Frontiers in Physiology (May 2023)

Amelogenesis imperfecta: Next-generation sequencing sheds light on Witkop’s classification

  • Agnes Bloch-Zupan,
  • Agnes Bloch-Zupan,
  • Agnes Bloch-Zupan,
  • Agnes Bloch-Zupan,
  • Agnes Bloch-Zupan,
  • Tristan Rey,
  • Tristan Rey,
  • Alexandra Jimenez-Armijo,
  • Marzena Kawczynski,
  • Naji Kharouf,
  • O-Rare consortium,
  • Muriel de La Dure-Molla,
  • Emmanuelle Noirrit,
  • Magali Hernandez,
  • Clara Joseph-Beaudin,
  • Serena Lopez,
  • Corinne Tardieu,
  • Béatrice Thivichon-Prince,
  • ERN Cranio Consortium,
  • Tatjana Dostalova,
  • Milan Macek,
  • International Consortium,
  • Mustapha El Alloussi,
  • Leila Qebibo,
  • Supawich Morkmued,
  • Patimaporn Pungchanchaikul,
  • Blanca Urzúa Orellana,
  • Marie-Cécile Manière,
  • Marie-Cécile Manière,
  • Bénédicte Gérard,
  • Isaac Maximiliano Bugueno,
  • Isaac Maximiliano Bugueno,
  • Isaac Maximiliano Bugueno,
  • Virginie Laugel-Haushalter,
  • Virginie Laugel-Haushalter,
  • Virginie Laugel-Haushalter,
  • Yves Alembik,
  • Victorin Ahossi,
  • Isabelle Bailleul-Forestier,
  • Isabelle Blanchet,
  • Ariane Berdal,
  • Marie José Boileau,
  • Nicolas Chassaing,
  • François Clauss,
  • Caroline Delfosse,
  • Anne De-Saint-Martin,
  • Jean-Christophe Dahlet,
  • Bérénice Doray,
  • Jean-Luc Davideau,
  • Tiphaine Davit-Béal,
  • Hélène Dollfus,
  • Jean-Pierre Duprez,
  • Muriel de La Dure Molla,
  • Klauss Dieterich,
  • Dominique Droz,
  • Salima El Chehadeh,
  • Olivier Etienne,
  • Edouard Euvrard,
  • Laurence Faivre,
  • Benjamin Fournier,
  • Elsa Garot,
  • Bruno Grollemund,
  • Nathalie Guffon-Fouilhoux,
  • Magali Hernandez,
  • Mathilde Huckert,
  • Bertand Isidor,
  • Clara Joseph-Beaudin,
  • Sophie Jung,
  • Didier Lacombe,
  • Alinoe Lavillaurex,
  • Marine Lebrun,
  • Bruno Leheup,
  • Adeline Loing,
  • Serena Lopez,
  • Sandrine Marlin,
  • Jean-Jacques Morrier,
  • Michèle Muller-Bolla,
  • Emmanuelle Noirrit,
  • Sylvie Odent,
  • Marie Paule Gelle,
  • Juliette Piard,
  • Linda Pons,
  • Béatrice Richard,
  • Massimiliano Rossi,
  • Prune Sadones,
  • Elise Schaefer,
  • Jean-Louis Sixou,
  • Sylvie Soskin,
  • Marion Strub,
  • Corinne Tardieu,
  • Béatrice Thivichon-Prince,
  • Annick Toutain,
  • Alain Verloes,
  • Frédéric Vaysse,
  • Delphine Wagner

DOI
https://doi.org/10.3389/fphys.2023.1130175
Journal volume & issue
Vol. 14

Abstract

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Amelogenesis imperfecta (AI) is a heterogeneous group of genetic rare diseases disrupting enamel development (Smith et al., Front Physiol, 2017a, 8, 333). The clinical enamel phenotypes can be described as hypoplastic, hypomineralized or hypomature and serve as a basis, together with the mode of inheritance, to Witkop’s classification (Witkop, J Oral Pathol, 1988, 17, 547–553). AI can be described in isolation or associated with others symptoms in syndromes. Its occurrence was estimated to range from 1/700 to 1/14,000. More than 70 genes have currently been identified as causative.Objectives: We analyzed using next-generation sequencing (NGS) a heterogeneous cohort of AI patients in order to determine the molecular etiology of AI and to improve diagnosis and disease management.Methods: Individuals presenting with so called “isolated” or syndromic AI were enrolled and examined at the Reference Centre for Rare Oral and Dental Diseases (O-Rares) using D4/phenodent protocol (www.phenodent.org). Families gave written informed consents for both phenotyping and molecular analysis and diagnosis using a dedicated NGS panel named GenoDENT. This panel explores currently simultaneously 567 genes. The study is registered under NCT01746121 and NCT02397824 (https://clinicaltrials.gov/).Results: GenoDENT obtained a 60% diagnostic rate. We reported genetics results for 221 persons divided between 115 AI index cases and their 106 associated relatives from a total of 111 families. From this index cohort, 73% were diagnosed with non-syndromic amelogenesis imperfecta and 27% with syndromic amelogenesis imperfecta. Each individual was classified according to the AI phenotype. Type I hypoplastic AI represented 61 individuals (53%), Type II hypomature AI affected 31 individuals (27%), Type III hypomineralized AI was diagnosed in 18 individuals (16%) and Type IV hypoplastic-hypomature AI with taurodontism concerned 5 individuals (4%). We validated the genetic diagnosis, with class 4 (likely pathogenic) or class 5 (pathogenic) variants, for 81% of the cohort, and identified candidate variants (variant of uncertain significance or VUS) for 19% of index cases. Among the 151 sequenced variants, 47 are newly reported and classified as class 4 or 5. The most frequently discovered genotypes were associated with MMP20 and FAM83H for isolated AI. FAM20A and LTBP3 genes were the most frequent genes identified for syndromic AI. Patients negative to the panel were resolved with exome sequencing elucidating for example the gene involved ie ACP4 or digenic inheritance.Conclusion: NGS GenoDENT panel is a validated and cost-efficient technique offering new perspectives to understand underlying molecular mechanisms of AI. Discovering variants in genes involved in syndromic AI (CNNM4, WDR72, FAM20A … ) transformed patient overall care. Unravelling the genetic basis of AI sheds light on Witkop’s AI classification.

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