Functional assessment of the genetic findings indicating mucopolysaccharidosis type II in the prenatal setting.
Maria FullerDavid KetteridgePublished in: JIMD reports (2021)
Mucopolysaccharidosis type II (MPS II) is a multi-systemic disorder arising due to pathogenic variants in the gene located on chromosome Xq28 encoding the lysosomal enzyme, iduronate 2-sulfatase (IDS). The broad clinical heterogeneity of MPS II can be partly ascribed to the high level of molecular diversity in the gene locus with the majority of variants localised within one family. Here, we describe a case of fetal hepatomegaly that was causatively investigated for 151 genes associated with fetal hydrops and lysosomal diseases. Sequence analysis identified a novel hemizygous variant, pAsp532Gly, in exon 9 of the IDS gene. Determination of IDS activity in cultured amniotic fluid cells returned 8% of normal activity and analysis of a second sulfatase was normal, the latter virtually excluding multiple sulfatase deficiency. Together, these data supported a diagnosis of MPS II in the fetus. Additional measurement of a signature disaccharide in the amniotic fluid was normal, conflicting with enzymology indications. The baby was unremarkable at birth and 3 years later shows no clinical suspicion of MPS II, normal urinary disaccharide concentrations, and reduced IDS activity in leukocytes. His 5-year-old brother was subsequently shown to carry the same pAsp532Gly variant, with normal urinary disaccharide concentrations, reduced leukocyte IDS activity and normal phenotype. This case highlights the importance of thorough biochemical investigations, clinical and family correlation in determining the significance of genetic variants in IDS.
Keyphrases
- copy number
- genome wide
- replacement therapy
- gene expression
- endothelial cells
- mesenchymal stem cells
- peripheral blood
- cell proliferation
- genome wide identification
- electronic health record
- oxidative stress
- umbilical cord
- machine learning
- genome wide analysis
- single molecule
- big data
- deep learning
- rare case
- solid phase extraction