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Functional and Structural Characterization of Exonic SNPs associated with Leprosy Risk: an In Silico Analysis
* 1 , 1 , 2 , 3 , 2 , 2 , 2 , 4 , 2 , 4 , 1 , 1
1  Bacteriology and Mycology Section of the Evandro Chagas Institute (IEC), Brazil
2  University of Pará State (UEPA), Brazil
3  Federal University of Pará (UFPA), Brazil
4  Evandro Chagas Institute (IEC), Brazil
Academic Editor: Silvia Turroni

Abstract:

Introduction: Leprosy, caused by Mycobacterium leprae, shows variations in individual susceptibility, which may be linked to genetic differences in the exome. Exonic single-nucleotide polymorphisms (SNPs) can influence immune response and disease progression. This study aimed to analyze, in silico, the functional and structural impact of exonic SNPs associated with leprosy susceptibility. Methods: Data from the literature on leprosy-risk SNPs were retrieved from PubMed and SciELO. Analyses were conducted on synonymous (sSNP) and non-synonymous SNPs (nsSNP). For sSNPs, predictions included effects on mRNA structure (RNAfold, CycleFold, Kinefold), splicing (MaxEnt Scan, Ex Skip), and miRNA binding (TargetScan Score). For nsSNPs, protein damage was assessed using SIFT, PolyPhen 2, PhD-SNP, SNPs and GO, and Predict SNP 2. Pathogenicity filters identified 11 nsSNPs for further analysis of stability (CUPSAT), functionality (MutPred), and evolutionary conservation (ConSurf). Results: A total of 35 exonic SNPs were analyzed (6 sSNPs, 29 nsSNPs). The sSNP rs2230365 (NFKBIL1) showed the highest potential for impacting miRNA regulation and mRNA structure, while no significant changes were observed in splicing. The nsSNP rs5743708 (TLR2) was predicted as deleterious by all tools used. SNP rs145562243 (NCKIPSD) was shown to be destabilizing with an unfavorable ΔΔG (-5.53 kcal/mol). The molecular, structural and evolutionary impacts of each SNP were reported. Highly conserved and exposed SNPs like rs5743708 suggest that they are critical for protein function. Conclusion: This study identified 12 exonic SNPs (1 sSNP and 11 nsSNPs) as potential candidates for further in vivo studies on leprosy. These SNPs reveal complex interactions between genetic variations and their functional consequences, contributing to the understanding of disease mechanisms.

Keywords: Leprosy; Single Nucleotide Polymorphism; Computational Biology; Genomics

 
 
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