DNA methylation dynamics during embryonic development and postnatal maturation of the mouse auditory sensory epithelium

Yizhar-Barnea, Ofer; Valensisi, Cristina; Jayavelu, Naresh Doni; Kishore, Kamal; Andrus, Colin; Koffler-Brill, Tal; Ushakov, Kathy; Perl, Kobi; Noy, Yael; Bhonker, Yoni; Pelizzola, Mattia; Hawkins, R. David; Avraham, Karen B.

doi:10.1038/s41598-018-35587-x

Download PDF

Article
Open access
Published: 26 November 2018

DNA methylation dynamics during embryonic development and postnatal maturation of the mouse auditory sensory epithelium

Ofer Yizhar-Barnea¹^na1,
Cristina Valensisi²^na1,
Naresh Doni Jayavelu²,
Kamal Kishore ORCID: orcid.org/0000-0002-4650-8745³,
Colin Andrus²,
Tal Koffler-Brill¹,
Kathy Ushakov¹,
Kobi Perl¹,
Yael Noy¹,
Yoni Bhonker¹,
Mattia Pelizzola ORCID: orcid.org/0000-0001-6672-9636³,
R. David Hawkins² &
…
Karen B. Avraham ORCID: orcid.org/0000-0002-4913-251X¹

Scientific Reports volume 8, Article number: 17348 (2018) Cite this article

4286 Accesses
23 Citations
62 Altmetric
Metrics details

Subjects

Abstract

The inner ear is a complex structure responsible for hearing and balance, and organ pathology is associated with deafness and balance disorders. To evaluate the role of epigenomic dynamics, we performed whole genome bisulfite sequencing at key time points during the development and maturation of the mouse inner ear sensory epithelium (SE). Our single-nucleotide resolution maps revealed variations in both general characteristics and dynamics of DNA methylation over time. This allowed us to predict the location of non-coding regulatory regions and to identify several novel candidate regulatory factors, such as Bach2, that connect stage-specific regulatory elements to molecular features that drive the development and maturation of the SE. Constructing in silico regulatory networks around sites of differential methylation enabled us to link key inner ear regulators, such as Atoh1 and Stat3, to pathways responsible for cell lineage determination and maturation, such as the Notch pathway. We also discovered that a putative enhancer, defined as a low methylated region (LMR), can upregulate the GJB6 gene and a neighboring non-coding RNA. The study of inner ear SE methylomes revealed novel regulatory regions in the hearing organ, which may improve diagnostic capabilities, and has the potential to guide the development of therapeutics for hearing loss by providing multiple intervention points for manipulation of the auditory system.

Single-cell long-read sequencing-based mapping reveals specialized splicing patterns in developing and adult mouse and human brain

Article Open access 09 April 2024

Anoushka Joglekar, Wen Hu, … Hagen U. Tilgner

Exome-wide analysis implicates rare protein-altering variants in human handedness

Article Open access 02 April 2024

Dick Schijven, Sourena Soheili-Nezhad, … Clyde Francks

Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data

Article Open access 12 April 2024

Qiuyue Yuan & Zhana Duren

Introduction

The mammalian inner ear is a highly complex organ that is responsible for hearing and balance^1,2. Both functions are crucial for the survival and development of the organism throughout its lifetime. Defects in either the structure or function of any part of the inner ear sensory organs may result in auditory and vestibular impairments and are responsible for the most diverse variety of genetic disorders^3,4,5. The sensory organ responsible for hearing, the cochlea, contains the sensory epithelium (SE)^1,2. This region is composed of sensory cell types, i.e., the hair cells, as well as non-sensory cell types, i.e., the supporting cells. Transcriptomic approaches have provided information about the genetic determinants that drive each decision during the development and differentiation process of the inner ear sensory organs^6,7,8. RNA high-throughput sequencing studies of the SE have led to the identification and characterization of non-coding RNAs (ncRNA), including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs)^9,10. RNA transcriptomic analysis at the single-cell level has been undertaken only recently in this tissue^11,12,13, setting the stage for understanding the role of the individual cell types encompassing the SE tissue. However, to date, epigenetic analysis of the SE is still lacking, due to the technical challenges involved in the separation of cell types, and the low yield of material available for subsequent analysis. In recent years, technological advances in the epigenomics field have opened the door to unprecedented opportunities for unraveling of the epigenetic regulation of SE development and maturation. Transcriptomic and epigenomic analyses of the auditory system will lead to a better understanding of the genetic program of SE development and maturation and may hold the key to enable genetic manipulation and regenerative medicine in inner ear-related pathologies, including deafness.

DNA methylation remodeling is an essential component of epigenetic regulation during development¹⁴. This study was designed to elucidate DNA methylation dynamics during mouse SE development and maturation. To this end, we generated single nucleotide resolution genome-wide maps at key developmental time points and built a regulatory network underpinning tissue transitions throughout the developmental process that culminates in a functional, hearing inner ear. We discovered a close association between methylation dynamics through key developmental time points and transitions of the mammalian inner ear, with implications for the regulation of major signaling pathways such as Wnt and Notch, and mechanisms such as neurogenesis. In addition, the analysis provides the basis for an expanded view of the regulatory mechanism of GJB6, a critical gene in human deafness. This study is the first to report a single-base resolution methylome of the mammalian inner ear and provides information about regulatory pathways defining sensorineural hearing loss and deafness. The results obtained from exploiting this unique resource shed new light on the complexity of developmental and pathological mechanisms of both hearing and deafness in humans.

Results

Inner ear sensory epithelium methylomes

Studies in tissues monitoring changes in DNA methylation patterns have revealed critical information about development and gene regulation^15,16,17. The resolution of sequencing-based approaches permits the detection of tissue-specific non-CpG methylation, differential methylation, and of cis-regulatory elements, and has revealed correlations with changes in expression. To explore how DNA methylation dynamics may contribute to regulate inner ear development and maturation, we generated single-base resolution whole genome methylomes of the mouse inner ear SE for three key developmental stages: embryonic day 16.5 (E16.5), postnatal day 0 (P0), and postnatal day 22 (P22) (Fig. 1a, Supplementary Fig. S1a). At P0, prosensory and non-prosensory cell specification has already occurred and the SE is in the process of developing into a single row of sensory inner hair cells and three rows of sensory outer hair cells, surrounded by non-sensory supporting cells. Although at P0 the cells are already post-mitotic and the tissue can be considered non-proliferative, the SE is not yet functional and the mice cannot hear. By P22, the SE is fully mature and mice have acquired hearing¹⁸.

