DNA Methylation Signature in Mononuclear Cells and Proinflammatory Cytokines May Define Molecular Subtypes in Sporadic Meniere Disease

biomedicines Article DNA Methylation Signature in Mononuclear Cells and Proinflammatory Cytokines May Define Molecular Subtypes in Sporadic Meniere Disease Marisa Flook 1,2,3,† , Alba Escalera-Balsera 1,2,3,† , Alvaro Gallego-Martinez 1,2,3 , Juan Manuel Espinosa-Sanchez 1,2,3 , Ismael Aran 4, Andres Soto-Varela 5 and Jose Antonio Lopez-Escamez 1,2,3,6,*   Citation: Flook, M.; Escalera-Balsera, A.; Gallego-Martinez, A.; Espinosa-Sanchez, J.M.; Aran, I.; Soto-Varela, A.; Lopez-Escamez, J.A. DNA Methylation Signature in Mononuclear Cells and Proinflammatory Cytokines May Define Molecular Subtypes in Sporadic Meniere Disease. Biomedicines 2021,9, 1530. https://doi.org/10.3390/ biomedicines9111530 Academic Editor: David G. Alleva Received: 25 September 2021 Accepted: 21 October 2021 Published: 25 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Otology & Neurotology Group CTS495, Department of Genomic Medicine, GENYO, Centre for Genomics and Oncological Research, Pfizer University of Granada Andalusian Regional Government, PTS, 18016 Granada, Spain; [email protected] (M.F.); [email protected] (A.E.-B.); [email protected] (A.G.-M.); [email protected] (J.M.E.-S.) 2Sensorineural Pathology Programme, Centro de Investigación Biomédica en Red en Enfermedades Raras, CIBERER, 28029 Madrid, Spain 3Department of Otolaryngology, Instituto de Investigación Biosanitaria ibs.Granada, Hospital Universitario Virgen de las Nieves, Universidad de Granada, 18014 Granada, Spain 4Department of Otolaryngology, Complexo Hospitalario de Pontevedra, 36071 Pontevedra, Spain; [email protected] 5Division of Otoneurology, Department of Otorhinolaryngology, Complexo Hospitalario Universitario, 15706 Santiago de Compostela, Spain; [email protected] 6Division of Otolaryngology, Department of Surgery, University of Granada, 18011 Granada, Spain *Correspondence: [email protected]; Tel.: +34-958-715-500-160 † These authors contributed equally to this work. Abstract: Meniere Disease (MD) is a multifactorial disorder of the inner ear characterized by vertigo attacks associated with sensorineural hearing loss and tinnitus with a significant heritability. Although MD has been associated with several genes, no epigenetic studies have been performed on MD. Here we performed whole-genome bisulfite sequencing in 14 MD patients and six healthy controls, with the aim of identifying an MD methylation signature and potential disease mechanisms. We observed a high number of differentially methylated CpGs (DMC) when comparing MD patients to controls (n= 9545), several of them in hearing loss genes, such as PCDH15, ADGRV1 and CDH23. Bioinformatic analyses of DMCs and cis-regulatory regions predicted phenotypes related to abnormal excitatory postsynaptic currents, abnormal NMDA-mediated receptor currents and abnormal glutamate-mediated receptor currents when comparing MD to controls. Moreover, we identified various DMCs in genes previously associated with cochleovestibular phenotypes in mice. We have also found 12 undermethylated regions (UMR) that were exclusive to MD, including two UMR in an inter CpG island in the PHB gene. We suggest that the DNA methylation signature allows distinguishing between MD patients and controls. The enrichment analysis confirms previous findings of a chronic inflammatory process underlying MD. Keywords: Meniere Disease; cytokines; WGBS; hearing loss; DNA methylation 1. Introduction Meniere Disease (MD, MIM 156000) is a chronic disorder of the inner ear that consists of episodes of spontaneous vertigo, usually associated with low to middle-frequency sensorineural hearing loss (SNHL), tinnitus and/or aural fullness [ 1 ]. The disorder is a heterogeneous condition that usually begins in one ear with tinnitus and hearing loss, but it can involve both ears and produce bilateral symptoms in up to 40% of patients [2]. Epidemiological and familial aggregation studies suggest that MD is a multifactorial disorder with a significant heritability [ 3 ]. The condition is polygenic, including sporadic Biomedicines 2021,9, 1530. https://doi.org/10.3390/biomedicines9111530 https://www.mdpi.com/journal/biomedicines Biomedicines 2021,9, 1530 2 of 18 and familial cases. Several genes, such as DTNA, FAM136A, PRKCB, DTP, and SEMA3D, have been associated in multiplex families with autosomal dominant inheritance with incomplete penetrance [ 4 ]. In addition, six families with rare missense variants in the OTOG gene have been reported, supporting an autosomal recessive compound heterozygous inheritance [5]. However, the majority of patients with MD are considered sporadic and there is growing evidence to support a central role of the immune response in MD [ 6 ]. Our group found two subgroups of MD patients according to the baseline levels of IL-1 β , differentiating patients with high levels of IL-1 β (MDH) and patients with low levels (MDL), who in turn may have different immune response profiles to antigens or even differences in the functional status of the immune system [7]. Methylation of cytosines in the DNA strand is a stable epigenetic mechanism essential in regulating gene expression and determining the phenotype of a cell. There is numerous evidence demonstrating the nature of epigenetics to human biology and pathology. Despite extensive research in methylation, little work has been conducted examining how epigenetic changes affect gene expression in hearing. Epigenetics and, therefore, DNA methylation, could play an important role in hearing-related diseases that have no identifiable perturbation to the DNA sequence, even in those with known mutations, epigenetic modifications could be important to phenotypic differences [8,9]. Previous DNA methylation studies on hearing loss with humans were made with different technologies: Reduced Representation Bisulfite Sequencing, arrays and methylationspecific PCR [ 10 – 13 ]. Nevertheless, Whole Genome Bisulfite Sequencing (WGBS) is perhaps the most powerful method to interrogate the methylome as it potentially allows investigation of every 5 0 —C—phosphate—G—3 0 (CpG) site in the genome (20–22 million CpGs are usually covered in the mappable human genome) [ 8 ]. Yizhar-Barnea et al. [ 14 ] used WGBS to obtain the first DNA methylome map of the mouse inner ear sensory epithelium, which revealed novel regulatory regions in the hearing organ. In this study, we performed WGBS in patients with MD and healthy controls with the aim of identifying an MD methylation pattern and potential disease mechanisms. 2. Materials and Methods 2.1. Human Subjects We included a total of 14 patients with definite MD and six healthy controls that were recruited between January and July 2019, from Spanish referral centers. Patients were diagnosed according to the diagnostic criteria of the Barany Society for MD [ 1 ]. The experimental protocols of this study were approved by the Institutional Review Board in all participating hospitals and every patient signed written informed consent. The study was carried out according to the principles of the Declaration of Helsinki revised in 2013 for investigation with humans. 2.2. Clinical Data A descriptive analysis was conducted using IBM SPSS Statistics v19 (IBM Corp, Armonk, NY, USA) for all clinical data. Patients were classified according to the cytokine levels and clinical variables were compared between both groups by applying Pearson’s chi-square test for qualitative variables and Student’s t-test for the quantitative ones. The level of significance considered was p-value < 0.05. 