and 184 controls. The research focused on how these non-coding single-nucleotide polymorphisms might affect autism risk through altered miRNA binding and post-transcriptional regulation. PTEN variant rs701848 showed significant association with ASD risk (p<0.0004 after correction), while TSC1 variant rs739442 showed suggestive association. Comprehensive bioinformatics analysis revealed rs701848 had high predicted functional impact, including loss of neurodevelopmental miRNA binding sites and high evolutionary conservation.

The study integrates population genetics with functional predictions to support the role of non-coding variants in ASD susceptibility.

Findings suggest genetic testing of PTEN 3'UTR variants may have clinical utility for ASD risk assessment. However, functional validation and replication studies are needed before clinical implementation. The research supports personalized medicine approaches based on genetic profiling.

Single population study (Tunisian) limits generalizability. Small sample size (108 cases) reduces statistical power. Functional predictions are computational rather than experimentally validated. Case-control design cannot establish causation. No replication in independent cohorts reported.

ASD etiology may be influenced by non-coding single-nucleotide polymorphisms (SNPs) in 3' untranslated regions (3'UTRs). These variations can affect post-transcriptional regulation by altering RNA structure and miRNA binding patterns. PTEN and TSC1, two key regulators of the PI3K/AKT/mTOR signaling pathway, are promising candidate genes for ASD. We performed a case-control study involving 108 individuals diagnosed with ASD and 184 healthy matched controls from the Tunisian population.

Four specific 3'UTR SNPs (PTEN: rs701848, rs34140758; TSC1: rs739442, rs2809244) were analyzed through genotyping. Statistical associations were assessed using various genetic inheritance models, with multivariate logistic regression adjusting for gender, family psychiatric history, and parental age. A thorough bioinformatics approach was applied, which included miRNASNP-v3 for predicting altered miRNA binding sites, RNAhybrid for calculating the minimum free energy (MFE) of miRNA-mRNA duplexes, PhyloP and phastCons for evolutionary conservation analysis, and mfold for modeling RNA secondary structures. PTEN rs701848 showed a significant statistical association with ASD risk that remained significant after correction for multiple testing (corrected p < .0004), while TSC1 rs739442 showed a suggestive association (p = .009, corrected p = .036).

Systematic functional assessment using multi-criteria evaluation identified rs701848 as having high predicted functional impact (extensive miRNA binding changes, including loss of neurodevelopmental miR-129 sites, high conservation (PhyloP = 2.24), and altered RNA stability), while rs739442 showed moderate impact, and rs2809244 minimal predicted functional significance. For mutant alleles, RNA structure modeling showed slight decreases in mRNA stability, which might affect transcript accessibility to regulatory factors. Evolutionary conservation analysis demonstrated that rs701848 and rs34140758 reside in highly conserved areas, whereas rs739442 and rs2809244 are in less evolutionarily conserved regions. By integrating population-genetics data with bioinformatics predictions, this study supports the relevance of non-coding SNPs as biological risk factors for ASD.

It emphasizes their functional significance in modulating post-transcriptional gene regulation mechanisms.