DNA methylation predominantly occurs at cytosine and its function depends on the sequence context. While methylated cytosines are mostly located in the context of CpG dinucleotide (mCG), the methylation in non-CpG contexts (mCHH and mCHG, where H = A, T or C) has been shown to occur in some cases, such as adult mammalian brain^15,19 and embryonic stem cells²⁰. To assess DNA methylation in both the CG and non-CpG (CHG and CHH) contexts, we generated 1.3 billion unique mapped reads, combining two biological replicates per each time point and with an average genomic coverage ranging from 11X to 19.5X (Supplementary Fig. S1b). As expected, most of the methylated cytosines are in the mCG context (Fig. 1b). Interestingly, at P22, we observed a prevalence of mCH methylation in both CHG and CHH, the former representing 2.9% of all methylated cytosines, and the latter 14.6% (Fig. 1b). While CH methylation is present in both mouse and human embryonic stem (ES) cells, it is largely lost upon cell differentiation^21,22. Recent evidence has shown that mCH methylation accumulates specifically in neurons during development, although the function is still unclear¹⁵. Thus, the increase in mCH we observed upon maturation of the SE might be associated with onset of function at both ends of the auditory system, the auditory cortex in the brain and the mechanosensory SE in the inner ear.

Regulatory landscape of the inner ear SE

Previous studies revealed that cis-regulatory elements such as promoters and enhancers can be identified in whole genome bisulfite sequencing (WGBS) data through the detection of unmethylated and low-methylated regions (UMR/LMRs), respectively^23,24. To identify putative regulatory elements, such as promoters and enhancers, in the SE, we defined UMRs as regions with an average methylation lower than 10%, and LMRs as regions with an average methylation between 10% and 50% (Supplementary Fig. S1c,d). With these conditions, we identified 31,916, 17,945 and 31,564 UMRs and 174,347, 237,282 and 206,144 LMRs in E16.5, P0, and P22 methylomes, respectively (Fig. 1c; Supplementary Table S1). UMRs were validated by their localization to annotated transcription start sites (TSS) and CpG islands (CGIs) (Supplementary Fig. S1e,g). In contrast, LMRs were predominantly located far from TSS in intergenic or intronic regions and were generally devoid of CGIs (Supplementary Fig. S1f,h).

To ensure that LMRs were representative of known distal regulatory elements, we examined whether they overlapped known DNase I Hypersensitive Sites (DHS) in the mouse genome, which are indicative of transcription factor (TF) binding. To do so, we leveraged the power of a large consortium project, Encyclopedia of DNA Elements (ENCODE)²⁵, where epigenomic data has been collected across numerous tissues, cell types, and cell lines. Using data from mouse ENCODE, we found that 93.6%, 85.5%, and 93.3% of LMRs from E16.5, P0, and P22, respectively, were DHS in other mouse tissues (Fig. 2a). Out of the LMRs that included a known DHS, 9.8–15.5% contained a CTCF motif and overlapped known CTCF binding sites^26,27, suggesting a role as insulators or in three-dimensional (3D) genome architecture (Fig. 2a). The remaining 84.5–90.2% overlapped known H3K4me1 sites from the mouse ENCODE project (Fig. 2a)^26,27, suggesting they may function as enhancer elements²⁸.

Further evidence that LMRs can act as putative enhancers, comes from the observation that 80% (12 out of a total of 15) of functionally validated mouse otic enhancers from the VISTA Enhancer Browser, a resource for experimentally validated human and mouse non-coding fragments with gene enhancer activity assessed in transgenic mice, are recovered by our LMRs (Fig. 2b; Supplementary Table S2). Collectively, these findings show that identification of LMRs is a valuable approach to annotating putative regulatory elements in the mouse genome related to SE gene regulation. In order to understand the regulatory implications of these elements, we determined their target genes. Distal UMRs, such as those at non-promoter CGIs, and LMRs were putatively assigned to target genes. We used the 4DGenome database for known interactions and the PreSTIGE (Predicting Specific Tissue Interactions of Genes and Enhancers) database for predicted interactions, a computational approach for defining enhancer–gene interactions, which integrates available H3K4me1 ChIP-seq and RNA-seq datasets and couples cell type-specific H3K4me1 signals with genes expressed in each cell type²⁹. Importantly, we were able to assign a number of distal UMRs and LMRs to functionally relevant genes with known roles in mouse inner ear development, and hearing impairment or deafness (Fig. 2c,d; Supplementary Table S3)³⁰. For each developmental time point, an average of 77 and 296 genes could be linked to distal UMRs and LMRs, respectively.

In addition, the presence of significantly enriched transcription factor binding site (TFBS) motifs in UMRs and LMRs was used in order to predict putative regulators of target genes (Fig. 2e,f; Supplementary Table S4). TFBS were screened for TFs expressed at the various developmental time points according to available transcriptomes^8,9,10. Our approach resulted in the identification of TFBS of several known inner ear SE master regulators (e.g. Atoh1, Sox2, NeuroD1, and Rfx3)^6,8. We also identified E2f1, which is linked to maternally inherited deafness originating from aberrant methylation and an increase in this pro-apoptotic TF³¹. Interestingly, we found that a Bach2 motif was significantly enriched at LMRs across all time points. Bach2 is known for its role in the Bcl6-Bcl2-p53 axis-controlling apoptosis (reviewed in³²), together with otic expression in the chick³³, although it has not been previously reported to play a role in inner ear development.

DNA methylation dynamics during development and maturation transitions

To explore how DNA methylation dynamics may contribute to regulate inner ear SE development and onset of hearing, we focused on two comparisons: E16.5 to P0, which we refer to as the “developmental transition” (DevTrans), and P0 to P22, which we refer to as the “maturation transition” (MatTrans) (Fig. 1a). We then determined differentially methylated regions (DMRs), where there was at least a 30% loss (hypo-DMR) or gain (hyper-DMR) in methylation across the transition (P-value < 0.05; Fig. 3a; Supplementary Fig. S2a,b, and Table S5). Our results indicated higher dynamics in MatTrans (~10,000 DMRs) than in DevTrans (~2,300 DMRs), although the ratio of hypo- and hyper-DMRs was similar for each transition. Because DMRs are primarily located in intronic and intergenic regions, they overlap LMRs more frequently than UMRs (Supplementary Fig. S2c,d). Collectively, these findings suggest that the majority of DNA methylation changes during both development and maturation of the SE occur in distal regulatory regions, for example in enhancers.

TFBS motif analysis of DMRs, performed in order to examine the regulatory network (Fig. 3b; Supplementary Table S5), revealed the enrichment of a number of known inner ear regulators, including Six1³⁴, Stat3³⁵, and Wnt factors Tcf3 and Tcf4^36,37,38. Another factor found in both LMRs and DMRs was Sox2, a key factor in the prosensory differentiation of hair cells³⁹. Interestingly, the motif for Atoh1 is hypermethylated by P22 (MatTrans hyper-DMRs), in accordance with the down-regulation of Atoh1 after P5, and its early role in major cell type differentiation processes. The Bach2 motif was enriched only in MatTrans hypomethylated DMRs (P = 1 * 10⁻⁵), and added to its enrichment pattern in LMR-predicted TFBS (Fig. 2e).

