Characterization of glycosylphosphatidylinositol biosynthesis defects by clinical features, flow cytometry, and automated image analysis.
Document Type
Article
Publication Date
1-9-2018
JAX Source
Genome Med 2018 Jan 9; 10(1):3
Volume
10
Issue
1
First Page
3
Last Page
3
ISSN
1756-994X
PMID
29310717
DOI
https://doi.org/10.1186/s13073-017-0510-5
Abstract
BACKGROUND: Glycosylphosphatidylinositol biosynthesis defects (GPIBDs) cause a group of phenotypically overlapping recessive syndromes with intellectual disability, for which pathogenic mutations have been described in 16 genes of the corresponding molecular pathway. An elevated serum activity of alkaline phosphatase (AP), a GPI-linked enzyme, has been used to assign GPIBDs to the phenotypic series of hyperphosphatasia with mental retardation syndrome (HPMRS) and to distinguish them from another subset of GPIBDs, termed multiple congenital anomalies hypotonia seizures syndrome (MCAHS). However, the increasing number of individuals with a GPIBD shows that hyperphosphatasia is a variable feature that is not ideal for a clinical classification.
METHODS: We studied the discriminatory power of multiple GPI-linked substrates that were assessed by flow cytometry in blood cells and fibroblasts of 39 and 14 individuals with a GPIBD, respectively. On the phenotypic level, we evaluated the frequency of occurrence of clinical symptoms and analyzed the performance of computer-assisted image analysis of the facial gestalt in 91 individuals.
RESULTS: We found that certain malformations such as Morbus Hirschsprung and diaphragmatic defects are more likely to be associated with particular gene defects (PIGV, PGAP3, PIGN). However, especially at the severe end of the clinical spectrum of HPMRS, there is a high phenotypic overlap with MCAHS. Elevation of AP has also been documented in some of the individuals with MCAHS, namely those with PIGA mutations. Although the impairment of GPI-linked substrates is supposed to play the key role in the pathophysiology of GPIBDs, we could not observe gene-specific profiles for flow cytometric markers or a correlation between their cell surface levels and the severity of the phenotype. In contrast, it was facial recognition software that achieved the highest accuracy in predicting the disease-causing gene in a GPIBD.
CONCLUSIONS: Due to the overlapping clinical spectrum of both HPMRS and MCAHS in the majority of affected individuals, the elevation of AP and the reduced surface levels of GPI-linked markers in both groups, a common classification as GPIBDs is recommended. The effectiveness of computer-assisted gestalt analysis for the correct gene inference in a GPIBD and probably beyond is remarkable and illustrates how the information contained in human faces is pivotal in the delineation of genetic entities. Genome Med 2018 Jan 9; 10(1):3.
Recommended Citation
Knaus A,
Pantel J,
Pendziwiat M,
Hajjir N,
Zhao M,
Hsieh T,
Schubach M,
Gurovich Y,
Fleischer N,
Jäger M,
Köhler S,
Muhle H,
Korff C,
Møller R,
Bayat A,
Calvas P,
Chassaing N,
Warren H,
Skinner S,
Louie R,
Evers C,
Bohn M,
Christen H,
van den Born M,
Obersztyn E,
Charzewska A,
Endziniene M,
Kortüm F,
Brown N,
Robinson P,
Schelhaas H,
Weber Y,
Helbig I,
Mundlos S,
Horn D,
Krawitz P.
Characterization of glycosylphosphatidylinositol biosynthesis defects by clinical features, flow cytometry, and automated image analysis. Genome Med 2018 Jan 9; 10(1):3