Immediate-Early Modifications to the Metabolomic Profile of the Perilymph Following an Acoustic Trauma in a Sheep Model

Abstract

The pathophysiological mechanisms of noise-induced hearing loss remain unknown. Identifying biomarkers of noise-induced hearing loss may increase the understanding of pathophysiological mechanisms of deafness, allow for a more precise diagnosis, and inform personalized treatment. Emerging techniques such as metabolomics can help to identify these biomarkers. The objective of the present study was to investigate immediate-early changes in the perilymph metabolome following acoustic trauma. Metabolomic analysis was performed using liquid chromatography coupled to mass spectrophotometry to analyze metabolic changes in perilymph associated with noise-induced hearing loss. Sheep (n = 6) were exposed to a noise designed to induce substantial hearing loss. Perilymph was collected before and after acoustic trauma. Data were analyzed using univariate analysis and a supervised multivariate analysis based on partial least squares discriminant analysis. A metabolomic analysis showed an abundance of 213 metabolites. Four metabolites were significantly changed following acoustic trauma (Urocanate (p = 0.004, FC = 0.48), S-(5'-Adenosyl)-L-Homocysteine (p = 0.06, FC = 2.32), Trigonelline (p = 0.06, FC = 0.46) and N-Acetyl-L-Leucine (p = 0.09, FC = 2.02)). The approach allowed for the identification of new metabolites and metabolic pathways involved with acoustic trauma that were associated with auditory impairment (nerve damage, mechanical destruction, and oxidative stress). The results suggest that metabolomics provides a powerful approach to characterize inner ear metabolites which may lead to identification of new therapies and therapeutic targets.

Keywords: acoustic trauma; hearing loss; metabolomic; perilymph.

Figure 1

Study design: (1) Sheep under general anesthesia. (2) Perform BC-ABR to confirm NH status in NH ear. (3) Sample perilymph from NH ear. (4) 60 min of impulse noise exposure at 120 dB SPL. (5) Perform BC-ABR to confirm SNHL status in NIHL ear. (6) Sample perilymph from NIHL ear. (7) Sheep euthanized. The polyethylene tube represents the perilymph sample, blood tube represents the serum.

Figure 2

Statistical analysis to compare the metabolomic profile of perilymph fluid from sheep before and after acoustic trauma. (A): Multivariate analysis using partial least squares discriminant analysis (PLSDA) to distinguish metabolomic profile of perilymph fluid from sheep before (red circles) and after acoustic trauma (green circles). Components 1 and 2 represent a linear combination of relevant metabolites expressing maximum variance. (B): The rank of different metabolites (top 15) identified by PLSDA according to the VIP (Variable Influence of Projection) score on the left. The colored boxes on the right indicate the relative concentrations of the corresponding metabolite in each group studied. (C): Univariate analysis via a plot based on fold change and p-value, highlighting 4 metabolites. The volcano plot based on the comparison between NH control and NIHL perilymph samples, highlighting metabolites characterized by a FC > 1.2 concentration ratio and a t-test (y) < 0.1 (pink points). Note that fold changes and p-values are log transformed. The further away from (0.0) position, the more important the feature.

Figure 3

Metabolomic Pathway Analysis based on the 15 VIP metabolites the most discriminant in the multivariate model explaining hearing loss. The circles resume all matched pathways according to the p values from the pathway enrichment analysis and pathway impact values from the pathway topology analysis (calculated as the sum of importance measures of the matched metabolites normalized by the sum of the importance measures of all metabolites in each pathway). Each circle represents a metabolite set with its color based on its p-value (darker colors indicate more significant changes in metabolites belonging to the corresponding pathway), and its size is based on pathway impact score. The most impacted pathways are annotated.