In order to predict putative target genes for DMRs, we used known interactions from the 4DGenome database and predicted interactions from the PreSTIGE database²⁹. We then leveraged the DMR motif analysis of TFs and putative target genes to construct DevTrans and MatTrans in silico regulatory networks and investigate any associated biological processes (Fig. 3c–e; Supplementary Tables S6 and S7). To reduce the complexity of regulatory networks, we chose to focus on a few specific transcription factors that are well known for their role in the inner ear and to use these as the basis for the creation of centralized networks. For DevTrans, we centered the network around Stat3 (Fig. 3c), a factor known to play a role in hair cell lineage determination and possibly regenerative capacity^35,40. One GO term that stood out was, ‘signaling pathways regulating pluripotency of stem cells’ (KEGG:04550 DevTrans adj P = 0.00149, MatTrans adj P = 0.0039), which illustrates that pluripotency factors such as Stat3, Sox2, Tcf3, Smad4, and Wnts are also key for regulation in the SE. Other connected processes of interest were ‘HIF-1 signaling^41,42, previously implicated in noise protection and ‘Lipid homeostasis’, which while largely unexplored in the auditory system, has been associated with neurodegenerative processes. Furthermore, there was an exclusive Atoh1-Stat3 interaction around the ‘Notch signaling pathway’ in the Stat3 centralized network, which may indicate a possible role of DNA methylation in the Notch signaling response in hair cells.

The network centered around Sox2 (Fig. 3d), a key TF that drives cochlear neurogenesis and other types of supporting cells^43,44, revealed possible interactions involved in regulating the sensory perception of sound (GO:0007605) during the SE maturation phase. Our analysis also suggests an interaction of Sox2 with various cell-to-cell adhesion-related genes (i.e. Cdh1) and stereocilia formation-related genes (i.e. Pcdh15 and Myo3a)^45,46 under the umbrella of sensory perception of mechanical stimulus (GO: 0050954). Interestingly, ‘response to lipid’ (GO:0033993) was enriched for Sox2 target genes and revealed the involvement of CEBPß and its target genes, an observation in accordance with the TFBS analysis of LMRs.

Time point-specific DNA methylation changes

Changes in DNA methylation are subject to thresholds that can be arbitrary. Our requirement for a DMR to have 30% change in overall methylation may have underestimated the dynamics of LMR methylation, and even to a certain extent that of UMRs (Supplementary Fig. S3a,b). Specific TFBS might be altered by much smaller changes that are not reflected in the average methylation change of DMRs, we therefore also determined 5692 UMRs and 314,192 LMRs that were identified at a single time point as additional sites of interest. These are referred to as time point-specific UMRs and LMRs (Fig. 4a).

We associated time point-specific UMRs to nearby genes and performed GO term enrichment analysis (Fig. 4a,b; Supplementary Fig. S3a and Table S8). The results revealed a number of enriched time point-specific terms, such as ‘sensory perception of sound’ (E16.5 and P0) for UMRs at Ush1c, Coch, and Lrig2. Other examples of time point-specific terms are ‘inner ear morphogenesis’ (E16.5), and ‘inner ear’ (P0 and P22). Neuronal development-related terms were enriched, as well as the formation of neurons (‘neurogenesis’), and the connections between neurons (‘axon guidance’). An examination of pathways known to be relevant to the SE, such as Wnt, suggested an association between the promoter methylation status and pathway regulation (e.g. Wnt9a). Other pathways found to be enriched included neurotrophins and MAPK signaling pathways.

When examining time point-specific LMR putative target genes we noted enrichment of SE-related terms such as ‘inner ear’ expressed genes (e.g. Atoh1, Lrig2, etc.). Interestingly, analysis of the mouse SE methylome resulted in enrichment of human phenotype terms such as ‘functional abnormality of the inner ear’, ‘abnormal inner ear morphology,’ and ‘abnormal innervation’, involving genes such as Gjb2, Gjb6 and Myo6 (Supplementary Table S9). Another feature we chose to highlight was the enrichment of immune-related terms for both time point-specific UMR- and LMR-associated genes. One example is TGF-ß signaling, which exhibits LMRs associated with BMP and SMAD gene families (Fig. 4b; Supplementary Tables S8 and S9).

Gene expression and DNA methylation

In order to investigate how DNA methylation changes may affect gene expression, we correlated transcriptomic data previously generated for E16.5 and P0^8,9,10 with DMRs for DevTrans, focusing on DMRs located at promoters or distal to TSS (>5 kb) with an expected anti-correlation between gene expression and DNA correlated with expression of their putative target genes (Fig. 5a) including a key inner ear transcription factor, Pou3f4, which is required for hair cell development⁴⁷. Upstream enhancers of Pou3f4 have been previously implicated in causing X-linked deafness type 3 (DFN3)⁴⁸. Our results showed that Pou3f4 expression was up-regulated during DevTrans (0.455-fold increase, adjusted P-value = 0.00013), while its associated distal DMR was hypomethylated (%methylation difference = −67.15%, P < 0.0001). Other genes identified included Hif-1α, already identified in the DMR TFBS motif analysis (Fig. 3c,d), and members of the ‘Hif-1α signaling pathway’ highlighted in our DMR-based network analysis (Fig. 3c,d). GO analysis on the 255 anti-correlated hyper- and hypo-DMR genes (Fig. 5b; Supplementary Table S10) highlighted organ-relevant terms such as ‘inner ear development’ (hyper-DMR, P-value = 0.03) and ‘focal adhesion’ (combined, P-value = 0.045). Interestingly, genes with a negative correlation between DMR and gene expression dynamics, specifically hyper-DMRs (found either in promoter or distal to TSSs) were found to be enriched for the ‘immune system process’ (P-value = 0.03, respectively), while the hypo-DMRs were enriched for ‘response to mechanical stimulus’ (P-value = 0.001). In accordance with our observations of Hif-1α (described above), we noted ‘response to hypoxia’ (P-value = 0.012) GO term enriched among hypo-DMRs anti correlated to gene expression. Our results suggest that although derived from a heterogeneous cell population, focusing on genes whose expression is inversely correlated with DNA methylation during development, can lead to the identification of tissue relevant genes and pathways.

Mouse inner ear SE LMRs are informative of regulation of human deafness

The annotation of cis-regulatory elements in the human genome has increased our understanding of disease-associated variants by revealing their location in regulatory elements such as enhancers^49,50,51,52. Here, we investigated how mouse inner ear SE LMRs could expand our understanding of human deafness, by using them to annotate putative regulatory elements in the human genome. We converted our mouse genome (mm10, Mus musculus genome assembly GRCm38, Genome Reference Consortium) LMR coordinates to human genome (hg19, Homo sapiens genome assembly GRCh37, Genome Reference Consortium) coordinates using the UCSC LiftOver tool. This approach allowed us to recover 56,439 LMRs at E16.5, 83,299 at P0, and 67,545 at P22. An average of 84% converted mouse-to-human LMRs were both hypersensitive (DHS) and marked by H3K4me1 in at least one human cell or tissue type²⁵, supporting the assumption that these elements act as enhancers in the human genome. As validation that our mouse-to-human converted LMRs function as enhancers, we found an overlap of functionally validated enhancers from the VISTA Enhancer Browser repository, recovering 9 out of 19 human otic enhancers (Fig. 6a).

