Identification of a novel frameshift mutation in the EDAR gene causing autosomal dominant hypohidrotic ectodermal dysplasia

EDA ligand triggers plasma membrane trafficking of its receptor EDAR via PKA activation and SNAP23-containing complexes

BACKGROUND

Ectodysplasin-A (EDA), a skin-specific TNF ligand, interacts with its membrane receptor EDAR to trigger EDA signaling in skin appendage formation. Gene mutations in EDA signaling cause Anhidrotic/Hypohidrotic Ectodermal Dysplasia (A/HED), which affects the formation of skin appendages including hair, teeth, and several exocrine glands.

RESULTS

We report that EDA triggers the translocation of its receptor EDAR from a cytosolic compartment into the plasma membrane. We use protein affinity purification to show that upon EDA stimulation EDAR associates with SNAP23-STX6-VAMP1/2/3 vesicle trafficking complexes. We find that EDA-dependent PKA activation is critical for the association. Notably, either of two HED-linked EDAR mutations, T346M and R420W, prevents EDA-induced EDAR translocation; and both EDA-induced PKA activation and SNAP23 are required for Meibomian gland (MG) growth in a skin appendage model.

CONCLUSIONS

Overall, in a novel regulatory mechanism, EDA increases plasma membrane translocation of its own receptor EDAR, augmenting EDA-EDAR signaling in skin appendage formation. Our findings also provide PKA and SNAP23 as potential targets for the intervention of HED.

Pathogenic EDA Mutations in Chinese Han Families With Hypohidrotic Ectodermal Dysplasia and Genotype-Phenotype: A Correlation Analysis

Background

This study aimed to investigate the genetic causes of hypohidrotic ectodermal dysplasia (HED) in two families and elucidate the molecular pathogenesis of HED in Chinese Han patients.

Methods

Whole-exome sequencing (WES) was used to screen HED-related genes in two family members, followed by confirmatory Sanger sequencing. Bioinformatics analysis was performed for the mutations. We reviewed HED-related articles in PubMed. χ²- and Fisher's tests were used to analyze the genotype–phenotype correlations.

Results

(1) WES identified EDA missense mutations [c.1127 C > T (p.T376M; NM_001005609)] in family 1 and an EDA nonframeshift deletion mutation [c.648_683delACCTGGTCCTCCAGGTCCTCCTGGTCCTCAAGGACC (p.216_228delPPGPPGPPGPQGP; NM_001005609)] in family 2. Sanger sequencing validated the results. ANNOVAR (ANNOtate VARiation) annotation indicated that c.1127 c > T was a deleterious mutation. (2) The review of published papers revealed 68 novel mutations related to HED: 57 (83.8%) were EDA mutations, 8 (11.8%) were EDAR mutations, 2 (2.9%) were EDARADD mutations, 1 (1.5%) was a WNT10A mutation, 31 (45.6%) were missense mutations, 23 (33.8%) were deletion mutations, and 1 (1.5%) was an indel. Genotype–phenotype correlation analysis revealed that patients with EDA missense mutations had a higher frequency of hypohidrosis (P = 0.021).

Conclusions

This study identified two EDA gene mutations in two Chinese Han HED families and provides a foundation for genetic diagnosis and counseling.

Meta-analysis of genome-wide association studies identifies 8 novel loci involved in shape variation of human head hair

Shape variation of human head hair shows striking variation within and between human populations, while its genetic basis is far from being understood. We performed a series of genome-wide association studies (GWASs) and replication studies in a total of 28 964 subjects from 9 cohorts from multiple geographic origins. A meta-analysis of three European GWASs identified 8 novel loci (1p36.23 ERRFI1/SLC45A1, 1p36.22 PEX14, 1p36.13 PADI3, 2p13.3 TGFA, 11p14.1 LGR4, 12q13.13 HOXC13, 17q21.2 KRTAP, and 20q13.33 PTK6), and confirmed 4 previously known ones (1q21.3 TCHH/TCHHL1/LCE3E, 2q35 WNT10A, 4q21.21 FRAS1, and 10p14 LINC00708/GATA3), all showing genome-wide significant association with hair shape (P < 5e-8). All except one (1p36.22 PEX14) were replicated with nominal significance in at least one of the 6 additional cohorts of European, Native American and East Asian origins. Three additional previously known genes (EDAR, OFCC1, and PRSS53) were confirmed at the nominal significance level. A multivariable regression model revealed that 14 SNPs from different genes significantly and independently contribute to hair shape variation, reaching a cross-validated AUC value of 0.66 (95% CI: 0.62–0.70) and an AUC value of 0.64 in an independent validation cohort, providing an improved accuracy compared with a previous model. Prediction outcomes of 2504 individuals from a multiethnic sample were largely consistent with general knowledge on the global distribution of hair shape variation. Our study thus delivers target genes and DNA variants for future functional studies to further evaluate the molecular basis of hair shape in humans.