Characterization of Usher Syndrome Cell line Genotypes and Elucidation of Novel MicroRNA Biomarkers using MicroRNA Microarray and Droplet Digital PCR

Characterization of Usher Syndrome Cell line Genotypes and Elucidation of Novel MicroRNA Biomarkers using MicroRNA Microarray and Droplet Digital PCR | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Characterization of Usher Syndrome Cell line Genotypes and Elucidation of Novel MicroRNA Biomarkers using MicroRNA Microarray and Droplet Digital PCR Wesley Tom, Dinesh S. Chandel, Chao Jiang, Gary Krzyzanowski, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3826668/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Usher syndrome (USH) is an inherited disorder characterized by sensorineural hearing loss (SNHL), retinitis pigmentosa (RP)-related vision loss, and vestibular dysfunction. USH presents itself as three distinct clinical types 1, 2 and 3, with no biomarker for early detection. This study aimed to explore novel microRNA (miRNA) biomarkers for USH by comparing miRNA expression patterns in cell lines derived from USH patients and control subjects. Methods: Lymphocytes from USH patients and healthy individuals were isolated and transformed into stable cell lines using Epstein-Barr virus (EBV). DNA from these cell lines was sequenced using a targeted panel to identify gene variants associated with USH types 1, 2, and 3. Microarray analysis was performed on RNA from both USH and control cell lines using NanoString miRNA microarray technology. Dysregulated miRNAs identified by the microarray were validated using droplet digital PCR technology. Results: DNA sequencing revealed that two USH patients had USH type 1 with gene variants in USH1B (MYO7A) and USH1D (CDH23), while the other two patients were classified as USH type 2 (USH2A) and USH type 3 (CLRN-1), respectively. The NanoString miRNA microarray detected 92 differentially expressed miRNAs in USH cell lines compared to controls. Significantly altered miRNAs exhibited at least a twofold increase or decrease with a p value below 0.05. Among these miRNAs, 20 were specific to USH1, 14 to USH2, and 5 to USH3. Three miRNAs that are known as miRNA-183-family which are crucial for inner ear and retina development have been significantly down regulated as compared to control cells. Subsequently, droplet digital PCR assays confirmed the dysregulation of twelve most prominent miRNAs in USH cell lines. Conclusion: This study identifies several miRNAs with differential expression in USH patients and their potential utility as biomarkers for Usher syndrome. Health sciences/Biomarkers Health sciences/Molecular medicine Usher syndrome Biomarkers miRNAs Micro-array Droplet digital PCR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Usher syndrome (USH) represents an autosomal recessive inherited disorder that profoundly impacts hearing, vision, and balance. It is characterized by the triad of sensorineural hearing loss (SNHL), vision impairment due to retinitis pigmentosa (RP), and vestibular dysfunction. The prevalence of USH ranges from 4 to 17 cases per 100,000 individuals [ 1 – 3 ]. USH is clinically categorized into three distinct types, distinguished by the age of onset of hearing and visual impairments, the severity of hearing loss, and the presence of balance issues. USH type 1 (USH1) is marked by early-onset SNHL, RP-related vision loss, and balance problems that commence within the first decade of life. USH type 2 (USH2) is characterized by the onset of mild to moderate non-progressive SNHL and RP between the ages of 10 and 20, accompanied by normal vestibular function. USH type 3 (USH3) involves progressive SNHL, sometimes accompanied by balance disturbances, with a variable onset time for SNHL and RP. USH presents as a clinically and genetically diverse condition with nine confirmed causative genes. While traditionally considered a monogenic disorder, recent studies have unveiled instances of digenic inheritance [ 4 , 5 ]. Mutations in eight genes, including MYO7A, USH1C, CDH23, PCDH23, USH1E, USH1G, USH1K, and CIB2/DFNB48, are responsible for USH1 [ 1 ]. USH2 results from genetic mutations in USH2A, GPR98 (also known as ADGRV1), and WHRN (also known as DFNB31) [ 1 ], while USH3 is attributed to mutations in CLRN-1, HARS, and PDZD7 genes [ 1 ]. MicroRNAs (miRNAs) are small single-stranded non-coding RNA molecules spanning 21 to 23 nucleotides in length, play pivotal roles in post-transcriptional gene expression regulation. These miRNA molecules are integral regulators of cellular homeostasis in both normal physiological and pathological processes [ 6 ]. Extensive research has underscored the significance of miRNAs in the visual, auditory, and vestibular systems [ 7 – 11 ]. Specifically, the microRNA-183 family, encompassing miRNA-183, miRNA-182, and miRNA-96, is highly conserved and indispensable for the development and maturation of sensory organs [ 12 ]. This family of miRNAs plays crucial roles in maintaining normal growth and population of hair cells and neurons within the inner ear [ 13 , 14 ]. These are also vital for the maturation of photoreceptor cells [ 15 , 16 ] since disruptions in the miRNA-183 family has been implicated in syndromic retinal degradation and substantial vestibular impairments [ 17 ]. As growing miRNA research continues to elucidate their important roles in inner ear development and hearing loss, differentially expressed miRNAs thus may have biomarker potential with future applications in diagnostics and treatment to restore hearing function [ 14 ]. However, defining precise roles and targets of individual miRNAs involved in neurosensory pathways in the inner ear remains a major challenge. The miRNA studies on hearing loss have relied mostly on animal models due to inaccessibility of the human inner ear. The present study was designed to investigate miRNA expression patterns in USH cell lines (derived from Usher patients) and compare them with those in control cell lines, aiming to identify potential miRNA biomarkers for Usher syndrome. Methods Cell lines Three of the cell lines utilized in this study, namely D3741 (USH1), D3739 (USH1), and D2880 (USH3), were originally established by Dr. William J. Kimberling's laboratory at Boys Town National Research Hospital in Omaha, Nebraska, USA. These cell lines were created through the infection of lymphocytes obtained from individuals with Usher syndrome (USH) using Epstein-Barr virus (EBV) derived from the B95-8 cell line. Informed consent was obtained from all donors prior to blood draw, and the study was approved by the Institutional Review Board at Boys Town National Research Hospital (IRB#96-06-0X). As part of our control group, lymphocytes were also isolated from healthy donors and subsequently immortalized using EBV. In addition, a lymphocyte cell line corresponding to Usher syndrome type 2A (USH2A) was procured from the Coriell Institute (Catalog ID: GM09053, Camden, New Jersey). These cell lines were cultivated in RPMI 1640 medium, supplemented with 20% fetal bovine serum (FBS) and 50 µg/mL of gentamicin. Cultures were maintained in 100 ⅹ 20-mm tissue culture plates in a humidified atmosphere containing 5% CO2, at 37°C. DNA extraction and shearing Genomic DNA was extracted from cultured cells employing the QIAamp DNA Mini kit (Cat. No. 56304), following manufacturer’s recommended protocol. Purified DNA was quantified using the "Qubit dsDNA BR Assay Kit" on a Qubit 4.0 fluorometer. To attain an optimal fragment size conducive of downstream applications, the genomic DNA was sheared using a Covaris M220 sonicator (Covaris LLC., Woburn, MA). Shearing resulted in fragments of approximately 250 bp. The effectiveness of the shearing process was confirmed through fragment size analysis using a D1000 Tapestation (P/N: 5067–5583, 5067–5582, Agilent Technologies). For each specific cell line under investigation (USH1B, USH1D, USH2A, and USH3A), 200 ng of sheared genomic DNA prepared in a final volume of 50.0 µL 0.1X-TE buffer, was used for targeted exon sequencing. Targeted Exon Sequencing A custom exon probe panel targeting coding regions of 26 genes associated with syndromic and non-syndromic hearing loss was created using Agilent’s SureDesign probe design tool (Agilent Technologies, Santa Clara CA). In total, 5740 biotinylated mRNA probes were synthesized covering the coding regions of 26 genes of interest (200.345 Kbp total length) and used in a hybridization and capture approach for enrichment of target DNA. Probes covered exonic regions including a 25bp extension at 3’ and 5’ UTRs. Coding regions with > 40bp gaps in coverage received custom boosting and tiling strategies to ensure sufficient sequencing coverage. Genes covered in the panel include: ABHD12, ADGRV1, ARSG, CDH23, CIB2, CLRN1, ESPN, FOXI1, GJB2, GJB6, HARS1, KCNE1, KCNJ10, KCNQ1, MYO7A, PCDH15, PDZD7, SLC26A4, USH1C, USH1G, USH2A, WHRN, CEP250, and CEP78. Libraries for the hearing loss exon capture were prepared using Agilent’s SureSelect XT HS2 DNA with a Post-capture pooling protocol, as per manufacturer’s instructions (P/N: G9985D, Agilent Technologies, Santa Clara CA). Briefly, 200ng extracted DNA in a total volume of 50 µL 0.1X TE buffer, sheared to ~ 250bp with a Covaris M220 sonicator, was used as the starting input for library preparation. Fragmented DNA samples underwent enzymatic end-repair followed by a dA-tail ligation. Sequencing adapters were ligated to dA-overhang and each cell line received a unique dual indexed primer pair with unique molecular indices. An 8 cycle PCR amplification of adapter ligated libraries was performed under the following conditions; 1 Cycle: 98ºC (2 min), 8 Cycles: 98ºC (30 sec), 60ºC (30 sec), 72ºC (1 min), 1 Cycle: 72ºC (5 min). Indexed libraries were then hybridized with a biotinylated custom HL probe panel, and DNA-probe hybrids were captured using streptavidin beads (P/N: 65601, Thermo Fisher Scientific). Targeted libraries were purified using AMPure XP (P/N: A63880, Beckman Coulter Genomics) 1X bead clean up, and library quality was assessed using a D1000 High Sensitivity Tapestation assay (P/N: 5067–5585, 5067–5584, Agilent Technologies). Post-capture libraries were diluted to equimolar concentrations and pooled for sequencing on an Illumina NextSeq 550DX 300 cycle high output flow-cell, 150bp paired end reads (P/N: 20024908, Illumina inc.). Targeted exon sequencing resulted in samples- USH3A (695.41Mbp), USH1B (712.23Mbp), USH1D (796.93Mbp), USH2A (767.00Mbp), with an average of 20.04 million reads per sample. Variant Calling and Interpretation Reads for each cell line were aligned and mapped to the human reference genome (GRCh38p.14) [ 18 ] using BWA and Samtools, respectively [ 19 , 20 ]. Sequence variants were called using Sentieon’s DNA pipeline for variant detection [ 21 ]. Sequencing variant interpretation according to ACMG classification criteria were applied using VarSeq software version 2.4.0 (Golden Helix, Inc., Bozeman MT, https://www.goldenhelix.com ) [ 22 , 23 ]. RNA extraction and miRNA Expression Assay Briefly, total RNA was extracted from cell lines using QIAzol® reagent (cat. # 79306), followed by purification using the miRNeasy Tissue/Cells Advanced Micro Kit (cat. #217684) protocol as per manufacturer’s instructions (QIAGEN Sciences Inc., Germantown, MD). Purified total miRNA from cell lines (100 ng miRNA per sample) was used as input for miRNA expression analysis. miRNA expression in Usher and control cell lines was quantified using NanoString© Human v3 miRNA assay (cat. # CSO-MIR3-12), performed on the nCounter Pro analysis system (NanoString Technology, Seattle, WA, USA). The assay detects 798 known human miRNAs, where each miRNA has specific tag sequences ligated with fluorescently barcoded reporter probes. After a hybridization period of 16h at 65℃, these miRNA-specific barcodes were detected by the nCounter Digital Analyzer providing miRNA copy numbers. Raw count data from the miRNA assay was normalized using the NanoString quality control dashboard (NACHO version 2.0.0) package in R [ 24 , 25 ]. There are no well-established “housekeeping” miRNAs, so NACHO’s housekeeping predict = TRUE function was used to select the top five housekeeping miRNA candidates directly from the NanoString assay. The miRNA count data was normalized relative to internal positive, negative, and housekeeping predictions using a geometric mean, normalization method = “GEO” [ 25 ]. The resulting normalized count table was used for downstream analysis in R. Absolute quantification of miRNAs by Droplet Digital PCR (ddPCR) Following miRNA-panel screening by NanoString microarray, 12 miRNAs (6-up and 6-down regulated) with significant differential expression patterns were selected for dd-PCR analysis. For dd PCR, equal starting RNA amount (10 ng) from each sample was first converted into cDNA by reverse transcription (RT) using miRNA specific primer sets (Life Technology Corp., CA, USA), following TaqMan™ RT- kit protocol (Applied Biosystems Cat.#4366596). The cDNA/RT reaction was carried out in a total volume of 15µL consisting of 2 µL of RNA template (5ng/µL), 3 µL of RT-specific miRNA primers (5x), 0.15 µL dNTP-mix (25mM each), 0.19 µL of RNAse-H, 1µL of Multiscribe™ reverse transcriptase enzyme (50U/µL), 1.5 µL of RT-buffer (10x) with a final reaction volume adjusted with nuclease free water (7.16 µL). RT reactions were performed on a PCR cycler (cfx1000, BioRad) with a set parameter of 16⁰C for 30m, 42⁰C for 30m, 85⁰C for 5 m and an infinite hold at 4⁰C. The resulting cDNA products were diluted based on relative abundance of individual miRNAs. The standard ddPCR reaction was performed in a 20 µL reaction volume by adding 10 µL of Bio-Rad 2x ddPCR Supermix for probes, 2.0 µL of diluted cDNA, 1.0 µL of individual miRNA-specific primer-probe mix and reaction volume adjusted to 20 µL by nuclease free water. Droplet digital PCR was performed using Bio-Rad Automated QX200 droplet digital PCR system as previously described [ 26 ]. Micro-RNA copy numbers per 1ng RNA was calculated using the equation given below: Statistical Analysis Normalized count data was imported into R statistical analysis software where all subsequent microarray analysis was performed [ 24 ]. Usher-1D and Usher-1B cell lines had three replicates in the NanoString assay. Usher-2A, Usher-3A, and the control cell lines had 4 replicates each. Differentially expressed (DE) miRNA analysis was performed using the “DESeq2” R package [ 19 ]. Venn diagrams depicting overlapping and unique miRNAs from DESeq2 differential analysis was performed using the ggvenn package [ 27 ]. All pairwise contrasts of phenotype (Usher-1B, Usher-1D, Usher-2A, Usher-3A, and NBT) were considered for differential miRNA expression analysis, and significance assigned at a Benjamini-Hochberg (BH) corrected p-value < 0.05. Heatmaps of top differential miRNAs from pairwise genotype and phenotype contrasts were created using “ComplexHeatmap” package in R. For heatmaps, expression count data was scaled for large differences in variation using the formula z i =(x i -u i )/s i , where x i is the count for miRNA i , u i is the mean for miRNA i , and s i is the standard deviation of miRNA i [ 28 ]. Samples were grouped using hierarchical clustering according to miRNA expression patterns [ 28 ]. Principal component analysis (PCA) was performed using a Bray-Curtis dissimilarity matrix calculated from normalized count data, and principal components were visualized with the “microViz” package [ 21 ]. Significant differences between miRNA profiles were performed using a permutational multivariate analysis of variance (PERMANOVA) on Bray-Curtis dissimilarity distances, with the adonis2 function in R’s “vegan” package [ 22 , 23 ]. The PERMANOVA used a generalized linear model considering the effect of phenotype (model formula: Bray-Curtis dissimilarity matrix ~ Phenotype). For ddPCR analysis, a one-way ANOVA with a Tukey test for pairwise mean comparisons was used to determine differential expression between all cell lines where statistical significance was assigned at p value A missense mutation in the CDH23 gene, thus attributing to the USH1D genotype. This