Next, we examined whether hearing-related variants from genome wide association studies (GWAS) and their proxy SNPs in linkage disequilibrium (LD) overlapped our mouse-to-human LMRs. Although most hearing-related studies have a limited number of associated variants, we succeeded in identifying 48 variants associated with hearing impairment, age-related hearing impairment, ear morphology and ear protrusion⁵³ in LMRs from all time points (Supplementary Table S11). All but four of the LMRs are marked by H3K4me1 in at least one human cell or tissue type, suggesting that they are enhancer elements. By employing the same type of known and predicted 3D interactions described above, we were able to assign target genes to 19 of the variants (Supplementary Table S11). Many of these interacting variants were clustered on chromosome 6, primarily in 6p21 and 6p22 (Fig. 6b). Chromosome 6 is a hotspot of non-syndromic deafness genes, harboring seven autosomal recessive genes or loci (four on 6p21), and six autosomal dominant genes or loci (three on 6p21). Two autosomal dominant loci, DFNA21 (6p21) and DFNA31 (6p21.3) remain undetermined^54,55.

We also examined the regions associated with deafness loci with unknown genes described in the Hereditary Hearing Loss Homepage (http://hereditaryhearingloss.org/) and performed a related reciprocal analysis. Chromosomal regions derived from microsatellite markers or coordinates of cytogenetic bands were located in the relevant manuscripts, identified in hg19, and converted to the homologous mouse interval (mm10). We identified SE LMRs in all of these regions, suggesting they may be associated with deafness in humans. These mouse-to-human maps provide a valuable resource for annotating putative regulatory elements relevant to the genetics of human deafness.

A number of affected individuals with hereditary hearing impairment do not harbor any known causative coding variants. For example, although almost half the cases of Pendred syndrome, a disorder that includes hearing impairment due to inner ear malformations in the cochlea and enlarged vestibular aqueduct (EVA), are caused by mutations in SLC26A4⁵⁶, the cause of the remaining cases is largely unknown. It is therefore feasible that some manifestations of hearing impairment are caused by distal regulatory variants and that mapping mouse LMRs from the inner ear SE to human coordinates could direct the search for human cis-regulatory elements of known deafness genes. We have illustrated such an example for SLC26A4, where several mouse-to-human LMRs are near the gene, and at least four have known interactions (Fig. 6c).

The human GJB2-GJB6 locus harbors a putative enhancer modulating GJB6 expression

In order to further validate that mouse-to-human LMRs can operate as gene enhancers, we utilized the CRISPR-on system⁵⁷ and focused on the GJB2 proximal regulatory region 1.34 kbp upstream of the GJB2 TSS (Fig. 7a). This region is located between GJB2 and GJB6, two prominent deafness genes, where GJB2 is associated with approximately 30–50% of cases of deafness⁵⁸. According to ENCODE data, this LMR is marked as an ‘active enhancer’ by ChromHMM segmentation in a GJB2 and GJB6 expressing cell line, NHEK (Normal Human Epidermal Keratinocytes) (Fig. 7a), confirming our prediction of this region as a putative enhancer in the inner ear SE. Two guide RNAs (gRNA) designed to target the GJB2 proximal enhancer (Fig. 7a) were transfected into HEK293 T (human embryonic kidney) cells that express neither GJB2 nor GJB6. Neither of the guides, individually or in combination, could modulate GJB2 expression (Fig. 7b), when compared to the control (‘no sgRNA’) sample, despite the proximity to the GJB2 promoter region (1.34 kbp between the LMR and GJB2 TSS). Surprisingly, use of gRNA#1 alone resulted in a 3.36-fold increase in GJB6 expression levels (P = 0.0043, n = 4), although gRNA#2 had no significant (P > 0.05) effect. While a combination of both gRNAs significantly increased GJB6 expression levels, the effect was lower than gRNA#1 alone (1.37-fold, p = 0.035, n = 4). A gRNA targeting TetO was used as a negative control. Examination of the putative enhancer and the lifted-over LMR at its core, using the JASPAR core TF motif search tool⁵⁹, detected two ATOH1 binding motifs, 38 bp (GACAGATTTG, JASPARScore = 4.257) and 80 bp (GAGCAGGC, JASPARscore = 3.311) upstream of the LMR and still within the putative enhancer (Fig. 7a,d). Based on this finding, we used the same CRISPR-on system to modulate ATOH1 expression, which also served as an internal positive control, indicating that the CRISPR-on system transfection had worked properly. Enhancing ATOH1 expression via CRISPR-on using three closely spaced sgRNAs targeting the ATOH1 TSS also enhanced GJB6 expression, but had no effect on GJB2 expression (Fig. 7b). Targeting the putative enhancer with gRNA#1 also increased ATOH1 expression by 1.27-fold (P = 0.047, n = 4). We further expanded our search to our RNA-seq data, where we identified a lncRNA, XLOC_012867, downstream to this LMR¹⁰. In the human hg38 genome, we detected a ncRNA transcript, indicated as RP11–264J4.10 (GenBank AF091526.1), which was previously annotated as a GJB2 ‘upstream regulatory region’ based on its proximity to the GJB2 gene⁶⁰. R11-264J4.10 fits the criteria of a lncRNA at > 200 bp long (1,594 bp) and no open reading frame. Using the CRISPR-on system against the enhancer/LMR resulted in an expression modulation pattern similar to the one observed for GJB6 (Fig. 7c), where gRNA#1 increased the lncRNA expression by 5.87-fold (P = 0.024, n = 5) and activation of ATOH1 expression increased the lncRNA by 10.3-fold (P = 0.042, n = 5) (Fig. 7c). This might indicate that the enhancer interacts with multiple genes and is regulated through ATOH1 binding. Our data suggests that by lifting over mouse LMR putative enhancers to the human genome, we have annotated a relevant human enhancer region proximal to the GJB2 gene and regulating the distal GJB6 gene and a previously annotated ncRNA. Our observations also suggest an interaction between ATOH1 expression and GJB6 regulation. While expression of these two genes is mutually exclusive in the differentiated SE, a possible intermediate stage could call for this regulatory interaction as early as the creation of the sensory primordium⁶¹.

Discussion

The link between DNA methylation, its dynamics, and the development and differentiation of tissue, cellular lineages, and cellular functionality has been well established. Although DNA methylation has been studied extensively in both mouse and human in many tissues, the inner ear SE has been lacking any high-resolution characterization of this crucial epigenetic mark. Our work provides the first DNA methylome map in the mammalian inner ear SE, covering two major transitions, the developmental embryonic stage between E16.5 and newborn (P0) mice, and across the maturation transition period soon after the onset of hearing. The DNA methylation data generated should serve as a relevant resource for the auditory research field and provide information about inner ear development, deafness, and hearing impairment genetics, from the realm of coding and non-coding genes, into epigenetics and regulatory regions of the genome.