2.3. DNA Extraction DNA was extracted from peripheral blood mononuclear cells (PBMCs) using the QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany), following the manufacturer’s protocol. DNA concentration and quality parameters were verified by Nanodrop (Thermo Fisher, Waltham, MA, USA) and Qubit (Invitrogen, Waltham, MA, USA) as previously described [ 15 ]. Additionally, DNA integrity was verified by electrophoresis in a 2% agarose gel. For WGBS the minimum parameters considered were a concentration superior to Biomedicines 2021,9, 1530 3 of 18 20 ng/µL, a 260/280 ratio superior to 1.8 and no observable smearing/DNA degradation by electrophoresis. 2.4. WGBS Library Preparation WGBS was carried out in 20 samples (7 MDH, 7 MDL and six healthy controls) by Macrogen (Seoul, Korea). Briefly, Accel-NGS Methyl-Seq DNA Library Kit (Zymo Research, Irvine, CA, USA) was used to prepare NGS libraries from bisulfite-converted DNA for sequencing [ 16 ]. For this, the samples were treated with bisulfite to convert the unmethylated cytosines to uracils, while retaining the methylated. This was followed by an Adaptase step that performed tailing and ligation of truncated adapters to 3 0 ends. The extension and ligation steps added truncated adapters, which was followed by an indexing PCR step increasing the yield and incorporating full-length adapters for single or dual indexing. Finally, bead-based clean-ups removed oligonucleotides and small fragments. 2.5. WGBS Data Analysis After sequencing on a NovaSeq 6000 system, raw sequence reads were filtered based on quality and adapter sequences were trimmed. Those sequences were mapped to the reference genome with BSMAP [ 17 ], based on the SOAP (Short Oligo Alignment Program). The only uniquely mapped reads were selected to sort and index, and PCR duplicates were removed with SAMBAMBA (v0.5.9) [18]. The reference genome used was hg19, then we decided to perform a lift over in the CpGs coordinates to the hg38 reference genome through the LiftOver tool from USCS (http://genome.ucsc.edu/, accessed on 4 June 2020) . CpG sites with less coverage than 10X, more methylation than 99.9% and not appearing in all the samples were filtered. The methylation ratio of every single cytosine location was extracted from the mapping results using the methylKit R package [ 19 ]. The coverage profile results were calculated as the number of C/effective CT counts for each cytosine in CpG. The batch effect was corrected by the origin of patients, with the limma R package [ 20 ]. Each Differentially Methylated CpG (DMC) was annotated by Bedtools [ 21 ] intersected using the comprehensive gene annotation on the reference chromosome from Gencode v33 [ 22 ], this included the functional location of each gene, gene ID and strand. Besides, DMCs in promoters were annotated using the Bedtools window with the same annotation file. Promoters were defined as the 1000 bp region before the transcriptional start site. Furthermore, for DMC, different comparisons between MDH, MDL and controls were performed and filtered out through statistical hypothesis testing using independent Student’s t-test for sites with a minimum 8% difference in methylation. We considered significant a False Discovery Rate (FDR) adjusted p-value < 0.05. For differentially methylated regions (DMR), the calling radmeth command-line tool in the Methpipe software package was used [ 23 ]. CpG sites with more methylation than 99.9% and not appearing in all the samples were filtered. The tool takes into account the coverage for each CpG site, so coverage was used from 1X. After that, p-values were corrected by the p-values of its neighbors located at a distance of 200 from each other, using the parameter 1:200:1. We required DMRs to contain at least two DMCs, a minimum of 8% difference in methylation and a corrected p-value < 0.05 [24,25]. Genes and promoters were annotated using Bedtools. 