genotype is consistently associated with USH1 where the variant creates a novel splice acceptor site which results in an in-frame deletion of 51 base pairs, removing a calcium binding motif of the protein [ 3 , 23 , 29 – 38 ]. The D3741 cell line contains a homozygous likely pathogenic G > T missense mutation in the MYO7A gene. Therefore, D3741 cell line was categorized under USH1B genotype. This variant has been detected both as a homozygous mutation and compound heterozygous mutation. The USH1B cell line possesses a likely pathogenic homozygous missense variant in the MYO7A gene c.2905G > T (p.Glu968Asp) which is predicted to cause splice site variation in the myosin VIIA gene [ 33 , 36 , 39 – 47 ]. In cell line D2880, a homozygous variant in CLRN1 gene was detected at c.528A > C (p.Tyr176X) thus categorized as USH3A. This mutation causes a premature stop codon, truncating the clarin-1 protein [ 48 – 51 ]. Interestingly, the USH2A cell line has a likely compound heterozygous mutation phenotype with two variants in the USH2A gene. The first variant- USH2A c.4338_4339del (p.Cys1447fs) is a frameshift variant which is predicted to cause a premature stop codon [ 3 , 52 – 61 ]. The second heterozygous variant has yet to be functionally confirmed. It occurs in USH2A c.14787del (p.Glu4930fs) and is computationally predicted to cause premature truncation or nonsense-mediated decay in the production of usher protein [ 53 , 54 , 59 , 60 ]. miRNA microarray analysis Normalized microarray data were further analyzed using DESeq2 to identify miRNAs that were differentially expressed in USH cell lines compared to control cells. At least a twofold increase or decrease with a BH-adjusted p value < 0.05 was considered as significantly altered miRNA expression. Using this criterion, we found 92 differentially expressed miRNAs in USH cells. Of these, 20 were unique to USH1, 14 to USH2, and 5 to USH3, 2 were unique to both USH1 and USH2, 5 to USH1 and USH3, and 10 to USH2 and USH3. The remaining 36 were identified as common to all USH types (Fig. 1 and Table 2 ). Table 2 Differentially expressed miRNAs from microarray using DESeq2. A table of all differentially abundant miRNAs by cell lines, which are broken down into the following categories: Common to all (differential miRNAs that are dysregulated in Usher cell lines compared to controls), Usher 1 only (miRNAs uniquely dysregulated only in Usher 1 cell lines compared with controls), Usher 2 only(miRNAs uniquely dysregulated only in Usher 2 cell lines compared with controls), Usher 3 only(miRNAs uniquely dysregulated only in Usher 3 cell lines compared with controls), Usher 1 and 2(miRNAs dysregulated in both Usher 1 and 2 compared to controls), Usher 1 and 3(miRNAs dysregulated in both Usher 1 and 3 compared to controls), Usher 2 and 3(miRNAs dysregulated in both Usher 2 and 3 compared to controls). Number Differentially abundant microRNAs Common To All 36 hsa-miR-222-3p, hsa-miR-424-5p, hsa-miR-503-5p, hsa-miR-1252-5p, hsa-miR-1246, hsa-miR-182-5p, hsa-miR-1205, hsa-miR-450a-5p, hsa-miR-654-5p, hsa-miR-3934-5p, hsa-miR-221-5p, hsa-miR-34a-5p, hsa-miR-150-5p, hsa-let-7d-5p, hsa-miR-483-3p, hsa-miR-146b-5p, hsa-miR-5010-3p, hsa-miR-591, hsa-miR-155-5p, hsa-miR-363-3p, hsa-let-7i-5p, hsa-miR-194-5p, hsa-miR-28-3p, hsa-miR-10a-5p, hsa-miR-146a-5p, hsa-miR-337-3p, hsa-miR-200c-3p, hsa-miR-96-5p, hsa-miR-5196-3p + hsa-miR-6732-3p, hsa-let-7f-5p, hsa-miR-577, hsa-let-7g-5p, hsa-miR-183-5p, hsa-let-7b-5p, hsa-miR-148a-3p, hsa-miR-98-5p Usher 1 Only 20 hsa-miR-151a-5p, hsa-miR-129-2-3p, hsa-miR-519d-3p, hsa-miR-4431, hsa-miR-345-5p, hsa-miR-152-3p, hsa-miR-1260b, hsa-miR-3916, hsa-miR-4455,hsa-miR-3195, hsa-miR-195-5p, hsa-miR-1226-3p, hsa-miR-151a-3p, hsa-miR-181b-2-3p, hsa-miR-1287-5p, hsa-miR-324-5p, hsa-miR-484, hsa-miR-16-5p, hsa-miR-331-3p, hsa-miR-26b-5p Usher 2 Only 14 hsa-miR-513b-5p, hsa-miR-137, hsa-miR-370-3p, hsa-miR-503-3p, hsa-miR-767-5p, hsa-miR-132-3p, hsa-let-7c-5p, hsa-miR-1304-5p, hsa-miR-514a-3p, hsa-let-7a-5p, hsa-miR-342-3p, hsa-miR-181a-5p, hsa-miR-181c-5p, hsa-miR-1193 Usher 3 Only 5 hsa-miR-125a-3p, hsa-miR-494-3p, hsa-miR-125b-5p, hsa-miR-4284, hsa-miR-181a-2-3p Usher 1 and 2 2 hsa-miR-551b-3p, hsa-miR-299-3p Usher 1 and 3 5 hsa-miR-371b-5p, hsa-miR-601, hsa-miR-1827, hsa-miR-27a-3p, hsa-miR-582-5p Usher 2 and 3 10 hsa-miR-574-3p, hsa-miR-223-3p, hsa-miR-1183, hsa-miR-28-5p, hsa-miR-542-5p, hsa-let-7e-5p, hsa-miR-23c, hsa-miR-221-3p, hsa-miR-374b-5p, hsa-miR-23a-3p PERMANOVA analysis of miRNA microarray data confirmed that miRNA expression profiles were significantly different between all cell lines with a BH-adjusted p -value of 0.001 (Fig. 2 ). Results obtained using PERMANOVA were corroborated in PCA analysis, showing distinct clustering by cell line. However, USH2A and USH3A clustered more similarly compared to controls or USH1 phenotypes, and a total of 94.8% of the variation in the miRNA expression profile dataset explained in the ordination of the first two principal components (Fig. 2 ). Additionally, of the top 6 miRNAs driving variation between samples, hsa-miR-155-5p, hsa-miR-142-3p, and hsa-let-7a-5p were differentially expressed among Usher cell lines, particularly USH2A and USH3A samples. Variation in levels of hsa-miR-16-5p and hsa-miR-19b-3p was assigned to the control cell lines. The hsa-miR-4454 + hsa-miR-7975 was different in one of the USH2A sample replicates. Table 3 provides a list of top 12 differentially expressed miRNAs in USH cells compared to controls. The heatmap (Fig. 3 ) shows differential expression of these miRNAs uniquely assigned to USH and control cell lines. All miRNAs in this list were differentially expressed in the DESeq2 analysis, with a BH-adjusted p -value < 0.05 and had at least a 2-fold change in miRNA expression between Usher cell lines and controls. Additionally, these 12 miRNAs had an average copy number greater than 50 within the dominant sample type. The following six miRNAs, Hsa-miR-16-5p, hsa-miR-19b-3p, hsa-miR-4454 + hsa-miR-7975, hsa-miR-142-3p, hsa-miR-155-5p, and hsa-let-7a-5p were also the top 6 miRNAs explaining high amounts of variation in miRNA profiles in the PCA biplot (Fig. 2 ). Both PCA and heatmap showed USH2 and USH3 grouped together. Control samples clustered together, and USH1B and USH1D samples shared similar miRNA expression profiles (Fig. 3 ). The top 12 miRNAs panel examined includes 6 down-regulated and 6 up-regulated miRNAs. The miRNA-28-5p, miRNA-96-5p, miRNA-182-3p, miRNA-183-3p, miRNA-16-5p and miRNA-19b-3p were downregulated in all USH types compared to controls (Fig. 4 ). According to Fig. 5 A, D & F, miRNA-363-3p, miRNA-155-5p and miRNA-142-3p were upregulated in all USH types. The miRNA-223-3p (Fig. 5 B) was upregulated only in USH2A and USH3A, whereas miRNA-150-5p showed increased expression only in USH2A (Fig. 5 C). MiRNA-let7a-5p is upregulated in USH2A, USH3A and USH1B (Fig. 5 E). However, this change was not evident in USH1D (Fig. 5 E) showing that same phenotype with different genotypes may vary in their miRNA expression patterns. Table 3 Top 12 differentially abundant miRNA biomarker Candidates. A summary table documenting the 12 miRNA biomarker candidates from the study. Log2 fold change (Log2FC) as well as BH-adjusted p-values for each miRNA in each Usher type when compared to the control lines are shown in each row. The final two columns show the average Log2FC and standard deviation of all cell lines. Usher 1 Usher 2 Usher 3 Mean log2FC vs. Control microRNA log2FC P-Value (BH-adj) log2FC P-Value (BH-adj) log2FC P-Value (BH-adj) Mean log2FC Standard Dev. hsa-miR-150-5p 4.8901 0 9.7786 0 6.5893 0 7.086 2.4818 hsa-miR-155-5p 4.15 0 4.6412 0 4.6898 0 4.4936 0.2986 hsa-miR-363-3p 4.1282 0 3.3498 0 3.3819 0 3.62 0.4404 hsa-miR-223-3p 1.6878 0.0002 4.804 0 3.2343 0 3.242 1.5581 hsa-let-7a-5p 0.9413 0.0095 2.0768 0 1.9006 0 1.6396 0.6111 hsa-miR-142-3p 0.5645 0.0259 1.6779 0 1.5316 0 1.258 0.6051 hsa-miR-19b-3p -1.4389 0.0001 -0.9554 0.0293 -1.1786 0.0041 -1.191 0.2419 hsa-miR-28-5p -1.9993 0 -2.5731 0 -2.0932 0 -2.2219 0.3078 hsa-miR-16-5p -2.1845 0 -1.0125 0.0487 -1.3422 0.0044 -1.513 0.6044 hsa-miR-183-5p -2.2771 0 -2.6795 0 -2.3467 0 -2.4344 0.2151 hsa-miR-96-5p -2.9386 0 -3.2701 0 -2.7408 0 -2.9832 0.2674 hsa-miR-182-5p -6.2691 0.0024 -8.0376 0.0005 -6.1735 0.0073 -6.8267 1.0498 miRNA ddPCR analysis The top 12 differentially expressed miRNAs identified after microarray screenings were validated using droplet digital PCR technology. Figure 6 shows the expression of 6 miRNAs that were significantly downregulated in USH cells compared to controls. These miRNAs include miRNA-28-5p, miRNA-96-5p, miRNA-182-3p, miRNA-183-3p, miRNA-16-5-p and miRNA-19b-3p. The first four miRNAs: miRNA-28-5p, miRNA-96-5p, miRNA-182-3p, and miRNA-183-3p showed no statistically significant difference among USH types (Fig. 6 panels A, B, C & D). However, miRNA-16-5-p and miRNA-19b-3p showed variation within USH types: USH1D showed relatively more downregulation than USH2A (Fig. 6 panels E and F). Figure 7 shows the expression of 6 miRNAs that were upregulated in USH cells compared to controls. Expression levels of miRNA-363-3p was significantly high in all USH types except for USH3A (Fig. 7 A). Similarly, miRNA-223-3p was significantly upregulated in all USH types except for USH1B (Fig. 7 B). Statistically significant upregulation of miRNA-150-5p was observed only in USH2A (Fig. 7 C). MiRNA-155-5p, miRNA-let-7a-5p, and miRNA-142-3p were upregulated in all USH types compared to control (Fig. 7 panels D, E & F). However, miRNA-223-3p was upregulated in all USH types except for USH1B (Fig. 7 B). Discussion In this study, we assessed the expression profiles of 798 miRNAs across different Usher and control cell line types, revealing intriguing variations. The Usher cell lines exhibited distinct miRNA expression profiles, as illustrated in Table 2 . These variations were not only evident among Usher type 1, 2, and 3 phenotypes but also discernible between USH1B and USH1D genotypes. According to microarray data (Fig. 2 ), the miRNA expressions in all Usher samples significantly differed from those in the control samples. We hypothesize that these differences in miRNA expression may be further complicated by unique combinations of sequence variants contributing to the phenotype. For instance, in the USH2A cell line, we identified two heterozygous frameshift mutations that appear to have a compounded effect, resulting in the observed phenotype. This compound mutation effect, although somewhat unconventional, has been documented in Usher syndrome, challenging the widely accepted notion that homozygous recessive mutations exclusively underlie the disorder [ 29 , 62 , 63 ]. In contrast, USH1D, USH1B, and USH3A all exhibited homozygous recessive mutations, aligning with the classical definition of autosomal recessive inheritance in Usher patients [ 31 , 41 , 64 ]. The miRNA-183/182/96 family emerged as a key player, showing significant downregulation in all Usher cell lines compared to controls, as evident from both DESeq2 analysis of microarray results (Fig. 4 panels B, C & D) and ddPCR analysis (Fig. 6 panels B, C & D). Previous studies have showcased the significance of these miRNAs, as their inactivation in mice led to significant developmental issues in cochlear hair cells [ 64 ]. Moreover, mutations affecting miRNA-96-5p have been linked to impaired growth, maturation, and, in some cases, malformation of cochlear hair cells in mice and humans [ 66 – 69 ]. Additionally, alterations in the expression of the microRNA-183/182/96 family have been associated with retinal dystrophy- also a component of Usher syndrome [ 16 , 17 , 70 ]. Our findings align with existing literature, supporting the notion that microRNA-183/182/96 downregulation could potentially serve as a diagnostic biomarker for Usher syndrome. Notably, our microarray data revealed an average 2.43 ± 0.22 log2 fold decrease in miRNA-183-3p expression, a 2.98 ± 0.27 log2 fold decrease in miRNA-96-5p expression, and approximately a 6.83 ± 1.05 log2 fold decrease in miRNA-182-5p expression in Usher samples compared to controls (Table 3 ). This downregulation across the entire miR-183/182/96 family was confirmed using ddPCR (Fig. 6 panels B, C & D) where Tukey HSD post hoc tests indicated significantly higher expression of all three miRNAs in control samples compared to Usher lines, with no significant differences observed among Usher lines (Fig. 6 panels B, C & D). Interestingly, we observed a significant downregulation of the miRNA-28-5p in all Usher samples, with an average 2.22 ± 0.31 log2 fold lower expression compared to controls (Table 3 ). Although no prior associations between miRNA-28-5p and Usher syndrome have been reported, Ji et al. (2017) suggested that miRNA-28 may target and regulate the expression of the cone-rod homeobox gene (CRX), making it a potential candidate for retinal degeneration, a component of Usher syndrome [ 71 ]. MiRNA-16-5p emerged as a driver of variation, particularly toward control samples, displaying an average 1.51 ± 0.60 log2 fold higher expression in control samples compared to Usher lines (Table 3 ). Interestingly, some literature links elevated miRNA-16-5p expression to noise-induced hearing loss (NIHL) and even Alzheimer’s disease [ 72 – 74 ]. However, our study indicated low levels of miRNA-16-5p in Usher samples compared to controls, suggesting that the hearing loss caused by Usher and NIHL may have different mechanisms. Six miRNAs, namely miRNA-150-5p, miRNA-155-5p, miRNA-363-3p, miRNA-223-3p, miRNA-let-7a-5p, and miRNA-142-3p, exhibited upregulation in the Usher cell lines compared to the control (Table 3 ). Among these, miRNA-155-5p, miRNA-142-3p, and miRNA-let-7a-5p were among the most abundant miRNAs in the dataset. Notably, miRNA-142-3p made up approximately 37.3% of miRNA copies in Usher samples, compared to 18.5% in the control line (supplementary Figs. 1 and 2). This miRNA displayed an average 1.26 log2 fold higher expression in Usher samples and holds potential as a robust biomarker candidate. While miRNA-142-3p has not been previously associated with Usher or sensorineural hearing loss (SNHL), its downregulation has been observed in the aqueous humor of patients with central retinal vein occlusion (CRVO), a condition occasionally linked to retinitis pigmentosa (RP) [ 75 , 76 ]. Interestingly, our study found miRNA-142-3p to be one of the most abundant miRNAs in all samples, particularly in Usher cell lines (~ 40% of all miRNA copies), which contrasts with observations in CRVO (supplementary Figs. 1 and 2). Additionally, miRNA-Let-7a emerged as a prominent driver of variation in both PCA and DESeq2 analyses, exhibiting an average 1.6 ± 0.61 log2 fold increase compared to control samples (Table 3 ). In Usher samples, miRNA-Let-7a accounted for 2.35% of the total miRNA copies per genotype, while control samples displayed only 0.9%. Analysis of the heatmap revealed that both USH2A and USH3A exhibited the highest levels of miRNA-Let-7a and miRNA-142-3p, while USH1 genotypes displayed lower expression (Fig. 3 ), especially USH1D. This observation is consistent with the trajectory of miR-Let-7a and miR-142-3p vectors in the PCA biplot (Fig. 2 ). In addition to examining miRNA expression patterns, we conducted targeted exome sequencing to identify genotypes and likely pathogenic sequence variants in previously clinically diagnosed Usher patient-derived cell lines (Table 1 ). Furthermore, we validated our microarray observations with ddPCR quantification of the 12 miRNAs, obtaining consistent results when compared to microarray data. These 12 miRNAs, characterized by their consistency across statistical tests and assays (ddPCR and microarray), present a list of potential biomarkers present in lymphocytes that could aid in the diagnosis of USH1B, USH1D, USH2A, and USH3A. Table 1 Sequence Variant Table. Exon capture sequencing variants classified using VarSeq. For each cell line, the phenotype that was clinically diagnosed was verified using NGS targeted exon sequencing where likely pathogenic mutations were documented. Chromosomal location of the mutations, the reference