Bioinformatic analysis of single nucleotide variants in the F12 gene associated with hereditary angioedema

Hereditary angioedema (HAE) is a genetically determined disorder, a primary immunodeficiency involving complement system dysfunction. In most patients, the disease is characterized by a deficiency of C1 inhibitor (type I HAE) or impaired functional activity of the C1 inhibitor (type II HAE). In such cases, the diagnosis is based on laboratory findings. In HAE with normal C1 inhibitor levels and activity, the diagnosis can only be based on family history and/or genetic testing. Among patients with HAE with normal C1 inhibitor, mutations in the F12 gene are most frequently observed, particularly in females. However, mutations with uncertain clinical significance are often identified. Given the limited number of HAE cases, it is not feasible to experimentally determine the clinical relevance of newly discovered polymorphic variants. A potential solution to this problem is the in silico analysis of each novel polymorphism. Hence, the aim of our study was to evaluate the predictive potential of bioinformatic methods in assessing polymorphic variants in the F12 gene. The study was focused on four polymorphic variants: NC_000005.9:g.176831285C>G, NC_000005.9:g.176831258C>G, NC_000005.9:g.176831232G>C, and NC_000005.9:g.176831232G>T, with varying clinical significance statuses. To predict the effect of these polymorphic variants on the F12 protein, we used various Web-based tools employing different algorithms, including SIFT, PolyPhen-2, FATHMM-XF, MutationTaster2021, MutPred2, MUpro, I-Mutant 2, HOPE, and ChimeraX. The in silico approach demonstrated that the NC_000005.9:g.176831232G>C (p.Thr328Arg), and NC_000005.9:g.176831232G>T (p.Thr328Lys) mutations have a pathogenic effect, which is fully consistent with their previously established clinical status. At the same time, the polymorphic variants NC_000005.9:g.176831258C>G (p.Gln319His), and NC_000005.9:g.176831285C>G (p.Arg310Ser) do not appear to be independent causes of the disease. However, their potential role in modifying the clinical phenotype cannot be excluded. Bioinformatic analysis plays a key role in preliminary assessment of clinical significance of newly detected mutations in the F12 gene and provides a more precise identification of pathogenic variants. Integration of bioinformatic tools into diagnostic workflows is essential for determining the cause of disease in patients with hereditary angioedema presenting with normal levels and functional activity of the C1 inhibitor.

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