Background: Mucopolysaccharidosis type I (MPS I) is an autosomal recessive disease due to deficiency of α-L-iduronidase (IDUA), a lysosomal enzyme that degrades glycosaminoglycans (GAG) heparan and dermatan sulfate. To achieve optimal clinical outcomes, early and proper treatment is essential, which requires early diagnosis and phenotype severity prediction. Results: To establish a genotype/phenotype correlation of MPS I disease, a combination of bioinformatics tools including SIFT, PolyPhen, I-Mutant, PROVEAN, PANTHER, SNPs&GO and PHD-SNP are utilized. Through analyzing single nucleotide polymorphisms (SNPs) by these in silico approaches, 28 out of 285 missense SNPs were predicted to be damaging. By integrating outcomes from these in silico approaches, a prediction algorithm (sensitivity 94%, specificity 80%) was thereby developed. Three dimensional structural analysis of 5 candidate SNPs (P533R, P496R, L346R, D349G, T374P) were performed by SWISS PDB viewer, which revealed specific structural changes responsible for the functional impacts of these SNPs. Additionally, SNPs in the untranslated region were analyzed by UTRscan and PolymiRTS. Moreover, by investigating known pathogenic mutations and relevant patient phenotypes in previous publications, phenotype severity (severe, intermediate or mild) of each mutation was deduced. Conclusions: Collectively, these results identified potential candidate SNPs with functional significance for studying MPS I disease. This study also demonstrates the effectiveness, reliability and simplicity of these in silico approaches in addressing complexity of underlying genetic basis of MPS I disease. Further, a step-by-step guideline for phenotype prediction of MPS I disease is established, which can be broadly applied in other lysosomal diseases or genetic disorders.
Bibliographical noteFunding Information:
This work is supported by NIH grant P01HD032652. Dr. Li Ou is a fellow of the Lysosomal Disease Network (U54NS065768). The Lysosomal Disease Network is a part of the Rare Diseases Clinical Research Network (RDCRN), an initiative of the Office of Rare Diseases Research (ORDR), and NCATS. This consortium is funded through a collaboration between NCATS, the National Institute of Neurological Disorders and Stroke (NINDS), and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).
© 2017 The Author(s).
- Genotype/phenotype correlation
- In silico
- Single nucleotide polymorphism