LINE-1 global DNA methylation, iron homeostasis genes, sex and age in sudden sensorineural hearing loss (SSNHL)

Abstract

Background: Sudden sensorineural hearing loss (SSNHL) is an abrupt loss of hearing, still idiopathic in most of cases. Several mechanisms have been proposed including genetic and epigenetic interrelationships also considering iron homeostasis genes, ferroptosis and cellular stressors such as iron excess and dysfunctional mitochondrial superoxide dismutase activity.

Results: We investigated 206 SSNHL patients and 420 healthy controls for the following genetic variants in the iron pathway: SLC40A1 - 8CG (ferroportin; FPN1), HAMP - 582AG (hepcidin; HEPC), HFE C282Y and H63D (homeostatic iron regulator), TF P570S (transferrin) and SOD2 A16V in the mitochondrial superoxide dismutase-2 gene. Among patients, SLC40A1 - 8GG homozygotes were overrepresented (8.25% vs 2.62%; P = 0.0015) as well SOD2 16VV genotype (32.0% vs 24.3%; P = 0.037) accounting for increased SSNHL risk (OR = 3.34; 1.54-7.29 and OR = 1.47; 1.02-2.12, respectively). Moreover, LINE-1 methylation was inversely related (r² = 0.042; P = 0.001) with hearing loss score assessed as pure tone average (PTA, dB HL), and the trend was maintained after SLC40A1 - 8CG and HAMP - 582AG genotype stratification (Δ_SLC40A1 = + 8.99 dB HL and Δ_HAMP = - 6.07 dB HL). In multivariate investigations, principal component analysis (PCA) yielded PC1 (PTA, age, LINE-1, HAMP, SLC40A1) and PC2 (sex, HFE_C282Y, SOD2, HAMP) among the five generated PCs, and logistic regression analysis ascribed to PC1 an inverse association with moderate/severe/profound HL (OR = 0.60; 0.42-0.86; P = 0.0006) and with severe/profound HL (OR = 0.52; 0.35-0.76; P = 0.001).

Conclusion: Recognizing genetic and epigenetic biomarkers and their mutual interactions in SSNHL is of great value and can help pharmacy science to design by pharmacogenomic data classical or advanced molecules, such as epidrugs, to target new pathways for a better prognosis and treatment of SSNHL.

Keywords: Epidrugs; Epigenetics; Epigenomics; Iron; LINE-1 methylation; Oxidative stress; Pharmacogenetics; Pharmacogenomics; SSNHL.

Fig. 1

Correlation analysis in the whole cohort of patients. Scatter plots of the correlation between PTA and LINE-1 methylation (A), age and PTA (B), age and LINE-1 methylation (C). Variables were centered and scaled as described in Materials and Methods section. Each panel shows the specific regression line in red and the R² coefficient

Fig. 2

Correlation analysis in the whole cohort of patients stratified by SLC40A1 gene variant. Scatter plots of the correlation between PTA and LINE-1 methylation (A), age and PTA (B), age and LINE-1 methylation (C). Variables were centered and scaled as described in Materials and Methods section. Each panel shows the specific regression lines, according to the indicated SLC40A1 genotype

Fig. 3.

3D plot of principal component analysis for the computed 10 variables: PC1, PC2 and PC3 loadings. Plotted by SPSS (Statistics version 22)

Fig. 4.

3D plot of principal component analysis for the computed 9 variables: PC1, PC2 and PC3 loadings. Plotted by SPSS (Statistics version 22)