Compensatory interplay between clarin-1 and 1 clarin-2 deafness-associated proteins govern phenotypic variability in hearing

Usher syndrome type III (USH3), caused by mutations in CLRN1 encoding clarin-1, presents with progressive hearing loss, vestibular dysfunction, and retinitis pigmentosa, with striking phenotypic variability even among patients sharing identical mutations. Clarin-1's paralog, clarin-2 (encoded by CLRN2), is similarly implicated in hearing loss, suggesting potential functional interplay between these proteins. To investigate this relationship, we conducted RNA-sequencing of cochlear tissues from Clrn1-/-, Clrn2-/-, and Clrn1-/-Clrn2-/- double-knockout mice, revealing that clarin-1 and clarin-2 cooperatively regulate essential auditory processes, including mechanoelectrical transduction, ionic homeostasis, and synaptic organization. The double knockout mice exhibited synergistic disruption of these pathways, resulting in more severe hearing deficits than either single knockout, demonstrating that these proteins functionally compensate for one another. These findings suggest that CLRN2 variants may influence hearing outcomes in USH3 patients, supporting the integration of CLRN2 analysis into genetic diagnostics. By demonstrating that clarin-1 and clarin-2 share overlapping functional roles in the inner ear, this study redefines USH3 as a network-dependent disorder, offering new insights for therapeutic development. The RNA-seq dataset provides a comprehensive resource for exploring gene interactions in auditory function and advancing precision medicine approaches for hearing loss. Overall design: At post-natal day 21 wild-type, Clrn1-/-, Clrn2-/-, and Clrn1-/-Clrn2-/- mice were injected intraperitoneally with a lethal cocktail of Ketamine and Xylazine and inner ears collected in ice-cold 1X HBSS (ThermoFisher, 24020117). Organs of Corti were quickly dissected out of the cochlea, removing the stria vascularis and modulus, and collected in RNAs-free 2mL micro tubes to be immediately flash frozen in liquid nitrogen. Organs of Corti were collected in triplicate and kept at -80°C until RNA extraction. RNA extraction and purification was performed using the NucleoSpin RNA, Mini kit for RNA purification (Macherey-Nagel, 740955.50), following manufacturer instructions with slight modifications in quantities of reagents used. RNA samples were measured with NanoDrop to assess RNA concentration and determine if there was any protein or ethanol contaminations. 2?g RNA samples were sent for RNA-sequencing at Integragen. RNA libraries were prepared with NEBNext Ultra II Directional RNA Library Prep Kit for Illumina protocol according supplier recommendations. Briefly, the key stages of the protocol are successively: the purification of Poly-A containing mRNA molecules using poly-T oligo attached magnetic beads from 100ng total RNA (with the Magnetic mRNA Isolation Kit from NEB), a fragmentation using divalent cations under elevated temperature to obtain approximately 300bp pieces, double strand cDNA synthesis and finally Illumina adapters ligation and cDNA library amplification by PCR for sequencing. Sequencing is then carried out on Paired End 100b reads of Illumina NovaSeq 6000. Image analysis and base calling was performed using Illumina Real Time Analysis (3.4.4) with default parameters. Gene expression was quantified using Integragen's Galileo software. STAR was used to obtain the number of reads associated to each gene in the Gencode vM24 annotation (restricted to protein-coding genes, antisense and lincRNAs). Raw counts for each sample were imported into R statistical software using the Bioconductor DESeq2 package. Extracted count matrix was normalized for library size and coding length of genes to compute FPKM expression levels. Unsupervised analysis of data used the Bioconductor edgeR package to import raw counts into R statistical software and compute normalized log2 CPM (counts per millions of mapped reads) using the TMM (weighted trimmed mean of M-values) as normalization procedure. The normalized expression matrix from the 1000 most variant genes (based on standard deviation) was used to classify the samples according to their gene expression patterns using principal component analysis (PCA). PCA was performed by FactoMineR::PCA function with “ncp = 10, scale.unit = FALSE” parameters. The Bioconductor edgeR package was used to import raw counts into R statistical software for differential expression analysis of clarin mutant mice relative to wild-type controls. Differential expression analysis was performed using the Bioconductor limma package and the voom transformation. To improve the statistical power of the analysis, only genes expressed in at least one sample (FPKM ? 0.1) were considered. A q-value threshold of ? 0.05 and a minimum fold change of 1.5 were used to define differentially expressed genes.