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Mutations in γ-secretase subunit–encoding PSENEN underlie Dowling-Degos disease associated with acne inversa
Damian J. Ralser, … , Benjamin Odermatt, Regina C. Betz
Damian J. Ralser, … , Benjamin Odermatt, Regina C. Betz
Published April 3, 2017; First published March 13, 2017
Citation Information: J Clin Invest. 2017;127(4):1485-1490. https://doi.org/10.1172/JCI90667.
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Categories: Brief Report Dermatology Genetics

Mutations in γ-secretase subunit–encoding PSENEN underlie Dowling-Degos disease associated with acne inversa

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Abstract

Dowling-Degos disease (DDD) is an autosomal-dominant disorder of skin pigmentation associated with mutations in keratin 5 (KRT5), protein O-fucosyltransferase 1 (POFUT1), or protein O-glucosyltransferase 1 (POGLUT1). Here, we have identified 6 heterozygous truncating mutations in PSENEN, encoding presenilin enhancer protein 2, in 6 unrelated patients and families with DDD in whom mutations in KRT5, POFUT1, and POGLUT1 have been excluded. Further examination revealed that the histopathologic feature of follicular hyperkeratosis distinguished these 6 patients from previously studied individuals with DDD. Knockdown of psenen in zebrafish larvae resulted in a phenotype with scattered pigmentation that mimicked human DDD. In the developing zebrafish larvae, in vivo monitoring of pigment cells suggested that disturbances in melanocyte migration and differentiation underlie the DDD pathogenesis associated with PSENEN. Six of the PSENEN mutation carriers presented with comorbid acne inversa (AI), an inflammatory hair follicle disorder, and had a history of nicotine abuse and/or obesity, which are known trigger factors for AI. Previously, PSENEN mutations were identified in familial AI, and comanifestation of DDD and AI has been reported for decades. The present work suggests that PSENEN mutations can indeed cause a comanifestation of DDD and AI that is likely triggered by predisposing factors for AI. Thus, the present report describes a DDD subphenotype in PSENEN mutation carriers that is associated with increased susceptibility to AI.

Authors

Damian J. Ralser, F. Buket Ü. Basmanav, Aylar Tafazzoli, Jade Wititsuwannakul, Sarah Delker, Sumita Danda, Holger Thiele, Sabrina Wolf, Michélle Busch, Susanne A. Pulimood, Janine Altmüller, Peter Nürnberg, Didier Lacombe, Uwe Hillen, Jörg Wenzel, Jorge Frank, Benjamin Odermatt, Regina C. Betz

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