High-Resolution Melt Curve Analysis: An Approach for Mutation Detection in the TPO Gene of Congenital Hypothyroid Patients in Bangladesh
Year: 2023
Type: preprint
Study design: Observational Study
Abstract: Thyroid Peroxidase ( TPO ) is known to be the major gene involved in Congenital hypothyroid patients with thyroid dyshormonogenesis. This present study aimed to establish high-resolution melting (HRM) curve analysis as a supplementary mutation detection approach of Sanger sequencing targeting commonly found mutations c.1117G>T, c.1193G>C, and c.2173A>C in the TPO gene in Bangladeshi patients. We enrolled 36 confirmed cases of congenital hypothyroid patients with dyshormonogenesis to establish the HRM method. Blood samples were collected, and genomic DNA was isolated for molecular techniques. Among the 36 specimens, 20 were pre-sequenced, and mutations were characterized through Sanger sequencing. The pre-sequenced specimens (n=20) were then subjected to real-time PCR-HRM curve analysis to get the appropriate HRM condition capable of differentiating heterozygous and homozygous states for the three mutations from the wild-type state. Furthermore, 16 unknown specimens were subjected to HRM analysis to validate the method. This method showed 100 percent sensitivity and specificity to distinguish wild-type alleles from homozygous or heterozygous states (c.1117G>T, c.1193G>C, and c.2173A>C) of alleles commonly found in Bangladeshi patients. The HRM data was found to be similar to the sequencing result, thus confirming the validity of the HRM approach for TPO gene mutation. In conclusion, the established HRM-based molecular technique targeting c.1117G>T, c.1193G>C, and c.2173A>C mutations could be used as a high throughput, rapid, reliable, and cost-effective screening approach for the detection of all common mutations in TPO gene in Bangladeshi patients with dyshormonogenesis.
Source: medRxiv
Authors: Mst. Noorjahan Begum, Rumana Mahtarin, Md Tarikul Islam, Nusrat Jahan Antora, Suprovath Kumar Sarker, Nusrat Sultana, Abu A. Sajib, Abul B.M.M.K Islam, Hurjahan Banu, M A Hasanat, Kohinoor Jahan Shyamaly, Suraiya Begum, Tasnia Kawsar Konika, Shahinur Haque, Mizanul Hasan, Sadia Sultana, Taufiqur Rahman Bhuiyan, Kaiissar Mannoor, Firdausi Qadri, Sharif Akhteruzzaman
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: University Grants Commission
OpenAlex ID: W4387736588