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

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 variants, which account for over a fifth of disease-causing variants in JEB. This study evaluated the genetic and clinical findings from a JEB cohort, investigating genotype–phenotype correlations through bioinformatic analyses and comparison with previously reported variants. Eighteen unique variants in LAMB3, LAMA3, LAMC2, or COL17A1 were identified from 17 individuals. Seven had severe JEB, 9 had intermediate JEB, and 1 had laryngo–onycho–cutaneous syndrome. Seven variants were previously unreported. Deep phenotyping was completed for all intermediate JEB cases and demonstrated substantial variation between individuals. Splice site variants underwent analysis with SpliceAI, a state-of-the-art artificial intelligence tool, 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 laminin-332 immunofluorescence. RT-PCR was performed for 1 case to investigate resultant transcripts produced from the splice site variant. This study expands the JEB genomic and phenotypic landscape. Artificial intelligence tools show potential for predicting the functional effects of splice site variants and may identify candidates for confirmatory laboratory investigation. Investigation of RNA transcripts will help to further elucidate genotype–phenotype correlations for novel variants.