2.6. Undermetlylated Regions For each sample, CpG sites with coverage below 10X were filtered. The R package methylSeekR was used [ 26 ]; firstly, partially methylated domains (PMDs) were identified by a hidden Markov model and they were discarded. The following criteria were applied to identify undermethylated regions (UMRs): FDR < 5% for regions and average DNA methylation < 10%. Using multiIntersectBed from BedTools [ 21 ] overlapping UMRs in MD samples and in control samples were found, those UMRs that appear in more than the 75% of MD samples and not in control samples were selected for further analyses. Biomedicines 2021,9, 1530 4 of 18 2.7. Inner Ear Gene Sets DMCs from the mapped genes for each comparison (MD patients vs. controls, MDH vs. controls, and MDL vs. controls), were filtered by the following gene sets: Sensorineural hearing loss genes retrieved from Deafness variation database (https: //deafnessvariationdatabase.org/, accessed on 10 April 2021), referred from now on as HL gene set (n= 224); genes showing a burden of rare variants in sporadic MD referred from now on as SMD gene set (n= 70) [ 27 ], and differentially expressed genes (DEGs) in the mouse stria vascularis single cell RNAseq dataset [ 28 ], referred from now on as SV gene set (n= 217). 2.8. Functional Analysis Goseq package was used with the aim of defining biological pathways, processes and functions using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases [ 29 ]. To correct the bias that occurs due to different gene lengths and distinct number of CpGs per gene after filtering, a bias value for each gene was formulated considering the weight of the number of CpGs in that gene after filtering summed to the inverse of the mean of the absolute values of differential methylation (DM) for all the DMCs in the gene: bias value =nCpGs +1 ∑|∆DM| nDMC So, genes with more CpGs have a bigger bias and genes with DMCs with higher differential methylation would have a lower bias value. This functional analysis was also done separating the genes based on whether they contained hypomethylated or hypermethylated DMCs, in each case the bias value was calculated for those DMCs. The Genomic Regions Enrichment of Annotations Tool (GREAT) version 4.0.4 (http://great.stanford.edu/public/html/, accessed on 17 May 2021) was used with the output of Methpipe software package, this is DMCs and DMRs coordinates, filtered for a minimum of 8% difference in methylation and a FDR adjusted p-value < 0.05. Gene regulatory domain definition was set as: 5 kb upstream, 1 kb downstream and a plus distal of 1 kb [ 30 ]. Results were considered significant if both the binomial test over geometric regions and the hypergeometric test over genes produced FDR q-values below 0.05, and if binomial fold enrichment is over 2. The findMotifsGenome functionality from HOMER Motif Analysis software was used to perform the Transcription Factor motif enrichment [31]. Lastly, a transcription factor enrichment analysis was carried out with Genecodis [ 32 ] using a hypergeometric test. 2.9. Visualizations The following R packages were used for the visualizations: circlize for circus plot [ 33 ]; annotatr for the distribution of CpG islands [ 34 ]; pheatmap for the heatmap [ 35 ], which contains a hierarchical clustering with euclidean distance; VennDiagram for the Venn Diagrams [ 36 ]; and ggplot2 [ 37 ], dplyr [ 38 ], forcats [ 39 ] and ggpubr [ 40 ] for the remaining visualizations. 3. Results 3.1. Patient Clinical History Table 1shows the clinical features of 14 MD patients (seven MDH and seven MDL) and six healthy controls. No differences were found for any of the controlled variables (p-value > 0.071) . Patients with MD can be classified into different clinical subgroups according to several comorbidities, such as migraine or autoimmune disorders. Most patients belong to the clinical subtype 1 of MD, independently of the level of cytokines Biomedicines 2021,9, 1530 5 of 18 (p-value = 0.401) . One of the patients suffers from antiphospholipid syndrome, an autoimmune disorder (subtype 5 of MD). Table 1. Clinical and demographic variables were assessed in patients with Meniere Disease with high levels of IL-1 β (MDH), Meniere Disease with low levels of IL-1β(MDL) and controls. Variable MDH (n= 7) MDL (n= 7) Controls (n= 6) p-Value