In this context, there have been previous reports that Notch factors are robustly activated in cells surrounding Atoh1-expressing cells and play a crucial role in supporting cell lineage determination and maturation⁶². Another example is the association of Tcf3 with lipid metabolism and regulation of stem cell states detected in our networks focused on supporting cell or hair cell TFs respectively. Tcf3 is known to play a role in inner ear SE development^63,64, and to repress Wnt-ß-catenin signaling in neural precursor cells³⁷ in order to maintain the undifferentiated state. Taken together, we can use our results to postulate an interplay between lipid metabolism and pivotal signaling pathways in SE development, and the potential to harness external lipid delivery to modulate signaling pathways. HIF-1α was another factor identified in our study as regulated by changes in the DNA methylation of regulatory elements. While very little is known about the role of HIF-1α in the mouse inner ear, there is evidence that exposure to noise induces HIF-1α expression, and that up-regulation of HIF-1 signaling protects the mouse from noise induced hearing loss^41,42. The Stat3-centric DMR network, therefore, suggests that HIF-1 signaling plays a role in tissue maturation. Collectively, these networks, which are anchored on DNA methylation dynamics, reveal epigenetic and regulatory insight on the development and maturation of the inner ear SE.

Our survey of regulatory regions in the mouse SE, based on the methylome analysis, will be an important source of information to prioritize non-coding variants identified in human families as whole genome sequencing (WGS) is increasingly becoming the method of choice for identifying pathogenic variants. As evidence that LMRs in the SE function as cis-regulatory elements, we have demonstrated that they harbor chromatin marks indicative of enhancers, such as DHS and H3K4me1 in other cell types. Moreover, the LMRs were shown to function as otic enhancers in transgenic assays presented in the VISTA Enhancer Browser, and we provide CRISPR-activation validation for one such candidate LMR, which was found in the GJB2-GJB6 locus. Using the CRISPR-on system targeting this putative regulatory element, we showed expression modulation of both GJB6 and a ncRNA, with as yet undefined function, but not the proximal GJB2 gene itself. Both GJB2 and GJB6 encode key structural proteins in the inner ear SE; therefore, their expression is likely to be tightly governed by key inner ear differentiation and maturation TFs. Taken together with known lncRNA acting as co-factors in modulating transcription, we can hypothesize two plausible models of operation connecting all factors at hand – GJB2, GJB6, ATOH1, lncRNA and the LMR/enhancer (Fig. 7e). In the first model, a classic enhancer model is at play, where the LMR/enhancer is bound by ATOH1 and chromatin looping facilitates interaction with the promoters for the lncRNA and GJB6. This looping excludes regulation of the nearby GJB2 gene. A second plausible model is based on chromatin looping, directly linking the putative enhancer with the GJB6 promoter, and involves a role for the lncRNA. The enhancer-promoter interaction could be stabilized by the lncRNA, creating a small “niche”, excluding the GJB2 promoter and gene from regulation over GJB6. Given that we have numerous other potential candidates to evaluate using this CRISPR-on validation approach, this methodology can also be employed in a high-throughput screen to validate converted mouse-to-human regulatory regions.

In conclusion, this study describes the first DNA methylome map of the mammalian inner ear SE and indicates that a genome-wide perspective of DNA methylation may provide valuable information about the processes of inner ear lineage formation, tissue morphogenesis, and gain of auditory function. Although these results represent the development of the SE tissue as a whole, future studies could concentrate on the DNA methylation and epigenetic regulation of specific pathways or cellular populations. Finally, increasing our knowledge of the role of a global epigenetic regulatory process, as well as the dynamics of regulatory elements, may advance regenerative research of the inner ear sensory organ by directing the manipulation of gene expression, or repurposing currently available epigenetic therapeutics in order to treat or prevent the onset of hearing impairment.

Materials and Methods

Animals and SE preparation

All procedures involving mice met the guidelines described in the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Animal Care and Use Committees of Tel Aviv University (M-13-114, M-13-115 and 01-16-100). C57BL/6J mice were purchased from Envigo, Jerusalem, Israel. The ages of the mice used were E16.5, P0 and P22. Two replicate groups of 6–8 mice each was used for each age group. Pregnant dams or postnatal mice were sacrificed by CO₂ suffocation, followed by immediate decapitation; only decapitation was employed when dissecting newborns. The skin at the top of the head was removed and the dorsal part of the skull was opened along the midline. The brain tissue was removed and the auditory nerve was pulled out and cut. The inner ear, containing both the cochlea and the vestibule, was removed from the temporal bone, and placed in phosphate buffered saline (PBS). The cochlea was further dissected; the otic capsule, the spiral ligament and the stria vascularis were removed to expose the SE. After this, the SE was separated, base to apex, from the spongy modulus bone. Both SE from each group were pooled together to generate two distinct biological replicates for each age group.

Whole genome bisulfite sequencing

We performed WGBS on SE using the MethylC-Seq protocol⁶⁵, with minor modifications. Genomic DNA was purified from replicate samples of pooled SE from both ears of 6–8 mice, using the QIAamp DNA Micro gDNA Kit (Qiagen). Each sample was spiked with 0.5% of total gDNA mass with Lambda unmethylated DNA to serve as an internal control for bisulfite (BS) conversion rates. gDNA samples were fragmented using the COVARIS S220 with Snap-Cap microTUBE with AFA fiber 6 × 16mm (Covaris). Illumina-compatible NGS libraries were produced using the NEBNext® Ultra DNA Library Prep Kit for Illumina® (NEB) and Multiplex Oligos for Illumina® (Methylated Adaptor, Index Primers Set 1) (NEB). Adaptors were ligated and the library DNA was cleaned and size selected using Agencourt AMPure XP (Beckman Coulter) magnetic beads. BS conversion was performed using Methylcode Bisulfite Conversion Kit (Applied Biosystems). The library DNA was PCR amplified through 4–6 cycles using KAPA HiFi Uracil + polymerase with indexed and universal Illumina compatible oligos. The library PCR was cleaned and size selected on AMPure XP beads. Library concentration was validated using the Qubit dsDNA High Sensitivity platform, and the size distribution was assessed with the DNA High Sensitivity Kit (Agilent). NGS was performed at BGI, China on an Illumina HiSeq 2500. Libraries were divided equally across lanes to minimize technical bias during sequencing.

