Computational analysis of functional single nucleotide polymorphisms associated with SLC26A4 gene

Area of Science:

• Genetics and Molecular Biology
• Bioinformatics
• Medical Genetics

Background:

• Single Nucleotide Polymorphisms (SNPs) are common genetic variations linked to diseases.
• The SLC26A4 gene encodes the Pendred protein, crucial for iodide transport in thyroid cells.
• Mutations in SLC26A4 are associated with Pendred syndrome, causing thyroid dysfunction and deafness.

Purpose of the Study:

• To perform in-silico analysis of missense SNPs in the SLC26A4 gene.
• To identify potentially damaging and disease-causing SNPs.
• To investigate the structural and functional impact of specific SLC26A4 variants.

Main Methods:

• In-silico analysis of 674 missense SNPs using multiple computational tools (SNPNEXUS, SNAP-2, PhD-SNP, SNPs&GO, I-Mutant, ConSurf, ModPred).
• Prediction of damaging and disease-causing nsSNPs.
• Protein structure prediction, protein-ligand docking, and Molecular Dynamics simulations for selected variants.

Main Results:

• Identified 23 highly confident damaging and disease-causing nsSNPs in SLC26A4.
• Predicted potential alterations in SLC26A4 gene function due to these SNPs.
• Confirmed significant impact of Y127H and G334A variants on protein structure and function through simulations.

Conclusions:

• The identified nsSNPs in SLC26A4 are potential contributors to Pendred syndrome.
• In-silico approaches are effective for prioritizing disease-associated genetic variations.
• Further experimental validation is warranted for the predicted damaging SNPs.