Heterozygous COL17A1 variants are a frequent cause of amelogenesis imperfecta.

BACKGROUND: Collagen XVII is most typically associated with human disease when biallelic COL17A1 variants (>230) cause junctional epidermolysis bullosa (JEB), a rare, genetically heterogeneous, mucocutaneous blistering disease with amelogenesis imperfecta (AI), a developmental enamel defect. Despite recognition that heterozygous carriers in JEB families can have AI, and that heterozygous COL17A1 variants also cause dominant corneal epithelial recurrent erosion dystrophy (ERED), the importance of heterozygous COL17A1 variants causing dominant non-syndromic AI is not widely recognised. METHODS: Probands from an AI cohort were screened by single molecule molecular inversion probes or targeted hybridisation capture (both a custom panel and whole exome sequencing) for COL17A1 variants. Patient phenotypes were assessed by clinical examination and analyses of affected teeth. RESULTS: Nineteen unrelated probands with isolated AI (no co-segregating features) had 17 heterozygous, potentially pathogenic COL17A1 variants, including missense, premature termination codons, frameshift and splice site variants in both the endo-domains and the ecto-domains of the protein. The AI phenotype was consistent with enamel of near normal thickness and variable focal hypoplasia with surface irregularities including pitting. CONCLUSION: These results indicate that COL17A1 variants are a frequent cause of dominantly inherited non-syndromic AI. Comparison of variants implicated in AI and JEB identifies similarities in type and distribution, with five identified in both conditions, one of which may also cause ERED. Increased availability of genetic testing means that more individuals will receive reports of heterozygous COL17A1 variants. We propose that patients with isolated AI or ERED, due to COL17A1 variants, should be considered as potential carriers for JEB and counselled accordingly, reflecting the importance of multidisciplinary care.

Amelogenesis Imperfecta; Genes, Proteins, and Pathways.

Amelogenesis imperfecta (AI) is the name given to a heterogeneous group of conditions characterized by inherited developmental enamel defects. AI enamel is abnormally thin, soft, fragile, pitted and/or badly discolored, with poor function and aesthetics, causing patients problems such as early tooth loss, severe embarrassment, eating difficulties, and pain. It was first described separately from diseases of dentine nearly 80 years ago, but the underlying genetic and mechanistic basis of the condition is only now coming to light. Mutations in the gene AMELX, encoding an extracellular matrix protein secreted by ameloblasts during enamel formation, were first identified as a cause of AI in 1991. Since then, mutations in at least eighteen genes have been shown to cause AI presenting in isolation of other health problems, with many more implicated in syndromic AI. Some of the encoded proteins have well documented roles in amelogenesis, acting as enamel matrix proteins or the proteases that degrade them, cell adhesion molecules or regulators of calcium homeostasis. However, for others, function is less clear and further research is needed to understand the pathways and processes essential for the development of healthy enamel. Here, we review the genes and mutations underlying AI presenting in isolation of other health problems, the proteins they encode and knowledge of their roles in amelogenesis, combining evidence from human phenotypes, inheritance patterns, mouse models, and in vitro studies. An LOVD resource (http://dna2.leeds.ac.uk/LOVD/) containing all published gene mutations for AI presenting in isolation of other health problems is described. We use this resource to identify trends in the genes and mutations reported to cause AI in the 270 families for which molecular diagnoses have been reported by 23rd May 2017. Finally we discuss the potential value of the translation of AI genetics to clinical care with improved patient pathways and speculate on the possibility of novel treatments and prevention strategies for AI.