Methylation data analysis

WGBS reads were aligned to the mouse reference genome (mm10 assembly) using Bismark (version v0.14.0)⁶⁶ with default options and duplicates were removed using SAMtools (0.1.18)⁶⁷. The library fold coverage was computed by dividing the number of uniquely aligned reads by the size of the genome. The mapping efficiency was computed by Bismark using the number of unique paired end alignments against the total paired end reads. The bisulfite conversion rates were computed by Bismark after mapping each sample with the lambda phage genome. Differential methylated regions (DMRs) were called using methylPipe (version 1.4.5)⁶⁸. A > 30% methylation difference and a non-parametric Wilcoxon test P-value < 0.05, corrected for multiple testing, were applied as cutoff. Unmethylated and low methylated regions (UMRs and LMRs) were identified using the R package MethylSeekR²³, using the following criteria: (i) FDR < 5% for regions, (ii) average DNA methylation < 10% (UMRs) or < 50% (LMRs), (iii) mCG/CG and > 5 CGs per region. Regions with any significant overlap between UMRs and LMRs were removed from the datasets to prevent ambiguity.

ENCODE data

H3K4me1, DHS, and CTCF binding site data peak calls from various cell types were downloaded from the ENCODE consortium²⁵ and overlap analysis was performed using the BEDTools suite⁶⁹.

Prediction of target genes for regulatory regions

To predict the potential regulatory interactions of LMRs, UMRs and DMRs with their target genes, known interactions were obtained from the 4DGenome database (https://4dgenome.research.chop.edu/) and predicted interactions from the PreSTIGE database⁷⁰. LMRs, UMRs and DMRs were cross-referenced with the interaction data to predict the target genes. The known human deafness genes were obtained from³⁰.

TFs analysis

TF motif enrichment analysis was performed using the HOMER Motif Analysis software (http://homer.ucsd.edu/homer/motif/)⁷¹. TF motifs with P-value < 1e-10 and TFs with expression, RPKM > 1 were selected from available inner SE RNA-seq datasets^8,9,10. TF DNA binding preferences were predicted using JASPAR, http://jaspar.genereg.net/ ⁵⁹.

Gene regulatory networks construction

Two gene regulatory networks were constructed for each transition. TFBS motif enrichment analyses of hyper- and hypo-DMRs from the two transitions (DevTrans and MatTrans) were used to identify enriched TF motifs (P-value < 0.01). We then compared the identified DMRs with enhancer-gene interaction data from the 4DGenome and PreSTIGE databases and assigned the TFs, according to the enriched motif present at DMRs, to their interacting genes. The results were screened for gene expression (differential expression, DEGs) and for anti-correlation, i.e. hyper-DMRs should interact with down-regulated genes and hypo-DMRs with up-regulated genes and for TFs with RPKM > 1. Network visualizations were created using the Cytoscape software tool⁷².

Gene ontology analysis

TSS proximal regulatory features were analyzed for GO terms and KEGG pathway using DAVID (https://david.ncifcrf.gov/)⁷³ with default parameters. Terms were ranked according to the P-value (EASE score), which is derived from a modified Fisher’s exact test. Associated genes were determined according to previously described genomic annotation of the TSS proximal genomic ranges datasets, derived from lists of official gene symbols or ENTERZ gene id numbers (for lists > 3000 genes long).

For TSS distal elements, analysis of GO term enrichment was executed using the HOMER Motif Analysis software (annotatePeaks.pl hg19 -go)⁷¹ for DMRs or GREAT (http://bejerano.stanford.edu/great)⁷⁴ for putative regulatory elements characterized as time point-specific LMRs. Terms were ranked according to the Binomial test Q score (Binom FDR Q-Val < 0.05) and the Hypergeometric test Q score (Hyper FDR Q-Val < 0.05).

Identification of proxy SNPs and LMR liftover

SNPs for available hearing-related impairment were downloaded from the NHGRI-EGI GWAS Catalog (https://www.ebi.ac.uk/gwas/). LD SNPs were determined using rAggr, max distance 500 kb (http://raggr.usc.edu). An R-squared threshold of 0.5 was used based on previous studies showing enrichment at distal cis-regulatory elements for SNPs from r2 1.0 to 0.5^75,76. Mouse mm10 coordinate LMRs were converted to human hg19 coordinates using the UCSC LiftOver tool (https://genome.ucsc.edu/cgi-bin/hgLiftOver).

CRISPR-on system

We used the pAC154-dual-dCas9VP160-sgExpression⁵⁷, a gift from Rudolf Jaenisch (Addgene plasmid # 48240), and designed gRNAs using the CHOPCHOP web tool (http://chopchop.cbu.uib.no/)⁷⁷ to select the top-ranking guides. Oligos suitable for cloning into the BbsI site were ordered from IDT. The guide sequences were: gRNA#1 FWD 5′-caccGacttcgcgatttttgcagag-3′, gRNA#1 REV 5′-aaacctctgcaaaaatcgcgaagtC-3′, gRNA#2 FWD 5′-caccGgtaaaaacgtgagcgcgag-3′, gRNA#2 REV 5′-aaacctcgcgctcacgtttttacC-3′. Correct integration of guide RNAs into the plasmid was validated using Sanger sequencing with a sequencing primer from the U6 promoter upstream of the integration site, U6_SEQ. 5′CAAGGCTGTTAGAGAGATAA-3′. All cloning design was performed using the Benchling platform (www.benchling.com). Plasmids were harvested using NucleoSpin MidiPrep (MN#74010). Plasmids were transfected into 50% confluence cells seeded 18 hours earlier in 6-well plates. Transfection was performed using JetPEI reagent according to the manufacturer’s protocol (Polyplus#101). Cells were harvested for RNA 48 hours post-transfection using the ZYMO Direct-zol RNA Miniprep Kit (Zymo research# R2070). RNA was measured using the NanoDrop and 500 ng of RNA was taken from each sample to prepare cDNA using the qScript Reverse Transcription Kit (Quantabio 95047). All experiments were performed with two technical repeats of each sample and across 5 biological replicates.

Real Time qPCR

The expression of mRNA was evaluated using the PerfeCTa SYBR® Green FastMix (QuantaBio) in the StepOneTM Real-Time PCR System (Applied Biosystems). Primers were designed using Primer3plus (http://primer3plus.com/cgi-bin/dev/primer3plus.cgi) with the default parameters to reach an amplicon of 80–150 bp. Oligos were ordered from IDT as a 100 µM stock. Primer sequences were: GJB2_FWD 5′AAAAGCCAGTTTAACGCATTGC'3, GJB2_REV 5′TTGTGTTGGGAAATGCTAGCG'3, GJB6_FWD 5′TGGCAAATTTGT GAACTGTCATG'3, GJB6_REV 5′TCAGTTGTTTGCAATGATTGGC'3, RP11–264J4.10_FWD 5′TGCTCATGAAGAGGCAAAGC'3, RP11-264J4.10_REV 5′TTAACAAGCCGACTCAGCAC'3. All samples were normalized to GAPDH endogenous expression with the following primers GAPDH 5′ GGAGCGAGATCCCTCCAAAAT'3 and GAPDH_REV 5′GGCTGTTGTCATACTTCTCATGG'3. The negative control was defined as the Non-Template Control (NTC). The relative expression level was measured using the 2-ddCt method. mRNA levels from un-transfected cells (termed “noDNA”) were defined as 1. The data in Fig. 7 are presented as the mean ± SE.

