Genotype-phenotype correlation in Junctional Epidermolysis Bullosa: signposts to severity

Abstract

Junctional epidermolysis bullosa (JEB) is a rare autosomal recessive genodermatosis with a broad spectrum of phenotypes. Current genotype-phenotype paradigms are insufficient to accurately predict JEB subtype and characteristics from genotype, particularly for splice site mutations, which account for over a fifth of disease-causing mutations in JEB. This study evaluated genetic and clinical findings from a JEB cohort, investigating genotype-phenotype correlations through bioinformatic analyses and comparison with previously reported mutations. Eighteen unique mutations in LAMB3, LAMA3, LAMC2 or COL17A1 were identified from seventeen individuals. Seven had severe JEB, nine intermediate JEB and one laryngo-onycho-cutaneous syndrome. Seven mutations were previously unreported. Deep phenotyping was completed for all intermediate JEB cases and demonstrated substantial variation between individuals. Splice site mutations underwent analysis with SpliceAI, a state-of-the-art artificial intelligence tool, in order to predict resultant transcripts. Predicted functional effects included exon skipping and cryptic splice site activation, which provided potential explanations for disease severity and in most cases correlated with lamimin-332 immunofluorescence. RT-PCR was performed for one case to investigate resultant transcripts produced from the splice site mutation. This study expands the JEB genomic and phenotypic landscape. AI tools show potential for predicting functional effects of splice site mutations and may identify candidates for confirmatory laboratory investigation. Investigation of RNA transcripts will help to further elucidate genotype-phenotype correlations for novel mutations.