Age (mean ±SD) 59.6 ±11.4 46.0 ±11.8 51.2 ±13.8 0.11 Age of onset (mean ±SD) 50.2 ±9.9 37.6 ±12.4 - 0.07 Sex (% female) 42.9 (3) 71.4 (5) 33.3 (2) 0.35 Laterality (% unilateral) 28.6 (2) 57.1 (4) - 0.39 Ear Family History (%) 0 (0) 14.3 (1) - 0.36 Migraine (%) 14.3 (1) 14.3 (1) - 0.91 History of autoimmune disease (%) 0 (0) 14.3 (1) - 0.30 Clinical Subtype (%) 1 (no autoimmune disorder) 83.3 (5) 71.4 (5) - 0.40 2 (delayed MD) 0 (0) 14.3 (1) - 3 (familial history of MD) 0 (0) 0 (0) - 4 (MD and migraine) 16.7 (1) 0 (0) - 5 (MD with autoimmune disorder) 0 (0) 14.3 (1) - 3.2. Screening DNA Methylation in Mononuclear Cells in Sporadic Meniere Disease WGBS was performed to compare the methylation profile in mononuclear cells of patients with MDH, MDL and controls. A total of 53,505,405 CpG sites were identified, after quality filtering for a minimum of 10X coverage per sample and methylation below 99.9%, 704,312 sites remained. We observed that differentially hypomethylated sites were more frequent along the genome (Figure 1A). We defined the distribution of the differentially methylated sites according to the functional position (promoter, intronic, intergenic, exonic, 5 0 UTR, 3 0 UTR) (Figure 1B) and we found that most sites were intronic (49.19% of hypomethylated DMCs and 3.81% of hypermethylated DMCs) and least frequently in the 5 0 UTR (0.02% of hypomethylated DMCs). We defined the distribution of DMCs across CpG islands and their neighboring regions and observed that CpG islands were the only region where there were more hypermethylated DMC (0.07%) than hypomethylated DMC (0.04%) (Figure 1C). Biomedicines 2021, 9, x FOR PEER REVIEW 6 of 21 Figure 1. Genome wide methylation differences between Meniere Disease (MD) and controls. (A) circos plot representative of the distribution of the hypomethylated DMCs (blue) and hypermethylated DMCs (pink) per chromosome. (B) bar plot indicating the percentages of hypomethylated DMCs (blue) and hypermethylated DMCs (pink) according to the genetic region (intronic, exonic, intergenic, 5′ UTR, 3′ UTR, promoter); (C) bar plot indicating the percentages of hypomethylated DMCs (blue) and hypermethylated DMCs (pink) per island features (inter CpG island, CpG shore, CpG shelves, CpG island). Figure 1. Genome wide methylation differences between Meniere Disease (MD) and controls. ( A ) circos plot representative of the distribution of the hypomethylated DMCs (blue) and hypermethylated DMCs (pink) per chromosome. ( B ) bar plot indicating the percentages of hypomethylated DMCs (blue) and hypermethylated DMCs (pink) according to the genetic region (intronic, exonic, intergenic, 5 0 UTR, 3 0 UTR, promoter); ( C ) bar plot indicating the percentages of hypomethylated DMCs (blue) and hypermethylated DMCs (pink) per island features (inter CpG island, CpG shore, CpG shelves, CpG island) . Biomedicines 2021,9, 1530 6 of 18 CpG methylation hierarchical clustering showed that MD patients form a different group than healthy controls and patients with MDH and MDL are also clustered separately (Figure 2A). Biomedicines 2021, 9, x FOR PEER REVIEW 7 of 21 Figure 2. Differentially methylated CpGs and genes in Meniere Disease patients and controls. (A) The rows are the methylation ratio (from 0 to 1) of each position and the columns are representative of each individual, which are classified by control, MDH (Meniere Disease High) and MDL (Meniere Disease Low). The positions represented were those with a higher absolute value of the difference in methylation: 150 DMCs (differentially methylated CpGs) that show the difference between Meniere Disease and control (Table S2), 250 DMCs for MDH (Table S3) and 100 DMCs for MDL (Table S4); (B) Venn diagram of DMCs (DM > 8%) comparing Meniere Disease to controls (MD), Meniere disease with high levels of cytokines to controls (MDH) and Meniere Disease with low levels of cytokines to controls (MDL); (C) Venn diagram of annotated genes from DMC analysis comparing Meniere Disease to controls (MD), Meniere disease with high levels of cytokines to controls (MDH) and Meniere Disease with low levels of cytokines to controls (MDL). 