base call, and the sample mutation are all provided. The ACMG classification criteria is also provided as well as the Human Genome Variant Society variant nomenclature. The gene that is affected, zygosity, predicted inheritance, and known associated conditions for all likely pathogenic mutations are all provided. Cell Line D3739 D3741 D2880 Coriell (GM09053) Phenotype Usher-1D Usher-1B Usher-3A Usher-2A Chr:Pos 10:71779316 11:77181589 3:150928107 1:216190280 1:215647526 Ref/Alt G/A G/T A/C AG/- T/- Genotype Homozygous Homozygous Homozygous Heterozygous Heterozygous Classification Likely Pathogenic Likely Pathogenic VUS/Conflicting Pathogenic Pathogenic ACMG Criteria PM2, PS1, PP3 PM2, PP2, PS1, PP3 BS1, PVS1 Strong, PP5 PM2, PVS1, PP5 PM2, PVS1, PP5 HGVS cDot NM_022124.6:c.5237G > A NM_000260.4:c.2904G > T NM_174878.3:c.528T > G NM_206933.4:c.4338_4339delCT NM_206933.4:c.14787delA Seq. Ontology missense missense stop gained frameshift frameshift Gene Name CDH23 MYO7A CLRN1 USH2A USH2A Inheritance Recessive Recessive Recessive Recessive Recessive Conditions Usher syndrome type 1D, CDH23-Related Disorders, Autosomal recessive nonsyndromic hearing loss 12, Pituitary adenoma 5, Rare genetic deafness, Retinal dystrophy, Childhood onset hearing loss, Usher syndrome Rare genetic deafness, Autosomal recessive nonsyndromic hearing loss 2, Usher syndrome type 1B Usher syndrome type 3, Rare genetic deafness, Retinitis pigmentosa 61; Usher syndrome type 3A Usher syndrome type 2A, USH2A-Related Disorders, Retinal dystrophy, Retinitis pigmentosa 39; Usher syndrome type 2A It is important to acknowledge certain limitations in our experiments. Firstly, miRNA expression may exhibit tissue specificity. Data exclusively collected from immortalized lymphocyte cell lines may not necessarily reflect miRNA expression in other tissues within the same patient or in non-immortalized cells [ 77 ]. Second, it's worth noting that miRNA expression patterns may likely vary depending on the patient's genotype. In our study, we observed significant differences in miRNA profiles within both USH1B and USH1D genotypes, despite both falling under the USH1 classification. This divergence is attributed to distinct sequence variants found in different genes, contributing to the observed phenotypic differences. Nevertheless, the analyzed miRNA panel demonstrated robust performance across multiple statistical tests, with some having prior support documented in the literature. These miRNAs offer a promising and novel set of candidates that warrant exploration in the context of Usher syndrome diagnostics. Conclusions This cell-line based study describes differential expression patterns of top 12 miRNA that may have important roles in pathophysiology of Usher Syndrome. Adopting both microarray and ddPCR techniques, we have shown levels of six upregulated and six down regulated miRNAs using transformed Lymphocyte cell lines from Usher patients and healthy controls. While some of these miRNAs showed unique expression patterns in all Usher types compared to control, others were distinctly assigned between Usher subtypes (1A and 1D). However, our findings necessitate further investigations involving actual patient population that may help unravel miRNAs regulatory mechanisms in neurodegenerative disorders impacting vision and hearing loss. Declarations Data Availability The targeted exome sequencing raw fastq files used in this study ar available through NCBI’s SRA repository under PRJNA1063720, https://dataview.ncbi.nlm.nih.gov/object/PRJNA1063720?reviewer=9ag03uu7aek2b1p0ph0ngegpke. Raw data and R code for miRNA and ddPCR analysis are publically availably on github, https://github.com/westom21/Usher_miRNA. Ethics approval and consent to participate. This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Boys Town National Research Hospital, Omaha NE, USA (IRB protocol number: # 20-14-XP: Approval date: September 28th, 2020). Informed consent was obtained from all subjects involved in the study. Availability of data and materials. The raw data supporting the conclusions of this article are available from the authors without any reservation. Competing interests All authors declare no conflict of interest. Funding This research was funded by a grant from Ryan Foundation, to MRF. Authors' contributions MRF: Conceptualization, project supervision, funding acquisition, and writing of original draft. DSC: Research investigation and methodology: miRNA Microarrays and Droplet Digital-dPCR assays, sections in original draft, review and editing of manuscript. WT: DNA sequencing, data analysis and visualization, sections in original draft, review and editing of manuscript. CJ: Cell culture methods, review and editing of manuscript. GK: Blood samples and Lymphocyte separation methods, research coordination, review and editing of manuscript. NF: DNA purification from Blood and Lymphocyte cell lines, review and editing of manuscript. AO: Methodology, investigation, review and editing of manuscript. All the authors have read and approved the final version of the manuscript. Acknowledgements We wish to thank Dr. Dominic Cosgrove, Center for Sensory Neuroscience, Boys Town National Research Hospital, Omaha NE USA for providing Usher cell lines, his advice and help during this project. We gratefully acknowledge the assistance from Rebecca Cash during our IRB application. We want to thank Dan Meehan, Center for Sensory Neuroscience for technical assistance. We also extend our thanks to Jennifer Bushing, Genomics Core Facility, University of Nebraska Medical Center, Omaha NE USA for her technical assistance in NanoString miRNA microarray experiments. The UNMC Genomics Core Facility receives partial support from the National Institute for General Medical Science (NIGMS) INBRE - P20GM103427-19, as well as the National Cancer Institute and The Fred & Pamela Buffett Cancer Center Support Grant- P30CA036727. This publications’ contents are the sole responsibility of the authors and do not necessarily represent the official views of the NIH or NIGMS. This research was funded by a research grant from the Ryan Foundation to MRF. Consent for publication Not applicable References Whatley, M. et al. Usher Syndrome: Genetics and Molecular Links of Hearing Loss and Directions for Therapy. Front. Genet. 11, 565216 (2020). Delmaghani, S. & El-Amraoui, A. The genetic and phenotypic landscapes of Usher syndrome: from disease mechanisms to a new classification. Hum. Genet. 141, 709–735 (2022). Kimberling, W. J. et al. Frequency of Usher syndrome in two pediatric populations: Implications for genetic screening of deaf and hard of hearing children. Genet. Med. Off. J. Am. Coll. Med. Genet. 12, 512–516 (2010). Zheng, Q. Y. et al. Digenic inheritance of deafness caused by mutations in genes encoding cadherin 23 and protocadherin 15 in mice and humans. Hum. Mol. Genet. 14, 103–111 (2005). Zheng, Q. Y. et al. Digenic inheritance of deafness caused by 8J allele of myosin-VIIA and mutations in other Usher I genes. Hum. Mol. Genet. 21, 2588–2598 (2012). Londin, E. et al. Analysis of 13 cell types reveals evidence for the expression of numerous novel primate- and tissue-specific microRNAs. Proc. Natl. Acad. Sci. U. S. A. 112, E1106-1115 (2015). Weston, M. D., Pierce, M. L., Rocha-Sanchez, S., Beisel, K. W. & Soukup, G. A. MicroRNA gene expression in the mouse inner ear. Brain Res. 1111, 95–104 (2006). Karali, M., Peluso, I., Marigo, V. & Banfi, S. Identification and characterization of microRNAs expressed in the mouse eye. Invest. Ophthalmol. Vis. Sci. 48, 509–515 (2007). Li, H. & Fekete, D. M. MicroRNAs in hair cell development and deafness. Curr. Opin. Otolaryngol. Head Neck Surg. 18, 459–465 (2010). Andreeva, K. & Cooper, N. G. F. MicroRNAs in the Neural Retina. Int. J. Genomics 2014, 165897 (2014). Ushakov, K., Rudnicki, A. & Avraham, K. B. MicroRNAs in sensorineural diseases of the ear. Front. Mol. Neurosci. 6, 52 (2013). Dambal, S., Shah, M., Mihelich, B. & Nonn, L. The microRNA-183 cluster: the family that plays together stays together. Nucleic Acids Res. 43, 7173–7188 (2015). Li, H., Kloosterman, W. & Fekete, D. M. MicroRNA-183 family members regulate sensorineural fates in the inner ear. J. Neurosci. Off. J. Soc. Neurosci. 30, 3254–3263 (2010). Mittal, R. et al. Role of microRNAs in inner ear development and hearing loss. Gene 686, 49–55 (2019). Xiang, L. et al. miR-183/96 plays a pivotal regulatory role in mouse photoreceptor maturation and maintenance. Proc. Natl. Acad. Sci. U. S. A. 114, 6376–6381 (2017). Zhang, C.-J. et al. Ablation of Mature miR-183 Leads to Retinal Dysfunction in Mice. Invest. Ophthalmol. Vis. Sci. 61, 12 (2020). Lumayag, S. et al. Inactivation of the microRNA-183/96/182 cluster results in syndromic retinal degeneration. Proc. Natl. Acad. Sci. U. S. A. 110, E507-516 (2013). Human Genome Overview - Genome Reference Consortium. https://www.ncbi.nlm.nih.gov/grc/human . Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinforma. Oxf. Engl. 25, 1754–1760 (2009). Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience 10, giab008 (2021). Weber, J. A., Aldana, R., Gallagher, B. D. & Edwards, J. S. Sentieon DNA pipeline for variant detection - Software-only solution, over 20× faster than GATK 3.3 with identical results. https://peerj.com/preprints/1672 (2016) doi: 10.7287/peerj.preprints.1672v2 . VarSeqTM (Version 8.x) [Software]. Bozeman, MT: Golden Helix, Inc. Available from https://www.goldenhelix.com . Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015). R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2022). Canouil, M. et al. NACHO: an R package for quality control of NanoString nCounter data. Bioinformatics 36, 970–971 (2020). Campomenosi P, Gini E, Noonan DM, Poli A, D'Antona P, Rotolo N, Dominioni L, Imperatori A. A comparison between quantitative PCR and droplet digital PCR technologies for circulating microRNA quantification in human lung cancer. BMC Biotechnol. 2016;16(1):60. doi: 10.1186/s12896-016-0292-7 . PMID: 27538962; PMCID: PMC4991011. Yan, Linlin. ggvenn: Draw Venn Diagram by ‘ggplot2’. R package version 0.1.9. (2022). Gu, Z. Complex heatmap visualization. iMeta 1, e43 (2022). Schultz, J. M. et al. Allelic hierarchy of CDH23 mutations causing non-syndromic deafness DFNB12 or Usher syndrome USH1D in compound heterozygotes. J. Med. Genet. 48, 767–775 (2011). Al Dhahouri, N., Ali, A., Hertecant, J. & Al-Jasmi, F. Case Report: Reinterpretation and Reclassification of ARSB:p.Arg159Cys Variant Identified in an Emirati Patient With Hearing Loss Caused by a Pathogenic Variant in the CDH23 Gene. Front. Pediatr. 9, 803732 (2021). Astuto, L. M. et al. CDH23 mutation and phenotype heterogeneity: a profile of 107 diverse families with Usher syndrome and nonsyndromic deafness. Am. J. Hum. Genet. 71, 262–275 (2002). Bolz, H. et al. Mutation of CDH23, encoding a new member of the cadherin gene family, causes Usher syndrome type 1D. Nat. Genet. 27, 108–112 (2001). Zhao, L. et al. Next-generation sequencing-based molecular diagnosis of 82 retinitis pigmentosa probands from Northern Ireland. Hum. Genet. 134, 217–230 (2015). Kannan-Sundhari, A. et al. Screening Consanguineous Families for Hearing Loss Using the MiamiOtoGenes Panel. Genet. Test. Mol. Biomark. 24, 674–680 (2020). Svidnicki, M. C. C. M. et al. Screening of genetic alterations related to non-syndromic hearing loss using MassARRAY iPLEX® technology. BMC Med. Genet. 16, 85 (2015). Bujakowska, K. M. et al. Targeted exon sequencing in Usher syndrome type I. Invest. Ophthalmol. Vis. Sci. 55, 8488–8496 (2014). Baux, D. et al. UMD-USHbases: a comprehensive set of databases to record and analyse pathogenic mutations and unclassified variants in seven Usher syndrome causing genes. Hum. Mutat. 29, E76-87 (2008). Becirovic, E. et al. Usher syndrome type 1 due to missense mutations on both CDH23 alleles: investigation of mRNA splicing. Hum. Mutat. 29, 452 (2008). Bonnet, C. et al. An innovative strategy for the molecular diagnosis of Usher syndrome identifies causal biallelic mutations in 93% of European patients. Eur. J. Hum. Genet. EJHG 24, 1730–1738 (2016). Sloan-Heggen, C. M. et al. Comprehensive genetic testing in the clinical evaluation of 1119 patients with hearing loss. Hum. Genet. 135, 441–450 (2016). Lenassi, E. et al. Natural history and retinal structure in patients with Usher syndrome type 1 owing to MYO7A mutation. Ophthalmology 121, 580–587 (2014). Le Quesne Stabej, P. et al. Comprehensive sequence analysis of nine Usher syndrome genes in the UK National Collaborative Usher Study. J. Med. Genet. 49, 27–36 (2012). Jacobson, S. G. et al. Disease boundaries in the retina of patients with Usher syndrome caused by MYO7A gene mutations. Invest. Ophthalmol. Vis. Sci. 50, 1886–1894 (2009). Buratti, E. et al. Aberrant 5’ splice sites in human disease genes: mutation pattern, nucleotide structure and comparison of computational tools that predict their utilization. Nucleic Acids Res. 35, 4250–4263 (2007). Ouyang, X. M. et al. Characterization of Usher syndrome type I gene mutations in an Usher syndrome patient population. Hum. Genet. 116, 292–299 (2005). Bharadwaj, A. K., Kasztejna, J. P., Huq, S., Berson, E. L. & Dryja, T. P. Evaluation of the myosin VIIA gene and visual function in patients with Usher syndrome type I. Exp. Eye Res. 71, 173–181 (2000). Zhang, M. Q. Statistical features of human exons and their flanking regions. Hum. Mol. Genet. 7, 919–932 (1998). Västinsalo, H. et al. Extended mutation spectrum of Usher syndrome in Finland. Acta Ophthalmol. (Copenh.) 91, 325–334 (2013). Isosomppi, J. et al. Disease-causing mutations in the CLRN1 gene alter normal CLRN1 protein trafficking to the plasma membrane. Mol. Vis. 15, 1806–1818 (2009). Fields, R. R. et al. Usher syndrome type III: revised genomic structure of the USH3 gene and identification of novel mutations. Am. J. Hum. Genet. 71, 607–617 (2002). Joensuu, T. et al. Mutations in a novel gene with transmembrane domains underlie Usher syndrome type 3. Am. J. Hum. Genet. 69, 673–684 (2001). Koenekoop, R., Arriaga, M., Trzupek, K. M. & Lentz, J. Usher Syndrome Type II. in GeneReviews® (eds. Adam, M. P. et al.) (University of Washington, Seattle, 1993). Lenassi, E. et al. A detailed clinical and molecular survey of subjects with nonsyndromic USH2A retinopathy reveals an allelic hierarchy of disease-causing variants. Eur. J. Hum. Genet. EJHG 23, 1318–1327 (2015). McGee, T. L., Seyedahmadi, B. J., Sweeney, M. O., Dryja, T. P. & Berson, E. L. Novel mutations in the long isoform of the USH2A gene in patients with Usher syndrome type II or non-syndromic retinitis pigmentosa. J. Med. Genet. 47, 499–506 (2010). Ebermann, I. et al. PDZD7 is a modifier of retinal disease and a contributor to digenic Usher syndrome. J. Clin. Invest. 120, 1812–1823 (2010). Ebermann, I. et al. An USH2A founder mutation is the major cause of Usher syndrome type 2 in Canadians of French origin and confirms common roots of Quebecois and Acadians. Eur. J. Hum. Genet. EJHG 17, 80–84 (2009). Sandberg, M. A. et al. Disease course in patients with autosomal recessive retinitis pigmentosa due to the USH2A gene. Invest. Ophthalmol. Vis. Sci. 49, 5532–5539 (2008). Seyedahmadi, B. J., Rivolta, C., Keene, J. A., Berson, E. L. & Dryja, T. P. Comprehensive screening of the USH2A gene in Usher syndrome type II and non-syndromic recessive retinitis pigmentosa. Exp. Eye Res. 79, 167–173 (2004). Dreyer, B. et al. Identification of novel USH2A mutations: implications for the structure of USH2A protein. Eur. J. Hum. Genet. EJHG 8, 500–506 (2000). Weston, M. D. et al. Genomic structure and identification of novel mutations in usherin, the gene responsible for Usher syndrome type IIa. Am. J. Hum. Genet. 66, 1199–1210 (2000). Eudy, J. D. et al. Mutation of a gene encoding a protein with extracellular matrix motifs in Usher syndrome type IIa. Science 280, 1753–1757 (1998). Zong, L., Chen, K., Wu, X., Liu, M. & Jiang, H. Compound heterozygous MYO7A mutations segregating Usher syndrome type 2 in a Han family. Int. J. Pediatr. Otorhinolaryngol. 90, 150–155 (2016). Xing, D. et al. Targeted exome sequencing identified a novel USH2A mutation in a Chinese usher syndrome family: a case report. BMC Ophthalmol. 