Targeted polymerase chain reaction-based enrichment and next generation sequencing for diagnostic testing of congenital disorders of glycosylation

Abstract

Purpose: Congenital disorders of glycosylation are a heterogeneous group of disorders caused by deficient glycosylation, primarily affecting the N-linked pathway. It is estimated that more than 40% of congenital disorders of glycosylation patients lack a confirmatory molecular diagnosis. The purpose of this study was to improve molecular diagnosis for congenital disorders of glycosylation by developing and validating a next generation sequencing panel for comprehensive mutation detection in 24 genes known to cause congenital disorders of glycosylation.

Methods: Next generation sequencing validation was performed on 12 positive control congenital disorders of glycosylation patients. These samples were blinded as to the disease-causing mutations. Both RainDance and Fluidigm platforms were used for sequence enrichment and targeted amplification. The SOLiD platform was used for sequencing the amplified products. Bioinformatic analysis was performed using NextGENe® software.

Results: The disease-causing mutations were identified by next generation sequencing for all 12 positive controls. Additional variants were also detected in three controls that are known or predicted to impair gene function and may contribute to the clinical phenotype.

Conclusions: We conclude that development of next generation sequencing panels in the diagnostic laboratory where multiple genes are implicated in a disorder is more cost-effective and will result in improved and faster patient diagnosis compared with a gene-by-gene approach. Recommendations are also provided for data analysis from the next generation sequencing-derived data in the clinical laboratory, which will be important for the widespread use of this technology.

Figure 1. NGS detection and Sanger sequencing confirmation for patient CDG-0103

Patient CDG-0103 has the homozygous missense mutation c.139A>C in the gene ALG8 A. NGS detection (labeled by arrow) of c.139A>C using RainDance for enrichment B. NGS detection (labeled by arrow) of c.139A>C using Fluidigm for enrichment C. Sanger sequencing confirmation of c.139A>C

Figure 2. NGS detection and Sanger sequencing confirmation for patient CDG-0327

Patient CDG-0327 has the homozygous insertion mutation c.323_324insT in the gene COG7 A. NGS detection (labeled by arrow) of c.323_324insT using RainDance for enrichment B. NGS detection (labeled by arrow) of c.323_324insT using Fluidigm for enrichment C. Sanger sequencing confirmation of c.323_324insT

Figure 3

Patient CDG-0216 has the deletion mutation c.1687_1688delTT and intronic mutation IVS3+1G>A in the gene COG8 A. NGS detection (labeled by arrow) of c.1687_1688delTT using RainDance for enrichment B. Sanger sequencing confirmation of c.1687_1688delTT C. NGS detection (labeled by arrow) of IVS3+1G>A using RainDance for enrichment D. NGS detection (labeled by arrow) of IVS3+1G>A using Fluidigm for enrichment E. Sanger sequencing confirmation of IVS3+1G>A. NGS data for c.1687_1688delTT using Fluidigm for enrichment is not available to due no coverage for exon 5 of COG8

Figure 4. Algorithm for clinical testing for patients suspected of having CDG

A combination of biochemical and molecular approaches are used to provide a diagnosis of which subtype of CDG a patient has.