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From genotype to phenotype: Early prediction of disease severity in argininosuccinic aciduria

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  • Matthias Zielonka
  • Sven F Garbade
  • Florian Gleich
  • Jürgen G Okun
  • Sandesh C S Nagamani
  • Andrea L Gropman
  • Georg F Hoffmann
  • Stefan Kölker
  • Roland Posset
  • Urea Cycle Disorders Consortium (UCDC) and the European registry and network for Intoxication type Metabolic Diseases (E-IMD) Consortia Study Group
  • Allan Meldgaard Lund (Member of study group)
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Argininosuccinic aciduria (ASA) is an inherited urea cycle disorder and has a highly variable phenotypic spectrum ranging from individuals with lethal hyperammonemic encephalopathy, liver dysfunction, and cognitive deterioration, to individuals with a mild disease course. As it is difficult to predict the phenotypic severity, we aimed at identifying a reliable disease prediction model. We applied a biallelic expression system to assess the functional impact of pathogenic argininosuccinate lyase (ASL) variants and to determine the enzymatic activity of ASL in 58 individuals with ASA. This cohort represented 42 ASL gene variants and 42 combinations in total. Enzymatic ASL activity was compared with biochemical and clinical endpoints from the UCDC and E-IMD databases. Enzymatic ASL activity correlated with peak plasma ammonium concentration at initial presentation and with the number of hyperammonemic events (HAEs) per year of observation. Individuals with ≤9% of enzymatic activity had more severe initial decompensations and a higher annual frequency of HAEs than individuals above this threshold. Enzymatic ASL activity also correlated with the cognitive outcome and the severity of the liver disease, enabling a reliable severity prediction for individuals with ASA. Thus, enzymatic activity measured by this novel expression system can serve as an important marker of phenotypic severity.

Original languageEnglish
JournalHuman Mutation
Volume41
Issue number5
Pages (from-to)946-960
Number of pages15
ISSN1059-7794
DOIs
Publication statusPublished - May 2020

ID: 62401883