20, 485 (2020). Västinsalo, H. et al. Alternative splice variants of the USH3A gene Clarin 1 (CLRN1). Eur. J. Hum. Genet. 19, 30–35 (2011). Geng, R. et al. The microRNA-183/96/182 Cluster is Essential for Stereociliary Bundle Formation and Function of Cochlear Sensory Hair Cells. Sci. Rep. 8, 18022 (2018). Kuhn, S. et al. miR-96 regulates the progression of differentiation in mammalian cochlear inner and outer hair cells. Proc. Natl. Acad. Sci. U. S. A. 108, 2355–2360 (2011). Lewis, M. A. et al. An ENU-induced mutation of miR-96 associated with progressive hearing loss in mice. Nat. Genet. 41, 614–618 (2009). Soldà, G. et al. A novel mutation within the MIR96 gene causes non-syndromic inherited hearing loss in an Italian family by altering pre-miRNA processing. Hum. Mol. Genet. 21, 577–585 (2012). Mencía, A. et al. Mutations in the seed region of human miR-96 are responsible for nonsyndromic progressive hearing loss. Nat. Genet. 41, 609–613 (2009). Xu, S. et al. Mutation Screening in the miR-183/96/182 Cluster in Patients With Inherited Retinal Dystrophy. Front. Cell Dev. Biol. 8, 619641 (2020). Ji, H.-P. et al. MicroRNA-28 potentially regulates the photoreceptor lineage commitment of Müller glia-derived progenitors. Sci. Rep. 7, 11374 (2017). Mahmoudian-Sani, M.-R. et al. MicroRNAs: effective elements in ear-related diseases and hearing loss. Eur. Arch. Oto-Rhino-Laryngol. Off. J. Eur. Fed. Oto-Rhino-Laryngol. Soc. EUFOS Affil. Ger. Soc. Oto-Rhino-Laryngol. - Head Neck Surg. 274, 2373–2380 (2017). Ding, L. et al. Analysis of plasma microRNA expression profiles in male textile workers with noise-induced hearing loss. Hear. Res. 333, 275–282 (2016). Zhang, N. et al. miR-16-5p and miR-19b-3p prevent amyloid β-induced injury by targeting BACE1 in SH-SY5Y cells. Neuroreport 31, 205–212 (2020). Hong, E. H. et al. Differential expression of aqueous humor microRNAs in central retinal vein occlusion and its association with matrix metalloproteinases: a pilot study. Sci. Rep. 12, 16429 (2022). Wang, D.-H., Gu, C. & Yuan, Y.-Z. Case Report: A Case of Cystoid Macular Edema in Retinitis Pigmentosa With Central Retinal Vein Occlusion. Front. Med. 9, (2022). Almenar-Pérez, E., Sarría, L., Nathanson, L. & Oltra, E. Assessing diagnostic value of microRNAs from peripheral blood mononuclear cells and extracellular vesicles in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Sci. Rep. 10, 2064 (2020). Additional Declarations No competing interests reported. Supplementary Files ushermirnasupplementarymaterials.docx ushermirnasupplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3826668","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267565389,"identity":"7aba16fd-c4d9-47fa-b3ad-aaca2a63ebf0","order_by":0,"name":"Wesley Tom","email":"","orcid":"","institution":"Boys Town National Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wesley","middleName":"","lastName":"Tom","suffix":""},{"id":267565390,"identity":"ad04afb6-87bf-45e8-ad9f-970486d3dbe5","order_by":1,"name":"Dinesh S. Chandel","email":"","orcid":"","institution":"Boys Town National Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dinesh","middleName":"S.","lastName":"Chandel","suffix":""},{"id":267565391,"identity":"2c1672ba-6cf6-405e-a9d4-b0952c4c5c2f","order_by":2,"name":"Chao Jiang","email":"","orcid":"","institution":"Boys Town National Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Jiang","suffix":""},{"id":267565392,"identity":"8c6e871e-8694-45ea-a922-a81355217d59","order_by":3,"name":"Gary Krzyzanowski","email":"","orcid":"","institution":"Boys Town National Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gary","middleName":"","lastName":"Krzyzanowski","suffix":""},{"id":267565393,"identity":"c1496358-9c5f-4f5a-a2e9-564ac4972c5f","order_by":4,"name":"Nirmalee Fernando","email":"","orcid":"","institution":"Boys Town National Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nirmalee","middleName":"","lastName":"Fernando","suffix":""},{"id":267565394,"identity":"9553db91-db07-4100-a200-ca585ca5a832","order_by":5,"name":"Appolinaire Olou","email":"","orcid":"","institution":"Boys Town National Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Appolinaire","middleName":"","lastName":"Olou","suffix":""},{"id":267565395,"identity":"ec45cbbd-1cd3-42e6-bab0-21fd809aa9fe","order_by":6,"name":"M. Rohan Fernando","email":"data:image/png;base64,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","orcid":"","institution":"Boys Town National Research Hospital","correspondingAuthor":true,"prefix":"","firstName":"M.","middleName":"Rohan","lastName":"Fernando","suffix":""}],"badges":[],"createdAt":"2024-01-01 00:44:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3826668/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3826668/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49825826,"identity":"342ed738-6e7c-46b8-be5c-c8e5d8242693","added_by":"auto","created_at":"2024-01-18 15:45:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn diagram of miRNAs that are differently expressed in USH phenotypes.\u003c/strong\u003e Venn diagram depicting the distribution of shared and unique differentially expressed miRNAs according to DESeq2 analysis. Among the 798 miRNAs detected by microarray 92 miRNAs were differently expressed in USH phenotypes. Among these 92 miRNAs, 20 were specific to USH1, 14 to USH2, 5 to USH3, 2 to USH1 \u0026amp; USH2, 5 to USH1 \u0026amp; USH3, 10 to USH2 \u0026amp; USH3 and the remaining 36 were common to all USH types.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3826668/v1/80a6616c4abe9efa381ac148.png"},{"id":49825824,"identity":"a646a799-22b6-4d0b-850e-28c1f6b98e5e","added_by":"auto","created_at":"2024-01-18 15:45:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108348,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA biplot showing differences between miRNA expression profiles.\u003c/strong\u003e Each point represents the miRNA profile of a sample, yellow triangles are control cell lines, blue triangles are USHI cell lines, grey triangles are USH2, and brown triangles are USH3 cell lines. Vectors labeled with miRNA are the top 6 miRNA contributing to variation in the first two principal components. The table indicates the results from PERMANOVA analysis, where Pr (\u0026gt;F) indicates significance at values less than 0.05.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3826668/v1/a0bc6259a9ae712fd50cc572.png"},{"id":49826642,"identity":"085eb7ea-d6cc-40e2-a1f0-db2132e71029","added_by":"auto","created_at":"2024-01-18 15:53:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":231711,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of differential miRNAs of interest from DESeq2 contrasts of genotype and phenotype.\u003c/strong\u003eRed tiling indicates downregulated miRNA expression, green tiling indicates upregulated miRNA expression. Rows and columns were clustered by hierarchical clustering. Vertical clusters A and B show relationships between expression patterns, predominantly separating miRNAs upregulated in the control group in cluster A, and upregulated miRNAs in the Usher phenotypes are represented in cluster B. Row names in the heatmap denote the phenotype of the cell line and which replicate, as well as the genotype reported from exome sequencing.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3826668/v1/71781b6fc4d449dadb871a4e.png"},{"id":49826643,"identity":"ea35e0f3-b2c3-4bfb-900f-627ebe72c885","added_by":"auto","created_at":"2024-01-18 15:53:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":142811,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantitative analysis of 6 downregulated candidate miRNAs in Usher cell lines, compared to controls using droplet digital PCR technology.\u003c/strong\u003e Statistical comparisons were made for each miRNA using a one-way ANOVA with Tukey’s adjustment for multiple comparisons, with significance denoted at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. Genotypes with the same letter above the error bars are statistically the same, while differing letters indicate significant differences from one another (i.e. p-values £0.05).\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3826668/v1/1a5bccb0a96b61b9cffdaad7.png"},{"id":49828107,"identity":"d67b6ad4-cc77-480c-a543-172ac066f387","added_by":"auto","created_at":"2024-01-18 16:01:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":141630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantitative analysis of 6 upregulated candidate miRNAs in Usher cell lines, compared to controls using droplet digital PCR technology.\u003c/strong\u003e Statistical comparisons were made for each miRNA using a one-way ANOVA with Tukeys adjustment for multiple comparisons, with significance denoted at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. Genotypes with the same letter above the error bars are statistically the same, while differing letters indicate significant differences from one another (i.e. p-values £0.05).\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3826668/v1/98ae064596e9b0be2f6d3cdc.png"},{"id":49825830,"identity":"2d37d17a-b986-45b5-b75c-5d47a3b0f64f","added_by":"auto","created_at":"2024-01-18 15:45:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":178695,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNanoString miRNA counts of downregulated 6 candidate miRNAs in Usher cell lines, compared to controls.\u003c/strong\u003e Statistical comparisons were made for each miRNA using a one-way ANOVA with Tukeys adjustment for multiple comparisons, with significance denoted at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. Genotypes with the same letter above the error bars are statistically the same, while differing letters indicate significant differences from one another (i.e. p-values £0.05).\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3826668/v1/b3678327239b7fb39547a804.png"},{"id":49825831,"identity":"5131f7c6-71a1-4d1a-8f20-95589733c5a0","added_by":"auto","created_at":"2024-01-18 15:45:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":177949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNanoString miRNA counts of upregulated 6 candidate miRNAs in Usher cell lines, compared to controls.\u003c/strong\u003e Statistical comparisons were made for each miRNA using a one-way ANOVA with Tukeys adjustment for multiple comparisons, with significance denoted at p£0.05. Genotypes with the same letter above the error bars are statistically the same, while differing letters indicate significant differences from one another (i.e. p-values £0.05).\u0026nbsp;\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3826668/v1/072eb053b1443481aad6fc77.png"},{"id":61670007,"identity":"eea5ad64-7a23-46eb-9e62-bec310808f44","added_by":"auto","created_at":"2024-08-02 17:55:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3542666,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3826668/v1/36a1bc8a-9dc3-471a-8144-2fba91dd09ec.pdf"},{"id":49825832,"identity":"761a9b60-418f-4836-ad92-fa4768c09bfe","added_by":"auto","created_at":"2024-01-18 15:45:27","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":217520,"visible":true,"origin":"","legend":"","description":"","filename":"ushermirnasupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3826668/v1/976ffc69fbaf8418ed473fd3.docx"},{"id":49825833,"identity":"11af7bc8-3ce7-4c7d-8de4-8e6e44fa3346","added_by":"auto","created_at":"2024-01-18 15:45:27","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":217520,"visible":true,"origin":"","legend":"","description":"","filename":"ushermirnasupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3826668/v1/1fe2af5e27a8c82f4f56a4f6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of Usher Syndrome Cell line Genotypes and Elucidation of Novel MicroRNA Biomarkers using MicroRNA Microarray and Droplet Digital PCR","fulltext":[{"header":"Background","content":"\u003cp\u003eUsher syndrome (USH) represents an autosomal recessive inherited disorder that profoundly impacts hearing, vision, and balance. It is characterized by the triad of sensorineural hearing loss (SNHL), vision impairment due to retinitis pigmentosa (RP), and vestibular dysfunction. The prevalence of USH ranges from 4 to 17 cases per 100,000 individuals [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. USH is clinically categorized into three distinct types, distinguished by the age of onset of hearing and visual impairments, the severity of hearing loss, and the presence of balance issues. USH type 1 (USH1) is marked by early-onset SNHL, RP-related vision loss, and balance problems that commence within the first decade of life. USH type 2 (USH2) is characterized by the onset of mild to moderate non-progressive SNHL and RP between the ages of 10 and 20, accompanied by normal vestibular function. USH type 3 (USH3) involves progressive SNHL, sometimes accompanied by balance disturbances, with a variable onset time for SNHL and RP.\u003c/p\u003e \u003cp\u003eUSH presents as a clinically and genetically diverse condition with nine confirmed causative genes. While traditionally considered a monogenic disorder, recent studies have unveiled instances of digenic inheritance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Mutations in eight genes, including MYO7A, USH1C, CDH23, PCDH23, USH1E, USH1G, USH1K, and CIB2/DFNB48, are responsible for USH1 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. USH2 results from genetic mutations in USH2A, GPR98 (also known as ADGRV1), and WHRN (also known as DFNB31) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], while USH3 is attributed to mutations in CLRN-1, HARS, and PDZD7 genes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMicroRNAs (miRNAs) are small single-stranded non-coding RNA molecules spanning 21 to 23 nucleotides in length, play pivotal roles in post-transcriptional gene expression regulation. These miRNA molecules are integral regulators of cellular homeostasis in both normal physiological and pathological processes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Extensive research has underscored the significance of miRNAs in the visual, auditory, and vestibular systems [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Specifically, the microRNA-183 family, encompassing miRNA-183, miRNA-182, and miRNA-96, is highly conserved and indispensable for the development and maturation of sensory organs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This family of miRNAs plays crucial roles in maintaining normal growth and population of hair cells and neurons within the inner ear [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These are also vital for the maturation of photoreceptor cells [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] since disruptions in the miRNA-183 family has been implicated in syndromic retinal degradation and substantial vestibular impairments [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs growing miRNA research continues to elucidate their important roles in inner ear development and hearing loss, differentially expressed miRNAs thus may have biomarker potential with future applications in diagnostics and treatment to restore hearing function [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, defining precise roles and targets of individual miRNAs involved in neurosensory pathways in the inner ear remains a major challenge. The miRNA studies on hearing loss have relied mostly on animal models due to inaccessibility of the human inner ear. The present study was designed to investigate miRNA expression patterns in USH cell lines (derived from Usher patients) and compare them with those in control cell lines, aiming to identify potential miRNA biomarkers for Usher syndrome.