Background: Periodontitis is a complex multifactorial inflammatory disease characterized by the initial gingival destruction and progressive destruction of the periodontal ligament and alveolar bone, altering homeostasis and potential tooth loss. Periodontitis is primarily caused by dysbiotic microbial biofilms and an exaggerated host immune response, with persistent inflammation and tissue breakdown and it is influenced by genetic, environmental, and behavioral risk factors. Gene polymorphisms are variations in DNA and those associated with inflammatory cytokines, immune response regulators, and matrix-degrading enzymes contribute to disease susceptibility and progression. Aim: To assess the functional impact of non-synonymous single nucleotide polymorphisms in specific genes linked to periodontitis using an In silico approach. Objectives: To assess the gene polymorphism of interleukin genes using various bio tools like Sift, Panther, PolyPhen, SNPS&Go, and FATHMM and to compare the likeliness of the results. Materials and Methods: In this study, an in-silico approach was employed to assess the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) in specific genes linked to periodontitis. Bioinformatics tools, including Sift, Panther, PolyPhen, SNPS&Go, and FATHMM, were utilized to predict the pathogenicity and structural consequences of selected polymorphisms. Results: The common tolerated sites in accordance with the various biotools are IL1B (rs1143634 F105F), IL3(rs40401 -P27S), IL4 (rs149950065- A102V, rs 199929962 -M128T), IL4R (rs1801275 -Q576R) IL6 (rs 2228145- D358A), IL 23R (rs11209026- R126Q R142Q) IL37(rs3811047 -T42A). The deleterious sites varied according to different biotool. Conclusion: These tools provided insights into evolutionary conservation, protein stability, and functional alterations induced by genetic variations. The findings highlight key deleterious polymorphisms that may serve as potential genetic markers for periodontitis susceptibility. This computational approach offers a cost-effective and rapid screening method for prioritizing polymorphisms for further experimental validation in genetic association studies.
Ramkumar et al. (Thu,) studied this question.