Crigler-Najjar syndrome II and pregnancy outcome

Crigler-Najjar type II in pregnancy: A case report

Crigler-Najjar is a rare, autosomal recessive disorder that results in mutations causing a complete absence (type I) or deficiency (type II) of the hepatic uridine diphospho-glucuronosyl transferase (UDPGT) enzyme. Both forms, however, result in unconjugated hyperbilirubinaemia which can lead to kernicterus and potentially death. Phenobarbitone can be used as an enzyme inducer in Type II to facilitate a reduction in total serum bilirubin. We report two consecutive pregnancies in a 29-year-old woman with Crigler-Najjar Type II syndrome. Phenobarbitone therapy was commenced in the first pregnancy at 16 weeks' gestation and was associated with favorable biochemical and clinical outcomes. There were no reports of long-term neonatal neurological sequelae. Tertiary center, multidisciplinary care is recommended for optimal pregnancy outcomes.

Regression Modeling and Meta-Analysis of Diagnostic Accuracy of SNP-Based Pathogenicity Detection Tools for UGT1A1 Gene Mutation

Aims. This review summarized all available evidence on the accuracy of SNP-based pathogenicity detection tools and introduced regression model based on functional scores, mutation score, and genomic variation degree. Materials and Methods. A comprehensive search was performed to find all mutations related to Crigler-Najjar syndrome. The pathogenicity prediction was done using SNP-based pathogenicity detection tools including SIFT, PHD-SNP, PolyPhen2, fathmm, Provean, and Mutpred. Overall, 59 different SNPs related to missense mutations in the UGT1A1 gene, were reviewed. Results. Comparing the diagnostic OR, our model showed high detection potential (diagnostic OR: 16.71, 95% CI: 3.38-82.69). The highest MCC and ACC belonged to our suggested model (46.8% and 73.3%), followed by SIFT (34.19% and 62.71%). The AUC analysis showed a significance overall performance of our suggested model compared to the selected SNP-based pathogenicity detection tool (P = 0.046). Conclusion. Our suggested model is comparable to the well-established SNP-based pathogenicity detection tools that can appropriately reflect the role of a disease-associated SNP in both local and global structures. Although the accuracy of our suggested model is not relatively high, the functional impact of the pathogenic mutations is highlighted at the protein level, which improves the understanding of the molecular basis of mutation pathogenesis.