3.3. Undermethylated Regions in Meniere Disease Figure 2. Differentially methylated CpGs and genes in Meniere Disease patients and controls. ( A ) The rows are the methylation ratio (from 0 to 1) of each position and the columns are representative of each individual, which are classified by control, MDH (Meniere Disease High) and MDL (Meniere Disease Low). The positions represented were those with a higher absolute value of the difference in methylation: 150 DMCs (differentially methylated CpGs) that show the difference between Meniere Disease and control (Table S2), 250 DMCs for MDH (Table S3) and 100 DMCs for MDL (Table S4); ( B ) Venn diagram of DMCs (DM > 8%) comparing Meniere Disease to controls (MD), Meniere disease with high levels of cytokines to controls (MDH) and Meniere Disease with low levels of cytokines to controls (MDL); ( C ) Venn diagram of annotated genes from DMC analysis comparing Meniere Disease to controls (MD), Meniere disease with high levels of cytokines to controls (MDH) and Meniere Disease with low levels of cytokines to controls (MDL). Biomedicines 2021,9, 1530 7 of 18 3.3. Undermethylated Regions in Meniere Disease The UMRs (<10% average methylation) in MD and control genomes were retrieved and mapped to the different genomic regions. There was a significant difference in the number of UMR between MD patients and controls in all genomic regions ( p-value < 0.0069 ). We also observed that CpG shores displayed the highest number of UMR (Figure 3). Biomedicines 2021, 9, x FOR PEER REVIEW 8 of 21 The UMRs (<10% average methylation) in MD and control genomes were retrieved and mapped to the different genomic regions. There was a significant difference in the number of UMR between MD patients and controls in all genomic regions (p-value < 0.0069). We also observed that CpG shores displayed the highest number of UMR (Figure 3). Figure 3. Boxplot representing the genomic region distribution of undermethylated regions (UMR) in MD and controls. Student’s t-test was used to calculate the p-value in each comparison. Next, UMRs were filtered to identify which were found in at least 75% of patients with MD, but not in controls. Namely, we identified two UMRs in the inter CpG region of the PHB gene (Table S1). 3.4. Mapping Differential Methylated Sites We found a total of 19,055 DMCs when comparing MDH patients to healthy controls (p-value < 0.05) (Figure 2B, Table S3). A total of 10,333 DMCs were uniquely found when comparing MDH to controls (p-value < 0.05), which were mapped to 1721 genes (Figure 2C). For MD compared to controls, we identified 124 DMRs with two or more DMCs and 96 DMCs or DMRs that were mapped to promoter regions (Table S5 and S8). Only the IL9RP3 gene had both mapped DMR and DMC in the promoter region. Comparing MDH to controls, we identified 144 DMRs with two or more DMCs and 106 DMCs or DMRs that were mapped to promoter regions (Table S6 and S9). Three genes H3Y1, ACSBG1 and IL32 had both mapped DMR and DM in the promoter region. We identified 36 DMRs with two or more DMCs, and 38 DMCs or DMRs that were mapped to promoter regions, when comparing MDL to controls, none of which were found in the same gene (Table S7 and S10). In all the comparisons the difference in methylation was greater than 8%. 3.5. Hearing Loss Gene Sets Figure 3. Boxplot representing the genomic region distribution of undermethylated regions (UMR) in MD and controls. Student’s t-test was used to calculate the p-value in each comparison. Next, UMRs were filtered to identify which were found in at least 75% of patients with MD, but not in controls. Namely, we identified two UMRs in the inter CpG region of the PHB gene (Table S1). 