Data Availability

The datasets are available in the NCBI Short Read Archive (SRA); the accession number for the data generated from mouse inner ear SE is SRP111167. Access to SRA metadata can be found at: www.ncbi.nlm.nih.gov/sra, as well as within the article and additional files.

References

Groves, A. K. & Fekete, D. M. Shaping sound in space: the regulation of inner ear patterning. Development 139, 245–257 (2012).
Article CAS PubMed PubMed Central Google Scholar
Kelley, M. W. Regulation of cell fate in the sensory epithelia of the inner ear. Nat. Rev. Neurosci. 7, 837 (2006).
Article CAS PubMed Google Scholar
Koffler, T., Ushakov, K. & Avraham, K. B. Genetics of hearing loss: syndromic. Otolaryngol. Clin. North Am. 48, 1041–1061 (2015).
Article PubMed PubMed Central Google Scholar
Dror, A. A. & Avraham, K. B. Hearing impairment: a panoply of genes and functions. Neuron 68, 293–308 (2010).
Article CAS PubMed Google Scholar
Richardson, G. P., de Monvel, J. B. & Petit, C. How the genetics of deafness illuminates auditory physiology. Ann. Rev. Physiol. 73, 311–334 (2011).
Article CAS Google Scholar
Elkon, R. et al. RFX transcription factors are essential for hearing in mice. Nat. Commun. 6, 8549 (2015).
Article CAS PubMed Google Scholar
Hertzano, R. et al. Cell type-specific transcriptome analysis reveals a major role for Zeb1 and miR-200b in mouse inner ear morphogenesis. PLoS Genet. 7, e1002309 (2011).
Article CAS PubMed PubMed Central Google Scholar
Scheffer, D. I., Shen, J., Corey, D. P. & Chen, Z. Y. Gene expression by mouse inner ear hair cells during development. J. Neurosci. 35, 6366–6380 (2015).
Article CAS PubMed PubMed Central Google Scholar
Rudnicki, A. et al. Next-generation sequencing of small RNAs from inner ear sensory epithelium identifies microRNAs and defines regulatory pathways. BMC Genomics 15, 484 (2014).
Article PubMed PubMed Central Google Scholar
Ushakov, K. et al. Genome-wide identification and expression profiling of long non-coding RNAs in auditory and vestibular systems. Sci Rep. 7, 8637 (2017).
Article ADS PubMed PubMed Central Google Scholar
Burns, J. C., Kelly, M. C., Hoa, M., Morell, R. J. & Kelley, M. W. Single-cell RNA-Seq resolves cellular complexity in sensory organs from the neonatal inner ear. Nat. Commun. 6, 8557 (2015).
Article ADS CAS PubMed Google Scholar
Durruthy-Durruthy, R. & Heller, S. Applications for single cell trajectory analysis in inner ear development and regeneration. Cell Tissue Res. 361, 49–57 (2015).
Article CAS PubMed Google Scholar
Ealy, M., Ellwanger, D. C., Kosaric, N., Stapper, A. P. & Heller, S. Single-cell analysis delineates a trajectory toward the human early otic lineage. Proc. Natl. Acad. Sci. USA 113, 8508–8513 (2016).
Article CAS PubMed PubMed Central Google Scholar
Smith, Z. D. & Meissner, A. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 14, 204 (2013).
Article CAS PubMed Google Scholar
Lister, R. et al. Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905 (2013).
Article PubMed PubMed Central Google Scholar
Schultz, M. D. et al. Human body epigenome maps reveal noncanonical DNA methylation variation. Nature 523, 212–216 (2015).
Article ADS CAS PubMed PubMed Central Google Scholar
Li, X., Liu, Y., Salz, T., Hansen, K. D. & Feinberg, A. Whole-genome analysis of the methylome and hydroxymethylome in normal and malignant lung and liver. Genome Res. 26, 1730–1741 (2016).
Article CAS PubMed PubMed Central Google Scholar
Song, L., McGee, J. & Walsh, E. J. Frequency-and level-dependent changes in auditory brainstem responses (ABRs) in developing mice. J. Acoust. Soc. Am. 119, 2242–2257 (2006).
Article ADS PubMed Google Scholar
Xie, W. et al. Base-resolution analyses of sequence and parent-of-origin dependent DNA methylation in the mouse genome. Cell 148, 816–831 (2012).
Article CAS PubMed PubMed Central Google Scholar
Lister, R. et al. Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 471, 68–73 (2011).
Article ADS CAS PubMed PubMed Central Google Scholar
Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).
Article ADS CAS PubMed PubMed Central Google Scholar
Ziller, M. J. et al. Genomic distribution and inter-sample variation of non-CpG methylation across human cell types. PLoS Genet. 7, e1002389 (2011).
Article CAS PubMed PubMed Central Google Scholar
Burger, L., Gaidatzis, D., Schübeler, D. & Stadler, M. B. Identification of active regulatory regions from DNA methylation data. Nucleic Acids Res. 41, e155–e155 (2013).
Article PubMed PubMed Central Google Scholar
Stadler, M. B. et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature 480, 490–495 (2011).
ADS CAS PubMed Google Scholar
The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Article ADS PubMed Central Google Scholar
Shen, Y. et al. A map of the cis-regulatory sequences in the mouse genome. Nature 488, 116 (2012).
Article ADS CAS PubMed PubMed Central Google Scholar
Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).
Article CAS PubMed PubMed Central Google Scholar
Heintzman, N. D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108–112 (2009).
Article ADS CAS PubMed PubMed Central Google Scholar
He, B., Chen, C., Teng, L. & Tan, K. Global view of enhancer–promoter interactome in human cells. Proc. Natl. Acad. Sci. USA 111, E2191–E2199 (2014).
Article ADS CAS PubMed PubMed Central Google Scholar
Brownstein, Z. et al. Targeted genomic capture and massively parallel sequencing to identify genes for hereditary hearing loss in Middle Eastern families. Genome Biol. 12, R89 (2011).
Article CAS PubMed PubMed Central Google Scholar
Raimundo, N. et al. Mitochondrial stress engages E2F1 apoptotic signaling to cause deafness. Cell 148, 716–726 (2012).
Article CAS PubMed PubMed Central Google Scholar
Morrill, S. & He, D. Z. Apoptosis in inner ear sensory hair cells. J. Otol. 12, 151–164 (2017).
Article PubMed PubMed Central Google Scholar
Chen, J. et al. A systems-level approach reveals new gene regulatory modules in the developing ear. Development 144, 1531–1543 (2017).
Article CAS PubMed PubMed Central Google Scholar
Zheng, W. et al. The role of Six1 in mammalian auditory system development. Development 130, 3989–4000 (2003).
Article CAS PubMed Google Scholar
