New algorythm for the characterization of novel variants in the thyroglobulin gene: integration of in silico tools and expression essays

Abstract

Thyroglobulin (TG) is a homodimeric glycoprotein synthesized by the thyroid gland. To date, two hundred twenty-seven variations of the TG gene had been identified in humans. Thyroid dyshormonogenesis due to TG gene mutations have an estimated incidence of approximately 1 in 100,000 newborns. The clinical spectrum ranges from euthyroid to mild or severe hypothyroidism. Missense variants represent a large number of spontaneous variations that cause human disease. Such variants can behave with heterogeneous patterns of pathogenicity, depending of the amino acids and structures involved and the impact of the variant to create folding rearrangements. Therefore, the pathogenicity of missense mutations can be more challenging to predict. In the present work we show pathogenicity predictions of two novel variants in TG identified by our group, p.Pro2232Leu and p.Cys1282Tyr, where we combine the performance between pathogenicity prediction programs, protein modeling using ChimeraX and the gold standard protein expression system in order to accurate our knowledge in the interpretation of results using In Silico tools. The results show that of 20 programs, Pro2232Leu and p.Cys1282Tyr variants were defined as pathogenic by 17 and 15 programs respectively. QuimeraX analysis showed important structural changes as rupture of hydrogen’s bonds and the arisement of Clashes that could affect the correct folding for both variants. To corroborate the results identified In Silico, we proceeded to perform directed mutagenesis on recombinant plasmids (pcDNA6-TG) and transfection of the same into HEK93T cells. The Western Blot to compare the cell lysate and supernatant showed that both p.Pro2232Leu and p.Cys1282Tyr variants produced intracellular retention. Our results show that the combination of In Silicoprediction programs with protein modeling analysis improves and makes the identification and characterization of pathogenic variants more effective.