Submitted Abstract
Meniere disease (MD) is a rare disorder of the inner ear, characterized by sensorineural hearing loss, episodic vertigo and tinnitus associated with an accumulation of endolymph in the cochlea and vestibular organs. Antigen exposure can trigger an allergic response with elevation of arginine-vasopressin levels and increase endolymphatic pressure. Clinical heterogeneity is observed and several subgroups have been described using cluster analyses, according to the associated comorbidities. Evidences from epidemiology and clinical studies suggest a genetic susceptibility involving multiple genes with an autoimmune and neuroinflammatory background. Although the genetic architecture of MD is not known, exome sequencing studies have described novel and rare single nucleotide variants (SNVs) in five pedigrees with autosomal dominant familial MD in FAM136A, DTNA, THAP1, SEMA3D, DPT and PRKCB genes, confirming genetic heterogeneity with variable expressivity in the phenotype.However, the identification of driver genes in sporadic MD remains to be established. Since neuroinflammation seems to be a hallmark of different neurological diseases, including MD and Parkinson´s disease (PD), a collaboration with LCSB was started in 2016, to use whole genome sequencing (WGS) as well as computational and systems biology approaches for a cross-disease comparison of Meniere and PD. The aim of the INTER-project is to bring together the disease specific know-how of the incoming researcher with the computational and systems analysis expertise of LCSB researchers to outline for a computationally readable MD-disease network that integrates experimental data, text-mining results and clinical data in order to facilitate the identification of molecular targets for therapy.The specific objectives of the project involve:1. Design a tool for deep phenotyping of patients with MD using Human Phenotype Ontology (HPO).2. Analysis of 40 whole exome and 5 WGS MD-family datasets to define new candidate genes.3. Design a target-sequencing panel including candidate genes for molecular diagnosis of 960 sporadic cases.4. Develop a first version of a disease map for MD integrating driver and modifier genes with clinical subgroups to define molecular targets for therapy.