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell lines\u003c/h2\u003e \u003cp\u003eThree of the cell lines utilized in this study, namely D3741 (USH1), D3739 (USH1), and D2880 (USH3), were originally established by Dr. William J. Kimberling's laboratory at Boys Town National Research Hospital in Omaha, Nebraska, USA. These cell lines were created through the infection of lymphocytes obtained from individuals with Usher syndrome (USH) using Epstein-Barr virus (EBV) derived from the B95-8 cell line. Informed consent was obtained from all donors prior to blood draw, and the study was approved by the Institutional Review Board at Boys Town National Research Hospital (IRB#96-06-0X). As part of our control group, lymphocytes were also isolated from healthy donors and subsequently immortalized using EBV. In addition, a lymphocyte cell line corresponding to Usher syndrome type 2A (USH2A) was procured from the Coriell Institute (Catalog ID: GM09053, Camden, New Jersey). These cell lines were cultivated in RPMI 1640 medium, supplemented with 20% fetal bovine serum (FBS) and 50 \u0026micro;g/mL of gentamicin. Cultures were maintained in 100 ⅹ 20-mm tissue culture plates in a humidified atmosphere containing 5% CO2, at 37\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eDNA extraction and shearing\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from cultured cells employing the QIAamp DNA Mini kit (Cat. No. 56304), following manufacturer\u0026rsquo;s recommended protocol. Purified DNA was quantified using the \"Qubit dsDNA BR Assay Kit\" on a Qubit 4.0 fluorometer. To attain an optimal fragment size conducive of downstream applications, the genomic DNA was sheared using a Covaris M220 sonicator (Covaris LLC., Woburn, MA). Shearing resulted in fragments of approximately 250 bp. The effectiveness of the shearing process was confirmed through fragment size analysis using a D1000 Tapestation (P/N: 5067\u0026ndash;5583, 5067\u0026ndash;5582, Agilent Technologies). For each specific cell line under investigation (USH1B, USH1D, USH2A, and USH3A), 200 ng of sheared genomic DNA prepared in a final volume of 50.0 \u0026micro;L 0.1X-TE buffer, was used for targeted exon sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTargeted Exon Sequencing\u003c/h2\u003e \u003cp\u003eA custom exon probe panel targeting coding regions of 26 genes associated with syndromic and non-syndromic hearing loss was created using Agilent\u0026rsquo;s SureDesign probe design tool (Agilent Technologies, Santa Clara CA). In total, 5740 biotinylated mRNA probes were synthesized covering the coding regions of 26 genes of interest (200.345 Kbp total length) and used in a hybridization and capture approach for enrichment of target DNA. Probes covered exonic regions including a 25bp extension at 3\u0026rsquo; and 5\u0026rsquo; UTRs. Coding regions with \u0026gt;\u0026thinsp;40bp gaps in coverage received custom boosting and tiling strategies to ensure sufficient sequencing coverage. Genes covered in the panel include: ABHD12, ADGRV1, ARSG, CDH23, CIB2, CLRN1, ESPN, FOXI1, GJB2, GJB6, HARS1, KCNE1, KCNJ10, KCNQ1, MYO7A, PCDH15, PDZD7, SLC26A4, USH1C, USH1G, USH2A, WHRN, CEP250, and CEP78.\u003c/p\u003e \u003cp\u003eLibraries for the hearing loss exon capture were prepared using Agilent\u0026rsquo;s SureSelect XT HS2 DNA with a Post-capture pooling protocol, as per manufacturer\u0026rsquo;s instructions (P/N: G9985D, Agilent Technologies, Santa Clara CA). Briefly, 200ng extracted DNA in a total volume of 50 \u0026micro;L 0.1X TE buffer, sheared to ~\u0026thinsp;250bp with a Covaris M220 sonicator, was used as the starting input for library preparation. Fragmented DNA samples underwent enzymatic end-repair followed by a dA-tail ligation. Sequencing adapters were ligated to dA-overhang and each cell line received a unique dual indexed primer pair with unique molecular indices. An 8 cycle PCR amplification of adapter ligated libraries was performed under the following conditions; 1 Cycle: 98\u0026ordm;C (2 min), 8 Cycles: 98\u0026ordm;C (30 sec), 60\u0026ordm;C (30 sec), 72\u0026ordm;C (1 min), 1 Cycle: 72\u0026ordm;C (5 min). Indexed libraries were then hybridized with a biotinylated custom HL probe panel, and DNA-probe hybrids were captured using streptavidin beads (P/N: 65601, Thermo Fisher Scientific). Targeted libraries were purified using AMPure XP (P/N: A63880, Beckman Coulter Genomics) 1X bead clean up, and library quality was assessed using a D1000 High Sensitivity Tapestation assay (P/N: 5067\u0026ndash;5585, 5067\u0026ndash;5584, Agilent Technologies). Post-capture libraries were diluted to equimolar concentrations and pooled for sequencing on an Illumina NextSeq 550DX 300 cycle high output flow-cell, 150bp paired end reads (P/N: 20024908, Illumina inc.). Targeted exon sequencing resulted in samples- USH3A (695.41Mbp), USH1B (712.23Mbp), USH1D (796.93Mbp), USH2A (767.00Mbp), with an average of 20.04\u0026nbsp;million reads per sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVariant Calling and Interpretation\u003c/h2\u003e \u003cp\u003eReads for each cell line were aligned and mapped to the human reference genome (GRCh38p.14) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] using BWA and Samtools, respectively [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Sequence variants were called using Sentieon\u0026rsquo;s DNA pipeline for variant detection [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Sequencing variant interpretation according to ACMG classification criteria were applied using VarSeq software version 2.4.0 (Golden Helix, Inc., Bozeman MT, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.goldenhelix.com\u003c/span\u003e\u003cspan address=\"https://www.goldenhelix.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and miRNA Expression Assay\u003c/h2\u003e \u003cp\u003eBriefly, total RNA was extracted from cell lines using QIAzol\u0026reg; reagent (cat. # 79306), followed by purification using the miRNeasy Tissue/Cells Advanced Micro Kit (cat. #217684) protocol as per manufacturer\u0026rsquo;s instructions (QIAGEN Sciences Inc., Germantown, MD). Purified total miRNA from cell lines (100 ng miRNA per sample) was used as input for miRNA expression analysis. miRNA expression in Usher and control cell lines was quantified using NanoString\u0026copy; Human v3 miRNA assay (cat. # CSO-MIR3-12), performed on the nCounter Pro analysis system (NanoString Technology, Seattle, WA, USA). The assay detects 798 known human miRNAs, where each miRNA has specific tag sequences ligated with fluorescently barcoded reporter probes. After a hybridization period of 16h at 65℃, these miRNA-specific barcodes were detected by the nCounter Digital Analyzer providing miRNA copy numbers. Raw count data from the miRNA assay was normalized using the NanoString quality control dashboard (NACHO version 2.0.0) package in R [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. There are no well-established \u0026ldquo;housekeeping\u0026rdquo; miRNAs, so NACHO\u0026rsquo;s housekeeping predict\u0026thinsp;=\u0026thinsp;TRUE function was used to select the top five housekeeping miRNA candidates directly from the NanoString assay. The miRNA count data was normalized relative to internal positive, negative, and housekeeping predictions using a geometric mean, normalization method = \u0026ldquo;GEO\u0026rdquo; [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The resulting normalized count table was used for downstream analysis in R.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAbsolute quantification of miRNAs by Droplet Digital PCR (ddPCR)\u003c/h2\u003e \u003cp\u003eFollowing miRNA-panel screening by NanoString microarray, 12 miRNAs (6-up and 6-down regulated) with significant differential expression patterns were selected for dd-PCR analysis. For dd PCR, equal starting RNA amount (10 ng) from each sample was first converted into cDNA by reverse transcription (RT) using miRNA specific primer sets (Life Technology Corp., CA, USA), following TaqMan\u0026trade; RT- kit protocol (Applied Biosystems Cat.#4366596). The cDNA/RT reaction was carried out in a total volume of 15\u0026micro;L consisting of 2 \u0026micro;L of RNA template (5ng/\u0026micro;L), 3 \u0026micro;L of RT-specific miRNA primers (5x), 0.15 \u0026micro;L dNTP-mix (25mM each), 0.19 \u0026micro;L of RNAse-H, 1\u0026micro;L of Multiscribe\u0026trade; reverse transcriptase enzyme (50U/\u0026micro;L), 1.5 \u0026micro;L of RT-buffer (10x) with a final reaction volume adjusted with nuclease free water (7.16 \u0026micro;L). RT reactions were performed on a PCR cycler (cfx1000, BioRad) with a set parameter of 16⁰C for 30m, 42⁰C for 30m, 85⁰C for 5 m and an infinite hold at 4⁰C. The resulting cDNA products were diluted based on relative abundance of individual miRNAs. The standard ddPCR reaction was performed in a 20 \u0026micro;L reaction volume by adding 10 \u0026micro;L of Bio-Rad 2x ddPCR Supermix for probes, 2.0 \u0026micro;L of diluted cDNA, 1.0 \u0026micro;L of individual miRNA-specific primer-probe mix and reaction volume adjusted to 20 \u0026micro;L by nuclease free water. Droplet digital PCR was performed using Bio-Rad Automated QX200 droplet digital PCR system as previously described [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Micro-RNA copy numbers per 1ng RNA was calculated using the equation given below:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eNormalized count data was imported into R statistical analysis software where all subsequent microarray analysis was performed [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Usher-1D and Usher-1B cell lines had three replicates in the NanoString assay. Usher-2A, Usher-3A, and the control cell lines had 4 replicates each.\u003c/p\u003e \u003cp\u003eDifferentially expressed (DE) miRNA analysis was performed using the \u0026ldquo;DESeq2\u0026rdquo; R package [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Venn diagrams depicting overlapping and unique miRNAs from DESeq2 differential analysis was performed using the ggvenn package [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. All pairwise contrasts of phenotype (Usher-1B, Usher-1D, Usher-2A, Usher-3A, and NBT) were considered for differential miRNA expression analysis, and significance assigned at a Benjamini-Hochberg (BH) corrected p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eHeatmaps of top differential miRNAs from pairwise genotype and phenotype contrasts were created using \u0026ldquo;ComplexHeatmap\u0026rdquo; package in R. For heatmaps, expression count data was scaled for large differences in variation using the formula z\u003csub\u003ei\u003c/sub\u003e=(x\u003csub\u003ei\u003c/sub\u003e-u\u003csub\u003ei\u003c/sub\u003e)/s\u003csub\u003ei\u003c/sub\u003e, where x\u003csub\u003ei\u003c/sub\u003e is the count for miRNA\u003csub\u003ei\u003c/sub\u003e, u\u003csub\u003ei\u003c/sub\u003e is the mean for miRNA\u003csub\u003ei\u003c/sub\u003e, and s\u003csub\u003ei\u003c/sub\u003e is the standard deviation of miRNA\u003csub\u003ei\u003c/sub\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Samples were grouped using hierarchical clustering according to miRNA expression patterns [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) was performed using a Bray-Curtis dissimilarity matrix calculated from normalized count data, and principal components were visualized with the \u0026ldquo;microViz\u0026rdquo; package [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Significant differences between miRNA profiles were performed using a permutational multivariate analysis of variance (PERMANOVA) on Bray-Curtis dissimilarity distances, with the adonis2 function in R\u0026rsquo;s \u0026ldquo;vegan\u0026rdquo; package [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The PERMANOVA used a generalized linear model considering the effect of phenotype (model formula: Bray-Curtis dissimilarity matrix\u0026thinsp;~\u0026thinsp;Phenotype).\u003c/p\u003e \u003cp\u003eFor ddPCR analysis, a one-way ANOVA with a Tukey test for pairwise mean comparisons was used to determine differential expression between all cell lines where statistical significance was assigned at p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTargeted Exon Sequencing\u003c/h2\u003e \u003cp\u003eThe genotype of all cell lines was confirmed using target exon sequencing as summarized in Table-1. The D3739 cell line has a homozygous likely pathogenic G\u0026thinsp;\u0026gt;\u0026thinsp;A missense mutation in the CDH23 gene, thus attributing to the USH1D genotype. This genotype is consistently associated with USH1 where the variant creates a novel splice acceptor site which results in an in-frame deletion of 51 base pairs, removing a calcium binding motif of the protein [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The D3741 cell line contains a homozygous likely pathogenic G\u0026thinsp;\u0026gt;\u0026thinsp;T missense mutation in the MYO7A gene. Therefore, D3741 cell line was categorized under USH1B genotype. This variant has been detected both as a homozygous mutation and compound heterozygous mutation. The USH1B cell line possesses a likely pathogenic homozygous missense variant in the MYO7A gene c.2905G\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Glu968Asp) which is predicted to cause splice site variation in the myosin VIIA gene [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43 CR44 CR45 CR46\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In cell line D2880, a homozygous variant in CLRN1 gene was detected at c.528A\u0026thinsp;\u0026gt;\u0026thinsp;C (p.Tyr176X) thus categorized as USH3A. This mutation causes a premature stop codon, truncating the clarin-1 protein [\u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Interestingly, the USH2A cell line has a likely compound heterozygous mutation phenotype with two variants in the USH2A gene. The first variant- USH2A c.4338_4339del (p.Cys1447fs) is a frameshift variant which is predicted to cause a premature stop codon [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR53 CR54 CR55 CR56 CR57 CR58 CR59 CR60\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The second heterozygous variant has yet to be functionally confirmed. It occurs in USH2A c.14787del (p.Glu4930fs) and is computationally predicted to cause premature truncation or nonsense-mediated decay in the production of usher protein [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003emiRNA microarray analysis\u003c/h2\u003e \u003cp\u003eNormalized microarray data were further analyzed using DESeq2 to identify miRNAs that were differentially expressed in USH cell lines compared to control cells. At least a twofold increase or decrease with a BH-adjusted \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as significantly altered miRNA expression. Using this criterion, we found 92 differentially expressed miRNAs in USH cells. Of these, 20 were unique to USH1, 14 to USH2, and 5 to USH3, 2 were unique to both USH1 and USH2, 5 to USH1 and USH3, and 10 to USH2 and USH3. The remaining 36 were identified as common to all USH types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDifferentially expressed miRNAs from microarray using DESeq2.\u003c/b\u003e A table of all differentially abundant miRNAs by cell lines, which are broken down into the following categories: Common to all (differential miRNAs that are dysregulated in Usher cell lines compared to controls), Usher 1 only (miRNAs uniquely dysregulated only in Usher 1 cell lines compared with controls), Usher 2 only(miRNAs uniquely dysregulated only in Usher 2 cell lines compared with controls), Usher 3 only(miRNAs uniquely dysregulated only in Usher 3 cell lines compared with controls), Usher 1 and 2(miRNAs dysregulated in both Usher 1 and 2 compared to controls), Usher 1 and 3(miRNAs dysregulated in both Usher 1 and 3 compared to controls), Usher 2 and 3(miRNAs dysregulated in both Usher 2 and 3 compared to controls).