3.4. Mapping Differential Methylated Sites We found a total of 19,055 DMCs when comparing MDH patients to healthy controls (p-value < 0.05) (Figure 2B, Table S3). A total of 10,333 DMCs were uniquely found when comparing MDH to controls (p-value < 0.05), which were mapped to 1721 genes (Figure 2C). For MD compared to controls, we identified 124 DMRs with two or more DMCs and 96 DMCs or DMRs that were mapped to promoter regions (Table S5 and S8). Only the IL9RP3 gene had both mapped DMR and DMC in the promoter region. Comparing MDH to controls, we identified 144 DMRs with two or more DMCs and 106 DMCs or DMRs that were mapped to promoter regions (Table S6 and S9). Three genes H3Y1, ACSBG1 and IL32 had both mapped DMR and DM in the promoter region. We identified 36 DMRs with two or more DMCs, and 38 DMCs or DMRs that were mapped to promoter regions, when comparing MDL to controls, none of which were found in the same gene (Table S7 and S10). In all the comparisons the difference in methylation was greater than 8%. Biomedicines 2021,9, 1530 8 of 18 3.5. Hearing Loss Gene Sets We filtered the mapped DMCs between MD and control by three gene sets: (a) Sensorineural hearing los genes retrieved from Deafness variation database (https:// deafnessvariationdatabase.org/, accessed on 10 April 2021), (b) genes showing a burden of rare variants in sporadic MD (SMD) and (c) differentially expressed genes (DEGs) according to mouse stria vascularis single cell RNAseq dataset. We found 68 DMCs (adjusted p-value < 0.05 ) that were mapped to 35 stria vascularis genes, 29 DMCs (adjusted p-value < 0.05 ) that were mapped to 12 SMD genes and 60 DMCs (adjusted p-value < 0.05) that were mapped to 30 hearing loss genes (Figure 4A, Table S11). DMXL2 was the only gene shared between the three gene sets that had differential methylation between MD and controls (Figure 4A). Biomedicines 2021, 9, x FOR PEER REVIEW 10 of 21 Figure 4. Differentially methylated CpGs present in inner ear gene sets. (A) Venn diagram representative of the number of genes in the Stria Vascularis (SV), HL (hearing loss) and Sporadic Meniere Disease (SMD) gene sets, which have DMC when comparing MD patients to controls. (B) boxplot representing the mean methylation of each CpG in the genes present in the Stria Vascularis (SV), HL (hearing loss) and Sporadic Meniere Disease (SMD) gene sets in controls (blue), all Meniere disease patients (yellow), Meniere disease patients with high cytokines (MDH) (orange) and Meniere disease patients with low cytokines (MDL) (green). Independent Student’s t-test was used to calculate the p-value in each comparison (Table S12). ** - p-value < 0.01; *** - p-value < 0.001; **** - p-value < 0.0001. Figure 4. Differentially methylated CpGs present in inner ear gene sets. ( A ) Venn diagram representative of the number of genes in the Stria Vascularis (SV), HL (hearing loss) and Sporadic Meniere Disease (SMD) gene sets, which have DMC when comparing MD patients to controls. ( B ) boxplot representing the mean methylation of each CpG in the genes present in the Stria Vascularis (SV), HL (hearing loss) and Sporadic Meniere Disease (SMD) gene sets in controls (blue), all Meniere disease patients (yellow), Meniere disease patients with high cytokines (MDH) (orange) and Meniere disease patients with low cytokines (MDL) (green). Independent Student’s t-test was used to calculate the p-value in each comparison (Table S12). **—p-value < 0.01; ***—p-value < 0.001; ****—p-value < 0.0001. Biomedicines 2021,9, 1530 9 of 18 We calculated the mean of all CpGs mapped to the genes in the above-mentioned gene sets. We observed that there were significant differences in all disease groups when compared to controls (p-value < 5.7 × 10 −7 ) and that MD patients had generally lower methylation in those genes (Figure 4B). Moreover, we observed that MDH patients were less methylated in those genes than MDL patients (p-value < 0.006) (Figure 4B). A list of the top 10 DMCs for each comparison and its corresponding positions with a higher difference in methylation can be found in Table 2. Table 