Anterior segment dysgenesis (ASD) encompasses a spectrum of ocular disorders affecting the structures of the anterior eye chamber. Mutations in several genes, involved in eye development, are implicated in this disorder. ASD is often accompanied by diverse multisystemic symptoms and another genetic cause, such as variants in genes encoding collagen type IV. Thus, a wide spectrum of phenotypes and underlying genetic diversity make fast and proper diagnosis challenging. Here, we used AMELIE, an automatic text mining tool that enriches data with the most up-to-date information from literature, and wANNOVAR, which is based on well-documented databases and incorporates variant filtering strategy to identify genetic variants responsible for severely-manifested ASD in a newborn child. This strategy, applied to trio sequencing data in compliance with ACMG 2015 guidelines, helped us find two compound heterozygous variants of the B3GLCT gene, of which c.660+1G>A (rs80338851) was previously associated with the phenotype of Peters plus syndrome (PPS), while the second, NM_194318.3:c.755delC (p.T252fs), in exon 9 of the same gene was noted for the first time. PPS, a very rare subtype of ASD, is a glycosylation disorder, where the dysfunctional B3GLCT gene product, O-fucose-specific β-1,3-glucosyltransferase, is ineffective in providing a noncanonical quality control system for proper protein folding in cells. Our study expands the mutation spectrum of the B3GLCT gene related to PPS. We suggest that the implementation of automatic text mining tools in combination with careful variant filtering could help translate sequencing results into diagnosis, thus, considerably accelerating the diagnostic process and, thereby, improving patient management.
Manually structured digital abstracts: A scaffold for automatic text mining