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDifferentially abundant microRNAs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommon To All\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehsa-miR-222-3p, hsa-miR-424-5p, hsa-miR-503-5p, hsa-miR-1252-5p, hsa-miR-1246, hsa-miR-182-5p,\u003c/p\u003e \u003cp\u003ehsa-miR-1205, hsa-miR-450a-5p, hsa-miR-654-5p, hsa-miR-3934-5p, hsa-miR-221-5p, hsa-miR-34a-5p, hsa-miR-150-5p, hsa-let-7d-5p, hsa-miR-483-3p, hsa-miR-146b-5p, hsa-miR-5010-3p, hsa-miR-591,\u003c/p\u003e \u003cp\u003ehsa-miR-155-5p, hsa-miR-363-3p, hsa-let-7i-5p, hsa-miR-194-5p, hsa-miR-28-3p, hsa-miR-10a-5p,\u003c/p\u003e \u003cp\u003ehsa-miR-146a-5p, hsa-miR-337-3p, hsa-miR-200c-3p, hsa-miR-96-5p, hsa-miR-5196-3p\u0026thinsp;+\u0026thinsp;hsa-miR-6732-3p, hsa-let-7f-5p, hsa-miR-577, hsa-let-7g-5p, hsa-miR-183-5p, hsa-let-7b-5p, hsa-miR-148a-3p, hsa-miR-98-5p\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUsher 1 Only\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehsa-miR-151a-5p, hsa-miR-129-2-3p, hsa-miR-519d-3p, hsa-miR-4431, hsa-miR-345-5p, hsa-miR-152-3p, hsa-miR-1260b, hsa-miR-3916, hsa-miR-4455,hsa-miR-3195, hsa-miR-195-5p, hsa-miR-1226-3p, \u003c/p\u003e \u003cp\u003ehsa-miR-151a-3p, hsa-miR-181b-2-3p, hsa-miR-1287-5p, hsa-miR-324-5p, hsa-miR-484, hsa-miR-16-5p, hsa-miR-331-3p, hsa-miR-26b-5p\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUsher 2 Only\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehsa-miR-513b-5p, hsa-miR-137, hsa-miR-370-3p, hsa-miR-503-3p, hsa-miR-767-5p, hsa-miR-132-3p, hsa-let-7c-5p, hsa-miR-1304-5p, hsa-miR-514a-3p, hsa-let-7a-5p, hsa-miR-342-3p, hsa-miR-181a-5p, hsa-miR-181c-5p, hsa-miR-1193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUsher 3 Only\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehsa-miR-125a-3p, hsa-miR-494-3p, hsa-miR-125b-5p, hsa-miR-4284, hsa-miR-181a-2-3p\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUsher 1 and 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehsa-miR-551b-3p, hsa-miR-299-3p\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUsher 1 and 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehsa-miR-371b-5p, hsa-miR-601, hsa-miR-1827, hsa-miR-27a-3p, hsa-miR-582-5p\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUsher 2 and 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehsa-miR-574-3p, hsa-miR-223-3p, hsa-miR-1183, hsa-miR-28-5p, hsa-miR-542-5p, hsa-let-7e-5p, hsa-miR-23c, hsa-miR-221-3p, hsa-miR-374b-5p, hsa-miR-23a-3p\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePERMANOVA analysis of miRNA microarray data confirmed that miRNA expression profiles were significantly different between all cell lines with a BH-adjusted \u003cem\u003ep\u003c/em\u003e-value of 0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Results obtained using PERMANOVA were corroborated in PCA analysis, showing distinct clustering by cell line. However, USH2A and USH3A clustered more similarly compared to controls or USH1 phenotypes, and a total of 94.8% of the variation in the miRNA expression profile dataset explained in the ordination of the first two principal components (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, of the top 6 miRNAs driving variation between samples, hsa-miR-155-5p, hsa-miR-142-3p, and hsa-let-7a-5p were differentially expressed among Usher cell lines, particularly USH2A and USH3A samples. Variation in levels of hsa-miR-16-5p and hsa-miR-19b-3p was assigned to the control cell lines. The hsa-miR-4454\u0026thinsp;+\u0026thinsp;hsa-miR-7975 was different in one of the USH2A sample replicates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a list of top 12 differentially expressed miRNAs in USH cells compared to controls. The heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) shows differential expression of these miRNAs uniquely assigned to USH and control cell lines. All miRNAs in this list were differentially expressed in the DESeq2 analysis, with a BH-adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and had at least a 2-fold change in miRNA expression between Usher cell lines and controls. Additionally, these 12 miRNAs had an average copy number greater than 50 within the dominant sample type. The following six miRNAs, Hsa-miR-16-5p, hsa-miR-19b-3p, hsa-miR-4454\u0026thinsp;+\u0026thinsp;hsa-miR-7975, hsa-miR-142-3p, hsa-miR-155-5p, and hsa-let-7a-5p were also the top 6 miRNAs explaining high amounts of variation in miRNA profiles in the PCA biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both PCA and heatmap showed USH2 and USH3 grouped together. Control samples clustered together, and USH1B and USH1D samples shared similar miRNA expression profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The top 12 miRNAs panel examined includes 6 down-regulated and 6 up-regulated miRNAs. The miRNA-28-5p, miRNA-96-5p, miRNA-182-3p, miRNA-183-3p, miRNA-16-5p and miRNA-19b-3p were downregulated in all USH types compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, D \u0026amp; F, miRNA-363-3p, miRNA-155-5p and miRNA-142-3p were upregulated in all USH types. The miRNA-223-3p (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) was upregulated only in USH2A and USH3A, whereas miRNA-150-5p showed increased expression only in USH2A (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). MiRNA-let7a-5p is upregulated in USH2A, USH3A and USH1B (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). However, this change was not evident in USH1D (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE) showing that same phenotype with different genotypes may vary in their miRNA expression patterns.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eTop 12 differentially abundant miRNA biomarker Candidates.\u003c/b\u003e A summary table documenting the 12 miRNA biomarker candidates from the study. Log2 fold change (Log2FC) as well as BH-adjusted p-values for each miRNA in each Usher type when compared to the control lines are shown in each row. The final two columns show the average Log2FC and standard deviation of all cell lines.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUsher 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUsher 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eUsher 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMean log2FC vs. Control\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emicroRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elog2FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-Value (BH-adj)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003elog2FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value (BH-adj)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003elog2FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-Value (BH-adj)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean log2FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStandard Dev.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-150-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.8901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.7786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.5893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.4818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-155-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.6412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.6898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.4936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-363-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.1282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.3819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.4404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-223-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.6878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.2343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.5581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-let-7a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.6396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.6111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-142-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.5316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.6051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-19b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.4389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.9554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.1786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-28-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.9993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.5731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.0932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.2219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-16-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.1845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.3422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.6044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-183-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.2771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.6795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.3467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.4344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-96-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.9386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.2701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.7408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.9832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-miR-182-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.2691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.0376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.1735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-6.8267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.0498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003emiRNA ddPCR analysis\u003c/h2\u003e \u003cp\u003eThe top 12 differentially expressed miRNAs identified after microarray screenings were validated using droplet digital PCR technology. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the expression of 6 miRNAs that were significantly downregulated in USH cells compared to controls. These miRNAs include miRNA-28-5p, miRNA-96-5p, miRNA-182-3p, miRNA-183-3p, miRNA-16-5-p and miRNA-19b-3p. The first four miRNAs: miRNA-28-5p, miRNA-96-5p, miRNA-182-3p, and miRNA-183-3p showed no statistically significant difference among USH types (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e panels A, B, C \u0026amp; D). However, miRNA-16-5-p and miRNA-19b-3p showed variation within USH types: USH1D showed relatively more downregulation than USH2A (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e panels E and F). Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the expression of 6 miRNAs that were upregulated in USH cells compared to controls. Expression levels of miRNA-363-3p was significantly high in all USH types except for USH3A (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Similarly, miRNA-223-3p was significantly upregulated in all USH types except for USH1B (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Statistically significant upregulation of miRNA-150-5p was observed only in USH2A (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). MiRNA-155-5p, miRNA-let-7a-5p, and miRNA-142-3p were upregulated in all USH types compared to control (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e panels D, E \u0026amp; F). However, miRNA-223-3p was upregulated in all USH types except for USH1B (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we assessed the expression profiles of 798 miRNAs across different Usher and control cell line types, revealing intriguing variations. The Usher cell lines exhibited distinct miRNA expression profiles, as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These variations were not only evident among Usher type 1, 2, and 3 phenotypes but also discernible between USH1B and USH1D genotypes. According to microarray data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the miRNA expressions in all Usher samples significantly differed from those in the control samples. We hypothesize that these differences in miRNA expression may be further complicated by unique combinations of sequence variants contributing to the phenotype. For instance, in the USH2A cell line, we identified two heterozygous frameshift mutations that appear to have a compounded effect, resulting in the observed phenotype. This compound mutation effect, although somewhat unconventional, has been documented in Usher syndrome, challenging the widely accepted notion that homozygous recessive mutations exclusively underlie the disorder [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. In contrast, USH1D, USH1B, and USH3A all exhibited homozygous recessive mutations, aligning with the classical definition of autosomal recessive inheritance in Usher patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe miRNA-183/182/96 family emerged as a key player, showing significant downregulation in all Usher cell lines compared to controls, as evident from both DESeq2 analysis of microarray results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e panels B, C \u0026amp; D) and ddPCR analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e panels B, C \u0026amp; D). Previous studies have showcased the significance of these miRNAs, as their inactivation in mice led to significant developmental issues in cochlear hair cells [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Moreover, mutations affecting miRNA-96-5p have been linked to impaired growth, maturation, and, in some cases, malformation of cochlear hair cells in mice and humans [\u003cspan additionalcitationids=\"CR67 CR68\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Additionally, alterations in the expression of the microRNA-183/182/96 family have been associated with retinal dystrophy- also a component of Usher syndrome [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Our findings align with existing literature, supporting the notion that microRNA-183/182/96 downregulation could potentially serve as a diagnostic biomarker for Usher syndrome. Notably, our microarray data revealed an average 2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 log2 fold decrease in miRNA-183-3p expression, a 2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 log2 fold decrease in miRNA-96-5p expression, and approximately a 6.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05 log2 fold decrease in miRNA-182-5p expression in Usher samples compared to controls (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This downregulation across the entire miR-183/182/96 family was confirmed using ddPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e panels B, C \u0026amp; D) where Tukey HSD post hoc tests indicated significantly higher expression of all three miRNAs in control samples compared to Usher lines, with no significant differences observed among Usher lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e panels B, C \u0026amp; D).\u003c/p\u003e \u003cp\u003eInterestingly, we observed a significant downregulation of the miRNA-28-5p in all Usher samples, with an average 2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 log2 fold lower expression compared to controls (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although no prior associations between miRNA-28-5p and Usher syndrome have been reported, Ji et al. (2017) suggested that miRNA-28 may target and regulate the expression of the cone-rod homeobox gene (CRX), making it a potential candidate for retinal degeneration, a component of Usher syndrome [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMiRNA-16-5p emerged as a driver of variation, particularly toward control samples, displaying an average 1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 log2 fold higher expression in control samples compared to Usher lines (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Interestingly, some literature links elevated miRNA-16-5p expression to noise-induced hearing loss (NIHL) and even Alzheimer\u0026rsquo;s disease [\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. However, our study indicated low levels of miRNA-16-5p in Usher samples compared to controls, suggesting that the hearing loss caused by Usher and NIHL may have different mechanisms.