Genomic profiling of interferon signaling pathway gene mutations in type 2 diabetic individuals with COVID-19

Abstract Objectives Comorbidities, especially diabetes, significantly contribute to the mortality and morbidity of COVID-19. Studies indicate higher rates of mortality and morbidity among diabetic COVID-19 patients compared to the general population. However, the precise mechanisms underlying this immune response remain incompletely understood. Our study aimed to explore potential disparities in COVID-19 prognosis among type 2 diabetic patients and investigate the genomic-level relationship between key proteins of the interferon signaling pathway: IFNAR1, IFNAR2, IRF3 , and IRF7. Methods Mutation/polymorphism analysis was conducted to identify potential mutations and polymorphisms in the study group. Predictive assessments of mutation pathogenicity were performed using the PolyPhen-2 bioinformatics tool, while STRING network analysis enhanced our understanding of functional protein relationships in cellular processes. Results We detected 10 mutations (3 missense, 2 intronic, 2 indel, 1 nonsense, 1 regulatory, and 1 frameshift mutation), all documented in the Human Gene Mutation Database. PolyPhen2 analysis flagged three missense and 1 nonsense mutations as potential pathogens. The study found no consistent trend in mutation rates across all genes. However, mutation rates in the IFNAR2 and IRF7 genes decreased as disease severity lessened in both patient and control groups. Diabetic and Covid-19 patients exhibited higher mutation rates in the IFNAR2, IRF3, and IRF7 genes compared to non-diabetic controls, suggesting that Type 2 diabetic patients might be more susceptible to genetic mutations when infected with COVID-19. Conclusions Understanding these genetic profiles could improve disease severity assessments, enhance preventive measures, and aid in developing effective treatment strategies for coronaviral syndromes and severe acute respiratory infections.