Variant predictions in congenital adrenal hyperplasia caused by mutations in CYP21A2

Mayara J Prado; Rodrigo Ligabue-Braun; Arnaldo Zaha; Maria Lucia Rosa Rossetti; Amit V Pandey

doi:10.3389/fphar.2022.931089

Variant predictions in congenital adrenal hyperplasia caused by mutations in CYP21A2

Front Pharmacol. 2022 Oct 5:13:931089. doi: 10.3389/fphar.2022.931089. eCollection 2022.

Authors

Mayara J Prado^{1

2

3

4}, Rodrigo Ligabue-Braun⁵, Arnaldo Zaha^{1

2}, Maria Lucia Rosa Rossetti^{1

6}, Amit V Pandey^{3

4}

Affiliations

¹ Graduate Program in Cell and Molecular Biology, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.
² Center for Biotechnology, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.
³ Translational Hormone Research, Department of Biomedical Research, University of Bern, Bern, Switzerland.
⁴ Pediatric Endocrinology Unit, Department of Pediatrics, University Children's Hospital Bern, Bern, Switzerland.
⁵ Departament of Pharmacosciences, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil.
⁶ Graduate Program in Molecular Biology Applied to Health, Universiade Luterana do Brasil (ULBRA), Canoas, Brazil.

Abstract

CYP21A2 deficiency represents 95% of congenital adrenal hyperplasia (CAH) cases, a group of genetic disorders that affect steroid biosynthesis. The genetic and functional analysis provide critical tools to elucidate complex CAH cases. One of the most accessible tools to infer the pathogenicity of new variants is in silico prediction. Here, we analyzed the performance of in silico prediction tools to categorize missense single nucleotide variants (SNVs) of CYP21A2. SNVs of CYP21A2 characterized in vitro by functional assays were selected to assess the performance of online single and meta predictors. SNVs were tested separately or in combination with the related phenotype (severe or mild CAH form). In total, 103 SNVs of CYP21A2 (90 pathogenic and 13 neutral) were used to test the performance of 13 single-predictors and four meta-predictors. All SNVs associated with the severe phenotypes were well categorized by all tools, with an accuracy of between 0.69 (PredictSNP2) and 0.97 (CADD), and Matthews' correlation coefficient (MCC) between 0.49 (PoredicSNP2) and 0.90 (CADD). However, SNVs related to the mild phenotype had more variation, with the accuracy between 0.47 (S3Ds&GO and MAPP) and 0.88 (CADD), and MCC between 0.18 (MAPP) and 0.71 (CADD). From our analysis, we identified four predictors of CYP21A2 variant pathogenicity with good performance, CADD, ConSurf, DANN, and PolyPhen2. These results can be used for future analysis to infer the impact of uncharacterized SNVs in CYP21A2.

Keywords: CAH; CYP21A2; mutation analysis; online prediction; pathogenicity prediction tools; steroid metabolism.