\u003c/p\u003e \u003cp\u003eSix miRNAs, namely miRNA-150-5p, miRNA-155-5p, miRNA-363-3p, miRNA-223-3p, miRNA-let-7a-5p, and miRNA-142-3p, exhibited upregulation in the Usher cell lines compared to the control (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among these, miRNA-155-5p, miRNA-142-3p, and miRNA-let-7a-5p were among the most abundant miRNAs in the dataset. Notably, miRNA-142-3p made up approximately 37.3% of miRNA copies in Usher samples, compared to 18.5% in the control line (supplementary Figs.\u0026nbsp;1 and 2). This miRNA displayed an average 1.26 log2 fold higher expression in Usher samples and holds potential as a robust biomarker candidate. While miRNA-142-3p has not been previously associated with Usher or sensorineural hearing loss (SNHL), its downregulation has been observed in the aqueous humor of patients with central retinal vein occlusion (CRVO), a condition occasionally linked to retinitis pigmentosa (RP) [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Interestingly, our study found miRNA-142-3p to be one of the most abundant miRNAs in all samples, particularly in Usher cell lines (~\u0026thinsp;40% of all miRNA copies), which contrasts with observations in CRVO (supplementary Figs.\u0026nbsp;1 and 2).\u003c/p\u003e \u003cp\u003eAdditionally, miRNA-Let-7a emerged as a prominent driver of variation in both PCA and DESeq2 analyses, exhibiting an average 1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 log2 fold increase compared to control samples (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Usher samples, miRNA-Let-7a accounted for 2.35% of the total miRNA copies per genotype, while control samples displayed only 0.9%. Analysis of the heatmap revealed that both USH2A and USH3A exhibited the highest levels of miRNA-Let-7a and miRNA-142-3p, while USH1 genotypes displayed lower expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), especially USH1D. This observation is consistent with the trajectory of miR-Let-7a and miR-142-3p vectors in the PCA biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to examining miRNA expression patterns, we conducted targeted exome sequencing to identify genotypes and likely pathogenic sequence variants in previously clinically diagnosed Usher patient-derived cell lines (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, we validated our microarray observations with ddPCR quantification of the 12 miRNAs, obtaining consistent results when compared to microarray data. These 12 miRNAs, characterized by their consistency across statistical tests and assays (ddPCR and microarray), present a list of potential biomarkers present in lymphocytes that could aid in the diagnosis of USH1B, USH1D, USH2A, and USH3A.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSequence Variant Table.\u003c/b\u003e Exon capture sequencing variants classified using VarSeq.\u0026nbsp;For each cell line, the phenotype that was clinically diagnosed was verified using NGS targeted exon sequencing where likely pathogenic mutations were documented. Chromosomal location of the mutations, the reference base call, and the sample mutation are all provided. The ACMG classification criteria is also provided as well as the Human Genome Variant Society variant nomenclature. The gene that is affected, zygosity, predicted inheritance, and known associated conditions for all likely pathogenic mutations are all provided.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCell Line\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD3739\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD3741\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD2880\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoriell (GM09053)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhenotype\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsher-1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsher-1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUsher-3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUsher-2A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChr:Pos\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10:71779316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11:77181589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3:150928107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1:216190280\u003c/p\u003e \u003cp\u003e1:215647526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRef/Alt\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAG/-\u003c/p\u003e \u003cp\u003eT/-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenotype\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomozygous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHomozygous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHomozygous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHeterozygous\u003c/p\u003e \u003cp\u003eHeterozygous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClassification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLikely Pathogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLikely Pathogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVUS/Conflicting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePathogenic\u003c/p\u003e \u003cp\u003ePathogenic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eACMG Criteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM2, PS1, PP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM2, PP2, PS1, PP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBS1, PVS1 Strong, PP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM2, PVS1, PP5\u003c/p\u003e \u003cp\u003ePM2, PVS1, PP5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHGVS cDot\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNM_022124.6:c.5237G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNM_000260.4:c.2904G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNM_174878.3:c.528T\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNM_206933.4:c.4338_4339delCT\u003c/p\u003e \u003cp\u003eNM_206933.4:c.14787delA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeq.\u0026nbsp;Ontology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emissense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emissense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003estop gained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eframeshift\u003c/p\u003e \u003cp\u003eframeshift\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGene Name\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDH23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMYO7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCLRN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSH2A\u003c/p\u003e \u003cp\u003eUSH2A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInheritance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecessive\u003c/p\u003e \u003cp\u003eRecessive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsher syndrome type 1D, CDH23-Related Disorders,\u003c/p\u003e \u003cp\u003eAutosomal recessive nonsyndromic hearing loss 12, Pituitary adenoma 5, Rare genetic deafness, Retinal dystrophy, \u003c/p\u003e \u003cp\u003eChildhood onset hearing loss, Usher syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRare genetic deafness, \u003c/p\u003e \u003cp\u003eAutosomal recessive nonsyndromic hearing loss 2, \u003c/p\u003e \u003cp\u003eUsher syndrome type 1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUsher syndrome type 3,\u003c/p\u003e \u003cp\u003eRare genetic deafness, \u003c/p\u003e \u003cp\u003eRetinitis pigmentosa 61; Usher syndrome type 3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUsher syndrome type 2A, \u003c/p\u003e \u003cp\u003eUSH2A-Related Disorders, \u003c/p\u003e \u003cp\u003eRetinal dystrophy, \u003c/p\u003e \u003cp\u003eRetinitis pigmentosa 39; Usher syndrome type 2A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIt is important to acknowledge certain limitations in our experiments. Firstly, miRNA expression may exhibit tissue specificity. Data exclusively collected from immortalized lymphocyte cell lines may not necessarily reflect miRNA expression in other tissues within the same patient or in non-immortalized cells [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Second, it's worth noting that miRNA expression patterns may likely vary depending on the patient's genotype. In our study, we observed significant differences in miRNA profiles within both USH1B and USH1D genotypes, despite both falling under the USH1 classification. This divergence is attributed to distinct sequence variants found in different genes, contributing to the observed phenotypic differences. Nevertheless, the analyzed miRNA panel demonstrated robust performance across multiple statistical tests, with some having prior support documented in the literature. These miRNAs offer a promising and novel set of candidates that warrant exploration in the context of Usher syndrome diagnostics.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis cell-line based study describes differential expression patterns of top 12 miRNA that may have important roles in pathophysiology of Usher Syndrome. Adopting both microarray and ddPCR techniques, we have shown levels of six upregulated and six down regulated miRNAs using transformed Lymphocyte cell lines from Usher patients and healthy controls. While some of these miRNAs showed unique expression patterns in all Usher types compared to control, others were distinctly assigned between Usher subtypes (1A and 1D). However, our findings necessitate further investigations involving actual patient population that may help unravel miRNAs regulatory mechanisms in neurodegenerative disorders impacting vision and hearing loss.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe targeted exome sequencing raw fastq files used in this study ar available through NCBI\u0026rsquo;s SRA repository under PRJNA1063720,\u0026nbsp; https://dataview.ncbi.nlm.nih.gov/object/PRJNA1063720?reviewer=9ag03uu7aek2b1p0ph0ngegpke. Raw data and R code for miRNA and ddPCR analysis are publically availably on github, https://github.com/westom21/Usher_miRNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Boys Town National Research Hospital, Omaha NE, USA (IRB protocol number: # 20-14-XP: Approval date: September 28th, 2020). Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article are available from the authors without any reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by a grant from Ryan Foundation, to MRF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMRF: Conceptualization, project supervision, funding acquisition, and writing of original draft. DSC: Research investigation and methodology: miRNA Microarrays and Droplet Digital-dPCR assays, sections in original draft, review and editing of manuscript. WT: DNA sequencing, data analysis and visualization, sections in original draft, review and editing of manuscript. CJ: Cell culture methods, review and editing of manuscript. GK: Blood samples and Lymphocyte separation methods, research coordination, review and editing of manuscript. NF: DNA purification from Blood and Lymphocyte cell lines, review and editing of manuscript. AO: Methodology, investigation, review and editing of manuscript. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to thank Dr. Dominic Cosgrove, Center for Sensory Neuroscience, Boys Town National Research Hospital, Omaha NE USA for providing Usher cell lines, his advice and help during this project. We gratefully acknowledge the assistance from Rebecca Cash during our IRB application. We want to thank Dan Meehan, Center for Sensory Neuroscience for technical assistance. We also extend our thanks to Jennifer Bushing, Genomics Core Facility, University of Nebraska Medical Center, Omaha NE USA for her technical assistance in NanoString miRNA microarray experiments. The UNMC Genomics Core Facility receives partial support from the National Institute for General Medical Science (NIGMS) INBRE - P20GM103427-19, as well as the National Cancer Institute and The Fred \u0026amp; Pamela Buffett Cancer Center Support Grant- P30CA036727. This publications\u0026rsquo; contents are the sole responsibility of the authors and do not necessarily represent the official views of the NIH or NIGMS. This research was funded by a research grant from the Ryan Foundation to MRF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWhatley, M. et al. Usher Syndrome: Genetics and Molecular Links of Hearing Loss and Directions for Therapy. Front. Genet. 11, 565216 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelmaghani, S. \u0026amp; El-Amraoui, A. The genetic and phenotypic landscapes of Usher syndrome: from disease mechanisms to a new classification. Hum. Genet. 141, 709\u0026ndash;735 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimberling, W. J. et al. Frequency of Usher syndrome in two pediatric populations: Implications for genetic screening of deaf and hard of hearing children. Genet. Med. Off. J. Am. Coll. Med. Genet. 12, 512\u0026ndash;516 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, Q. Y. et al. Digenic inheritance of deafness caused by mutations in genes encoding cadherin 23 and protocadherin 15 in mice and humans. Hum. Mol. Genet. 14, 103\u0026ndash;111 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, Q. Y. et al. Digenic inheritance of deafness caused by 8J allele of myosin-VIIA and mutations in other Usher I genes. Hum. Mol. Genet. 21, 2588\u0026ndash;2598 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLondin, E. et al. Analysis of 13 cell types reveals evidence for the expression of numerous novel primate- and tissue-specific microRNAs. Proc. Natl. Acad. Sci. U. S. A. 112, E1106-1115 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeston, M. D., Pierce, M. L., Rocha-Sanchez, S., Beisel, K. W. \u0026amp; Soukup, G. A. MicroRNA gene expression in the mouse inner ear. Brain Res. 1111, 95\u0026ndash;104 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarali, M., Peluso, I., Marigo, V. \u0026amp; Banfi, S. Identification and characterization of microRNAs expressed in the mouse eye. Invest. Ophthalmol. Vis. Sci. 48, 509\u0026ndash;515 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, H. \u0026amp; Fekete, D. M. MicroRNAs in hair cell development and deafness. Curr. Opin. Otolaryngol. Head Neck Surg. 18, 459\u0026ndash;465 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndreeva, K. \u0026amp; Cooper, N. G. F. MicroRNAs in the Neural Retina. Int. J. Genomics 2014, 165897 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUshakov, K., Rudnicki, A. \u0026amp; Avraham, K. B. MicroRNAs in sensorineural diseases of the ear. Front. Mol. Neurosci. 6, 52 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDambal, S., Shah, M., Mihelich, B. \u0026amp; Nonn, L. The microRNA-183 cluster: the family that plays together stays together. Nucleic Acids Res. 43, 7173\u0026ndash;7188 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, H., Kloosterman, W. \u0026amp; Fekete, D. M. MicroRNA-183 family members regulate sensorineural fates in the inner ear. J. Neurosci. Off. J. Soc. Neurosci. 30, 3254\u0026ndash;3263 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMittal, R. et al. Role of microRNAs in inner ear development and hearing loss. Gene 686, 49\u0026ndash;55 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang, L. et al. miR-183/96 plays a pivotal regulatory role in mouse photoreceptor maturation and maintenance. Proc. Natl. Acad. Sci. U. S. A. 114, 6376\u0026ndash;6381 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, C.-J. et al. Ablation of Mature miR-183 Leads to Retinal Dysfunction in Mice. Invest. Ophthalmol. Vis. Sci. 61, 12 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLumayag, S. et al. Inactivation of the microRNA-183/96/182 cluster results in syndromic retinal degeneration. Proc. Natl. Acad. Sci. U. S. A. 110, E507-516 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuman Genome Overview - Genome Reference Consortium. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/grc/human\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/grc/human\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, H. \u0026amp; Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinforma. Oxf. Engl. 25, 1754\u0026ndash;1760 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience 10, giab008 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeber, J. A., Aldana, R., Gallagher, B. D. \u0026amp; Edwards, J. S. Sentieon DNA pipeline for variant detection - Software-only solution, over 20\u0026times; faster than GATK 3.3 with identical results. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://peerj.com/preprints/1672\u003c/span\u003e\u003cspan address=\"https://peerj.com/preprints/1672\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7287/peerj.preprints.1672v2\u003c/span\u003e\u003cspan address=\"10.7287/peerj.preprints.1672v2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarSeqTM (Version 8.x) [Software]. Bozeman, MT: Golden Helix, Inc. Available from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.goldenhelix.com\u003c/span\u003e\u003cspan address=\"https://www.goldenhelix.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405\u0026ndash;424 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanouil, M. et al. NACHO: an R package for quality control of NanoString nCounter data. Bioinformatics 36, 970\u0026ndash;971 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampomenosi P, Gini E, Noonan DM, Poli A, D'Antona P, Rotolo N, Dominioni L, Imperatori A. A comparison between quantitative PCR and droplet digital PCR technologies for circulating microRNA quantification in human lung cancer. BMC Biotechnol. 2016;16(1):60. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12896-016-0292-7\u003c/span\u003e\u003cspan address=\"10.1186/s12896-016-0292-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 27538962; PMCID: PMC4991011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan, Linlin. ggvenn: Draw Venn Diagram by \u0026lsquo;ggplot2\u0026rsquo;. R package version 0.1.9. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu, Z. Complex heatmap visualization. iMeta 1, e43 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchultz, J. M. et al. Allelic hierarchy of CDH23 mutations causing non-syndromic deafness DFNB12 or Usher syndrome USH1D in compound heterozygotes. J. Med. Genet. 48, 767\u0026ndash;775 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Dhahouri, N., Ali, A., Hertecant, J. \u0026amp; Al-Jasmi, F. Case Report: Reinterpretation and Reclassification of ARSB:p.Arg159Cys Variant Identified in an Emirati Patient With Hearing Loss Caused by a Pathogenic Variant in the CDH23 Gene. Front. Pediatr. 9, 803732 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAstuto, L. M. et al. CDH23 mutation and phenotype heterogeneity: a profile of 107 diverse families with Usher syndrome and nonsyndromic deafness. Am. J. Hum. Genet. 71, 262\u0026ndash;275 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolz, H. et al. Mutation of CDH23, encoding a new member of the cadherin gene family, causes Usher syndrome type 1D. Nat. Genet. 27, 108\u0026ndash;112 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, L. et al. Next-generation sequencing-based molecular diagnosis of 82 retinitis pigmentosa probands from Northern Ireland. Hum. Genet. 134, 217\u0026ndash;230 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKannan-Sundhari, A. et al. Screening Consanguineous Families for Hearing Loss Using the MiamiOtoGenes Panel. Genet. Test. Mol. Biomark. 24, 674\u0026ndash;680 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSvidnicki, M. C. C. M. et al. Screening of genetic alterations related to non-syndromic hearing loss using MassARRAY iPLEX\u0026reg; technology. BMC Med. Genet. 16, 85 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBujakowska, K. M. et al. Targeted exon sequencing in Usher syndrome type I. Invest. Ophthalmol. Vis. Sci. 55, 8488\u0026ndash;8496 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaux, D. et al. UMD-USHbases: a comprehensive set of databases to record and analyse pathogenic mutations and unclassified variants in seven Usher syndrome causing genes. Hum. Mutat. 29, E76-87 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecirovic, E. et al. Usher syndrome type 1 due to missense mutations on both CDH23 alleles: investigation of mRNA splicing. Hum. Mutat. 29, 452 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonnet, C. et al. An innovative strategy for the molecular diagnosis of Usher syndrome identifies causal biallelic mutations in 93% of European patients. Eur. J. Hum. Genet. EJHG 24, 1730\u0026ndash;1738 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSloan-Heggen, C. M. et al. Comprehensive genetic testing in the clinical evaluation of 1119 patients with hearing loss. Hum. Genet. 135, 441\u0026ndash;450 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenassi, E. et al. Natural history and retinal structure in patients with Usher syndrome type 1 owing to MYO7A mutation. Ophthalmology 121, 580\u0026ndash;587 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Quesne Stabej, P. et al. Comprehensive sequence analysis of nine Usher syndrome genes in the UK National Collaborative Usher Study. J. Med. Genet. 49, 27\u0026ndash;36 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacobson, S. G. et al. Disease boundaries in the retina of patients with Usher syndrome caused by MYO7A gene mutations. Invest. Ophthalmol. Vis. Sci. 50, 1886\u0026ndash;1894 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuratti, E. et al. Aberrant 5\u0026rsquo; splice sites in human disease genes: mutation pattern, nucleotide structure and comparison of computational tools that predict their utilization. Nucleic Acids Res. 35, 4250\u0026ndash;4263 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuyang, X. M. et al. Characterization of Usher syndrome type I gene mutations in an Usher syndrome patient population. Hum. Genet. 116, 292\u0026ndash;299 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBharadwaj, A. K., Kasztejna, J. P., Huq, S., Berson, E. L. \u0026amp; Dryja, T. P. Evaluation of the myosin VIIA gene and visual function in patients with Usher syndrome type I. Exp. Eye Res. 71, 173\u0026ndash;181 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, M. Q. Statistical features of human exons and their flanking regions. Hum. Mol. Genet. 7, 919\u0026ndash;932 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026auml;stinsalo, H. et al. Extended mutation spectrum of Usher syndrome in Finland. Acta Ophthalmol. (Copenh.) 91, 325\u0026ndash;334 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsosomppi, J. et al. Disease-causing mutations in the CLRN1 gene alter normal CLRN1 protein trafficking to the plasma membrane. Mol. Vis. 15, 1806\u0026ndash;1818 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFields, R. R. et al. Usher syndrome type III: revised genomic structure of the USH3 gene and identification of novel mutations. Am. J. Hum. Genet. 71, 607\u0026ndash;617 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoensuu, T. et al. Mutations in a novel gene with transmembrane domains underlie Usher syndrome type 3. Am. J. Hum. Genet. 69, 673\u0026ndash;684 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoenekoop, R., Arriaga, M., Trzupek, K. M. \u0026amp; Lentz, J. Usher Syndrome Type II. in GeneReviews\u0026reg; (eds. Adam, M. P. et al.) (University of Washington, Seattle, 1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenassi, E. et al. A detailed clinical and molecular survey of subjects with nonsyndromic USH2A retinopathy reveals an allelic hierarchy of disease-causing variants. Eur. J. Hum. Genet. EJHG 23, 1318\u0026ndash;1327 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGee, T. L., Seyedahmadi, B. J., Sweeney, M. O., Dryja, T. P. \u0026amp; Berson, E. L. Novel mutations in the long isoform of the USH2A gene in patients with Usher syndrome type II or non-syndromic retinitis pigmentosa. J. Med. Genet. 47, 499\u0026ndash;506 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbermann, I. et al. PDZD7 is a modifier of retinal disease and a contributor to digenic Usher syndrome. J. Clin. Invest. 120, 1812\u0026ndash;1823 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbermann, I. et al. An USH2A founder mutation is the major cause of Usher syndrome type 2 in Canadians of French origin and confirms common roots of Quebecois and Acadians. Eur. J. Hum. Genet. EJHG 17, 80\u0026ndash;84 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandberg, M. A. et al. Disease course in patients with autosomal recessive retinitis pigmentosa due to the USH2A gene. Invest. Ophthalmol. Vis. Sci. 49, 5532\u0026ndash;5539 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeyedahmadi, B. J., Rivolta, C., Keene, J. A., Berson, E. L. \u0026amp; Dryja, T. P. Comprehensive screening of the USH2A gene in Usher syndrome type II and non-syndromic recessive retinitis pigmentosa. Exp. Eye Res. 79, 167\u0026ndash;173 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDreyer, B. et al. Identification of novel USH2A mutations: implications for the structure of USH2A protein. Eur. J. Hum. Genet. EJHG 8, 500\u0026ndash;506 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeston, M. D. et al. Genomic structure and identification of novel mutations in usherin, the gene responsible for Usher syndrome type IIa. Am. J. Hum. Genet. 66, 1199\u0026ndash;1210 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEudy, J. D. et al. Mutation of a gene encoding a protein with extracellular matrix motifs in Usher syndrome type IIa. Science 280, 1753\u0026ndash;1757 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZong, L., Chen, K., Wu, X., Liu, M. \u0026amp; Jiang, H. Compound heterozygous MYO7A mutations segregating Usher syndrome type 2 in a Han family. Int. J. Pediatr. Otorhinolaryngol. 90, 150\u0026ndash;155 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXing, D. et al. Targeted exome sequencing identified a novel USH2A mutation in a Chinese usher syndrome family: a case report. BMC Ophthalmol. 20, 485 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026auml;stinsalo, H. et al. Alternative splice variants of the USH3A gene Clarin 1 (CLRN1). Eur. J. Hum. Genet. 19, 30\u0026ndash;35 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeng, R. et al. The microRNA-183/96/182 Cluster is Essential for Stereociliary Bundle Formation and Function of Cochlear Sensory Hair Cells. Sci. Rep. 8, 18022 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuhn, S. et al. miR-96 regulates the progression of differentiation in mammalian cochlear inner and outer hair cells. Proc. Natl. Acad. Sci. U. S. A. 108, 2355\u0026ndash;2360 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewis, M. A. et al. An ENU-induced mutation of miR-96 associated with progressive hearing loss in mice. Nat. Genet. 41, 614\u0026ndash;618 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSold\u0026agrave;, G. et al. A novel mutation within the MIR96 gene causes non-syndromic inherited hearing loss in an Italian family by altering pre-miRNA processing. Hum. Mol. Genet. 21, 577\u0026ndash;585 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenc\u0026iacute;a, A. et al. Mutations in the seed region of human miR-96 are responsible for nonsyndromic progressive hearing loss. Nat. Genet. 41, 609\u0026ndash;613 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, S. et al. Mutation Screening in the miR-183/96/182 Cluster in Patients With Inherited Retinal Dystrophy. Front. Cell Dev. Biol. 8, 619641 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi, H.-P. et al. MicroRNA-28 potentially regulates the photoreceptor lineage commitment of M\u0026uuml;ller glia-derived progenitors. Sci. Rep. 7, 11374 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahmoudian-Sani, M.-R. et al. MicroRNAs: effective elements in ear-related diseases and hearing loss. Eur. Arch. Oto-Rhino-Laryngol. Off. J. Eur. Fed. Oto-Rhino-Laryngol. Soc. EUFOS Affil. Ger. Soc. Oto-Rhino-Laryngol. - Head Neck Surg. 274, 2373\u0026ndash;2380 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing, L. et al. Analysis of plasma microRNA expression profiles in male textile workers with noise-induced hearing loss. Hear. Res. 333, 275\u0026ndash;282 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, N. et al. miR-16-5p and miR-19b-3p prevent amyloid β-induced injury by targeting BACE1 in SH-SY5Y cells. Neuroreport 31, 205\u0026ndash;212 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong, E. H. et al. Differential expression of aqueous humor microRNAs in central retinal vein occlusion and its association with matrix metalloproteinases: a pilot study. Sci. Rep. 12, 16429 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, D.-H., Gu, C. \u0026amp; Yuan, Y.-Z. Case Report: A Case of Cystoid Macular Edema in Retinitis Pigmentosa With Central Retinal Vein Occlusion. Front. Med. 9, (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmenar-P\u0026eacute;rez, E., Sarr\u0026iacute;a, L., Nathanson, L. \u0026amp; Oltra, E. Assessing diagnostic value of microRNAs from peripheral blood mononuclear cells and extracellular vesicles in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Sci. Rep. 10, 2064 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Usher syndrome, Biomarkers, miRNAs, Micro-array, Droplet digital PCR","lastPublishedDoi":"10.21203/rs.3.rs-3826668/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3826668/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eUsher syndrome (USH) is an inherited disorder characterized by sensorineural hearing loss (SNHL), retinitis pigmentosa (RP)-related vision loss, and vestibular dysfunction. USH presents itself as three distinct clinical types 1, 2 and 3, with no biomarker for early detection. This study aimed to explore novel microRNA (miRNA) biomarkers for USH by comparing miRNA expression patterns in cell lines derived from USH patients and control subjects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eLymphocytes from USH patients and healthy individuals were isolated and transformed into stable cell lines using Epstein-Barr virus (EBV). DNA from these cell lines was sequenced using a targeted panel to identify gene variants associated with USH types 1, 2, and 3. Microarray analysis was performed on RNA from both USH and control cell lines using NanoString miRNA microarray technology. Dysregulated miRNAs identified by the microarray were validated using droplet digital PCR technology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e DNA sequencing revealed that two USH patients had USH type 1 with gene variants in USH1B (MYO7A) and USH1D (CDH23), while the other two patients were classified as USH type 2 (USH2A) and USH type 3 (CLRN-1), respectively. The NanoString miRNA microarray detected 92 differentially expressed miRNAs in USH cell lines compared to controls. Significantly altered miRNAs exhibited at least a twofold increase or decrease with a \u003cem\u003ep\u003c/em\u003e value below 0.05. Among these miRNAs, 20 were specific to USH1, 14 to USH2, and 5 to USH3. Three miRNAs that are known as miRNA-183-family which are crucial for inner ear and retina development have been significantly down regulated as compared to control cells. Subsequently, droplet digital PCR assays confirmed the dysregulation of twelve most prominent miRNAs in USH cell lines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThis study identifies several miRNAs with differential expression in USH patients and their potential utility as biomarkers for Usher syndrome.\u003c/p\u003e","manuscriptTitle":"Characterization of Usher Syndrome Cell line Genotypes and Elucidation of Novel MicroRNA Biomarkers using MicroRNA Microarray and Droplet Digital PCR","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-18 15:45:22","doi":"10.21203/rs.3.rs-3826668/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f35b378-8a2b-4666-81e7-1c22fe817f59","owner":[],"postedDate":"January 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28189087,"name":"Health sciences/Biomarkers"},{"id":28189088,"name":"Health sciences/Molecular medicine"}],"tags":[],"updatedAt":"2024-08-02T17:47:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-18 15:45:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3826668","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3826668","identity":"rs-3826668","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}