2. Top 10 DMCs ranked according to ∆ Mean value found in hearing loss (HL), sporadic Meniere disease (SMD) and stria vascularis (SV) gene sets when comparing MD patients to controls. p-value is adjusted by FDR. ∆ Mean—difference in methylation between MD patients and controls. Gene Set Gene Protein Activity or Function/Location Position ∆Mean p-Value HL MSRB3 Reduction of methionine sulfoxide to methionine chr12:65397684 −0.20 5.77 ×10−3 PTPRQ Plasma membrane tyrosine phosphatase receptor chr12:80550423 −0.18 6.49 ×10−3 ADGRV1 G-protein coupled receptor, binds calcium chr5:90721360 −0.15 5.73 ×10−3 ADGRV1 G-protein coupled receptor, binds calcium chr5:90665789 −0.15 2.64 ×10−2 MSRB3 Reduction of methionine sulfoxide to methionine chr12:65440113 −0.15 9.32 ×10−3 CACNA1D Voltage-dependent calcium channel chr3:53684153 −0.14 9.09 ×10−4 USH2A Usherin—maintenance of the hair bundle ankle formation chr1:215677582 −0.13 6.81 ×10−5 LMX1A Transcriptional activator chr1:165321950 −0.13 2.04 ×10−4 PCDH15 Membrane protein that mediates calcium-dependent cell-cell adhesion chr10:54924915 −0.12 1.83 ×10−4 ATP2B2 Intracellular calcium homeostasis chr3:10545443 −0.12 1.85 ×10−5 SMD ADGRV1 G-protein coupled receptor, binds calcium chr5:90721360 −0.15 5.73 ×10−3 ADGRV1 G-protein coupled receptor, binds calcium chr5:90665789 −0.15 2.64 ×10−2 ADAM12 Cell-cell and cell-matrix interactions chr10:126355102 −0.13 3.83 ×10−4 PCDH15 Membrane protein that mediates calcium-dependent cell-cell adhesion chr10:54924915 −0.12 1.83 ×10−4 TPTE Signal transduction chr21:10561174 −0.12 1.78 ×10−2 MPDZ AMPAR potentiation and synaptic plasticity in excitatory synapses chr9:13106557 −0.10 8.14 ×10−3 PCDH15 Membrane protein that mediates calcium-dependent cell-cell adhesion chr10:54280633 −0.10 2.93 ×10−4 CFTR Chloride channel chr7:117360906 −0.10 2.56 ×10−4 ATM Cell cycle checkpoint kinase chr11:108237615 0.10 1.29 ×10−2 PCDH15 Membrane protein that mediates calcium-dependent cell-cell adhesion chr10:55026981 −0.10 5.23 ×10−3 SV ROBO2 Axon guidance and cell migration chr3:76840338 0.25 9.46 ×10−3 ROBO2 Axon guidance and cell migration chr3:76611689 −0.20 2.71 ×10−4 NFKB1 Pleiotropic transcription factor chr4:102589956 0.19 4.21 ×10−2 DLC1 Regulation of small GTP-binding proteins chr8:13480274 0.16 1.79 ×10−4 BMPR1B Transmembrane serine/threonine kinases receptor chr4:95037875 −0.16 3.75 ×10−2 DLC1 Regulation of small GTP-binding proteins chr8:13446699 −0.16 8.60 ×10−5 ROBO1 Mediates cellular responses to molecular guidance cues chr3:79605304 −0.16 3.69 ×10−3 DLC1 Regulation of small GTP-binding proteins chr8:13522539 −0.15 1.82 ×10−2 PARD3 Asymmetrical cell division and cell polarization processes chr10:34349620 −0.14 3.23 ×10−3 ROBO1 Mediates cellular responses to molecular guidance cues chr3:79389106 −0.14 4.67 ×10−2 Biomedicines 2021,9, 1530 16 of 18 Informed Consent Statement: Informed consent was obtained from all subjects involved in the study . Data Availability Statement: The dataset supporting the conclusions of this article is available in the European Nucleotide Archive (ENA; hosted by the EBI) under the project ID PRJEB45377 and can be accessed via the following link https://www.ebi.ac.uk/ena/browser/view/PRJEB45377, 31 May 2021. Acknowledgments: Marisa Flook is a student in the Biomedicine Program at the University of Granada and this work is part of her thesis. Conflicts of Interest: The authors declare no conflict of interest. References 1. Lopez-Escamez, J.A.; Carey, J.; Chung, W.-H.; Goebel, J.A.; Magnusson, M.; Mandalà, M.; Newman-Toker, D.E.; Strupp, M.; Suzuki, M.; Trabalzini, F.; et al. Diagnostic Criteria for Menière’s Disease. J. Vestib. Res. 2015,25, 1–7. [CrossRef] [PubMed] 2. Lopez-Escamez, J.A.; Viciana, D.; Garrido-Fernandez, P. Impact of Bilaterality and Headache on Health-Related Quality of Life in Meniere’s Disease. Ann. Otol. Rhinol. Laryngol. 2009,118, 409–416. [CrossRef] 3. 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