ArticleOrphanet journal of rare diseases2017
Phenotype prediction for mucopolysaccharidosis type I by in silico analysis.
Article in Orphanet journal of rare diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 33 citations in OpenAlex.
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- Proteus syndrome with progressive paralysis of the unilateral lower limb: A rare case report and literature review.Heliyon · 2024Article
- Protein modeling and in silico analysis to assess pathogenicity ofMolecular vision · 2023Article
- In silico analysis of mutations associated with occult hepatitis B virus (HBV) infection in South Africa.Archives of virology · 2021Article
- Molecular Analysis of Vietnamese Patients with Mucopolysaccharidosis Type I.Life (Basel, Switzerland) · 2021Article
- A computational approach to analyse the amino acid variants of GLB1 protein causing GM1 Gangliosidosis.Metabolic brain disease · 2021Article
- Mutation analysis and clinical characterization of Iranian patients with mucopolysaccharidosis type I.Journal of clinical laboratory analysis · 2019Article
- Genotype-phenotype relationships in mucopolysaccharidosis type I (MPS I): Insights from the International MPS I Registry.Clinical genetics · 2019Article
- Genotype-phenotype correlation of gangliosidosis mutations using in silico tools and homology modeling.Molecular genetics and metabolism reports · 2019Article
- A longitudinal study of neurocognition and behavior in patients with Hurler-Scheie syndrome heterozygous for the L238Q mutation.Molecular genetics and metabolism reports · 2019Article
- In silico analysis of missense mutations in exons 1-5 of the F9 gene that cause hemophilia B.BMC bioinformatics · 2019Article
- Identification and functional analysis of missense mutations in the lecithin cholesterol acyltransferase gene in a Chilean patient with hypoalphalipoproteinemia.Lipids in health and disease · 2019Article
- In silico approach to the analysis of SNPs in the human APAF1 gene.Turkish journal of biology = Turk biyoloji dergisi · 2019Article
- Computational modelling approaches as a potential platform to understand the molecular genetics association between Parkinson's and Gaucher diseases.Metabolic brain disease · 2018Article
- In Silico Analysis of Hepatitis B Virus Occult Associated Mutations in Botswana Using a Novel Algorithm.Genes · 2018Article
- SAAMP 2.0: An algorithm to predict genotype-phenotype correlation of lysosomal storage diseases.Clinical genetics · 2018Article
- In silico VHL Gene Mutation Analysis and Prognosis of Pancreatic Neuroendocrine Tumors in von Hippel-Lindau Disease.The Journal of clinical endocrinology and metabolism · 2018Article
- Molecular andF1000Research · 2018Article
- Coping Strategies, Stress, and Support Needs in Caregivers of Children with Mucopolysaccharidosis.JIMD reports · 2018Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundMucopolysaccharidosis 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.
resultsTo 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.
conclusionsCollectively, 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.
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