Computational analysis of mRNA expression profiling in the inner ear reveals candidate transcription factors associated with proliferation, differentiation, and deafness

Abstract

BackgroundHearing loss is a major cause of disability worldwide, impairing communication, health, and quality of life. Emerging methods of gene therapy aim to address this morbidity, which can be employed to fix a genetic problem causing hair cell dysfunction and to promote the proliferation of supporting cells in the cochlea and their transdifferentiation into hair cells. In order to extend the applicability of gene therapy, the scientific community is focusing on discovery of additional deafness genes, identifying new genetic variants associated with hearing loss, and revealing new factors that can be manipulated in a coordinated manner to improve hair cell regeneration. Here, we addressed these challenges via genome-wide measurement and computational analysis of transcriptional profiles of mouse cochlea and vestibule sensory epithelium at embryonic day (E)16.5 and postnatal day (P)0. These time points correspond to developmental stages before and during the acquisition of mechanosensitivity, a major turning point in the ability to hear.ResultsWe hypothesized that tissue-specific transcription factors are primarily involved in differentiation, while those associated with development are more concerned with proliferation. Therefore, we searched for enrichment of transcription factor binding motifs in genes differentially expressed between the tissues and between developmental ages of mouse sensory epithelium. By comparison with transcription factors known to alter their expression during avian hair cell regeneration, we identified 37 candidates likely to be important for regeneration. Furthermore, according to our estimates, only half of the deafness genes in human have been discovered. To help remedy the situation, we developed a machine learning classifier that utilizes the expression patterns of genes to predict how likely they are to be undiscovered deafness genes.ConclusionsWe used a novel approach to highlight novel additional factors that can serve as points of intervention for enhancing hair cell regeneration. Given the similarities between mouse and human deafness, our predictions may be of value in prioritizing future research on novel human deafness genes.Electronic supplementary materialThe online version of this article (10.1186/s40246-018-0161-7) contains supplementary material, which is available to authorized users.

A rare genomic duplication in 2p14 underlies autosomal dominant hearing loss DFNA58

“…The duplication in DFNA58 patients includes two entire known protein-coding genes, PLEK and CNRIP1, and the first exon of another known protein-coding gene PPP3R1. Our results are consistent with bioinformatics prediction of deafness genes based on inner ear expression levels: Using a classifier algorithm that utilizes the genome-wide transcriptional expression patterns of genes differentially expressed between mouse cochlear and vestibular sensory epithelia and between developmental ages, PPP3R1, CNRIP1 and PLEK have been predicted as deafness genes with probabilities of 1.52%, 1.33% and 1,31%, respectively, probabilities similar to many known deafness genes (SLC22A4-1.53%, PRPS1-1.41%, DIAPH1-1.18%, HGF-1.02%; DFNA5-0.95%, DIAPH3-0.89%, NARS2-0.75%) (15).…”

“…Challenges remain not only in identifying candidate variants, but also in finding many affected individuals with variants in the same gene, either from a single large family or from different small ones, and in performing proper functional studies that give support to the pathogenic nature of those variants. In addition, given that the causal gene of at least 44 mapped loci have not been characterized (11), and also given that computational predictions estimate that only half of the human deafness genes are already known (15), it is evident that more crucial genes for auditory function are to be revealed.…”

gEAR: Gene Expression Analysis Resource portal for community-driven, multi-omic data exploration

“…High-profile, hearingrelated datasets have been made available in the gEAR even before publication, and links to gEAR as the primary method for accessing datasets are ubiquitous within talks/posters at professional conferences. Additionally, the gEAR has already been used for data dissemination 5,[23][24][25][26][27][28][29][30][31][32][33][34][35] , hypothesis generation [36][37][38][39][40][41][42][43][44] and validation of gene expression 29,30,[45][46][47][48][49][50][51][52][53][54][55][56][57] by numerous publications in the ear field. It has also been referenced by numerous groups as a useful tool [58][59][60][61][62][63][64] .…”

Defining the Inflammatory Microenvironment in the Human Cochlea by Perilymph Analysis: Toward Liquid Biopsy of the Cochlea

“…Nevertheless, significantly higher concentrations of IL-13 and IL-9 were detected in perilymph of patients with complete loss compared to residual hearing, which would at least argue for an impact of Th2 and Th9 cytokines. Both cytokines can be induced by various stimuli including toxic substances via the aryl hydrocarbon receptor (AhR) that has recently been shown to act as transcription factor during development of the inner ear (60).…”