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Review
. 2018 May;41(3):541-553.
doi: 10.1007/s10545-018-0156-5. Epub 2018 Apr 13.

Recognizable phenotypes in CDG

Carlos R Ferreira  1   2 , Ruqaia Altassan  3   4 , Dorinda Marques-Da-Silva  5   6 , Rita Francisco  5   6 , Jaak Jaeken  3   4 , Eva Morava  7   8   9
Affiliations
Review

Recognizable phenotypes in CDG

Carlos R Ferreira et al. J Inherit Metab Dis. 2018 May.

Abstract

Pattern recognition, using a group of characteristic, or discriminating features, is a powerful tool in metabolic diagnostic. A classic example of this approach is used in biochemical analysis of urine organic acid analysis, where the reporting depends more on the correlation of pertinent positive and negative findings, rather than on the absolute values of specific markers. Similar uses of pattern recognition in the field of biochemical genetics include the interpretation of data obtained by metabolomics, like glycomics, where a recognizable pattern or the presence of a specific glycan sub-fraction can lead to the direct diagnosis of certain types of congenital disorders of glycosylation. Another indispensable tool is the use of clinical pattern recognition-or syndromology-relying on careful phenotyping. While genomics might uncover variants not essential in the final clinical expression of disease, and metabolomics could point to a mixture of primary but also secondary changes in biochemical pathways, phenomics describes the clinically relevant manifestations and the full expression of the disease. In the current review we apply phenomics to the field of congenital disorders of glycosylation, focusing on recognizable differentiating findings in glycosylation disorders, characteristic dysmorphic features and malformations in PMM2-CDG, and overlapping patterns among the currently known glycosylation disorders based on their pathophysiological basis.

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Conflict of interest statement

Conflicts of interest

Carlos R. Ferreira, Ruqaiah Altassan, Rita Francesco, Dorinda Marquez-Da-Silva, Jaak Jaeken and Eva Morava declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Facial recognition analysis of patients with PMM2-CDG. A: Composite photo created by averaging the extracted mathematical information of the photos in the control cohort. B: Composite photo obtained from images of patients with PMM2-CDG. C: Score distribution of the binary comparison between unaffected controls and patients with PMM2-CDG. D: ROC curve with pertinent statistics obtained after conducting 10 random splits (true positive rate on the ordinate, true negative rate on abscissa).
Figure 2
Figure 2
Diagram on the different steps in lipid-linked oligosaccharide assembly.
See this image and copyright information in PMC

References

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