Liang, J. et al. The stat3/socs3a pathway is a key regulator of hair cell regeneration in zebrafish. J. Neurosci. 32, 10662–10673 (2012).
Article CAS PubMed PubMed Central Google Scholar
Flora, A., Garcia, J. J., Thaller, C. & Zoghbi, H. Y. The E-protein Tcf4 interacts with Math1 to regulate differentiation of a specific subset of neuronal progenitors. Proc. Natl. Acad. Sci. USA 104, 15382–15387 (2007).
Article ADS CAS PubMed PubMed Central Google Scholar
Kuwahara, A. et al. Tcf3 represses Wnt–β-catenin signaling and maintains neural stem cell population during neocortical development. PloS One 9, e94408 (2014).
Article ADS PubMed PubMed Central Google Scholar
Riccomagno, M. M., Takada, S. & Epstein, D. J. Wnt-dependent regulation of inner ear morphogenesis is balanced by the opposing and supporting roles of Shh. Genes Dev. 19, 1612–1623 (2005).
Article CAS PubMed PubMed Central Google Scholar
Mulvaney, J. & Dabdoub, A. Atoh1, an essential transcription factor in neurogenesis and intestinal and inner ear development: function, regulation, and context dependency. J. Assoc. Res. Otolaryngol. 13, 281–293 (2012).
Article PubMed PubMed Central Google Scholar
Jiang, L., Romero-Carvajal, A., Haug, J. S., Seidel, C. W. & Piotrowski, T. Gene-expression analysis of hair cell regeneration in the zebrafish lateral line. Proc. Natl. Acad. Sci. USA 111, E1383–E1392 (2014).
Article ADS CAS PubMed PubMed Central Google Scholar
Chung, J. W., Kang, H. H., Shin, J. E. & Kim, J. U. Accumulation of hypoxia-inducible factor-1α in mouse inner ear by noise stimulation. Neuroreport 15, 2353–2356 (2004).
Article CAS PubMed Google Scholar
Chung, J. W. et al. Up-regulation of hypoxia-inducible factor-1 alpha by cobalt chloride prevents hearing loss in noise-exposed mice. Environ. Toxicol. Pharmacol. 31, 153–159 (2011).
Article CAS PubMed Google Scholar
Gou, Y., Vemaraju, S., Sweet, E. M., Kwon, H.-J. & Riley, B. B. Sox2 and sox3 play unique roles in development of hair cells and neurons in the zebrafish inner ear. Dev. Biol. 435, 73–83 (2018).
Article CAS PubMed PubMed Central Google Scholar
Steevens, A. R., Sookiasian, D. L., Glatzer, J. C. & Kiernan, A. E. SOX2 is required for inner ear neurogenesis. Sci. Rep. 7, 4086 (2017).
Article ADS PubMed PubMed Central Google Scholar
Grati, M. H. et al. MYO3A causes human dominant deafness and interacts with protocadherin 15‐CD2 isoform. Hum. Mutat. 37, 481–487 (2016).
Article CAS PubMed PubMed Central Google Scholar
Schneider, M. E. et al. A new compartment at stereocilia tips defined by spatial and temporal patterns of myosin IIIa expression. J. Neurosci. 26, 10243–10252 (2006).
Article CAS PubMed PubMed Central Google Scholar
Xiang, M. et al. Essential role of POU–domain factor Brn-3c in auditory and vestibular hair cell development. Proc. Natl. Acad. Sci. USA 94, 9445–9450 (1997).
Article ADS CAS PubMed PubMed Central Google Scholar
Naranjo, S. et al. Multiple enhancers located in a 1-Mb region upstream of POU3F4 promote expression during inner ear development and may be required for hearing. Hum. Genet. 128, 411–419 (2010).
Article CAS PubMed PubMed Central Google Scholar
Hawkins, R. D. et al. Distinct epigenomic landscapes of pluripotent and lineage-committed human cells. Cell Stem Cell 6, 479–491 (2010).
Article CAS PubMed PubMed Central Google Scholar
Hawkins, R. D. et al. Global chromatin state analysis reveals lineage-specific enhancers during the initiation of human T helper 1 and T helper 2 cell polarization. Immunity 38, 1271–1284 (2013).
Article CAS PubMed PubMed Central Google Scholar
Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Article CAS PubMed PubMed Central Google Scholar
Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–5119 (2012).
Article ADS CAS PubMed PubMed Central Google Scholar
MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).
Article CAS PubMed Google Scholar
Kunst, H. et al. Non-syndromic autosomal dominant progressive non-specific mid-frequency sensorineural hearing impairment with childhood to late adolescence onset (DFNA21). Clin. Otolaryngol. Allied Sci. 25, 45–54 (2000).
Article CAS PubMed Google Scholar
Snoeckx, R. L. et al. A novel locus for autosomal dominant non-syndromic hearing loss, DFNA31, maps to chromosome 6p21.3. J. Med. Genet. 41, 11–13 (2004).
Article CAS PubMed PubMed Central Google Scholar
Ito, T. et al. SLC26A4 genotypes and phenotypes associated with enlargement of the vestibular aqueduct. Cell Physiol. Biochem. 28, 545–552 (2011).
Article CAS PubMed PubMed Central Google Scholar
Cheng, A. W. et al. Multiplexed activation of endogenous genes by CRISPR-on, an RNA-guided transcriptional activator system. Cell Res. 23, 1163–1171 (2013).
Article CAS PubMed PubMed Central Google Scholar
Lerer, I. et al. A deletion mutation in GJB6 cooperating with a GJB2 mutation in trans in non‐syndromic deafness: a novel founder mutation in Ashkenazi Jews. Hum. Mutat. 18, 460–460 (2001).
Article CAS PubMed Google Scholar
Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260–D266 (2018).
Article PubMed Google Scholar
Van Laer, L. et al. A common founder for the 35delG GJB2 gene mutation in connexin 26 hearing impairment. J. Med. Genet. 38, 515–518 (2001).
Article PubMed PubMed Central Google Scholar
Chen, P., Johnson, J. E., Zoghbi, H. Y. & Segil, N. The role of Math1 in inner ear development: Uncoupling the establishment of the sensory primordium from hair cell fate determination. Development 129, 2495–2505 (2002).
Article CAS PubMed Google Scholar
Murata, J., Ikeda, K. & Okano, H. Notch signaling and the developing inner ear. In Notch Signaling in Embryology and Cancer (eds Reichrath, J. & Reichrath, S.) 161–173 (Springer, 2012).
Gálvez, H., Tena, J. J., Giraldez, F. & Abelló, G. The repression of Atoh1 by Neurogenin1 during inner ear development. Front. Mol. Neurosci. 10, 321 (2017).
Article PubMed PubMed Central Google Scholar
Masuda, M., Pak, K., Chavez, E. & Ryan, A. F. TFE2 and GATA3 enhance induction of POU4F3 and myosin VIIa positive cells in nonsensory cochlear epithelium by ATOH1. Dev. Biol. 372, 68–80 (2012).
Article CAS PubMed PubMed Central Google Scholar
Urich, M. A., Nery, J. R., Lister, R., Schmitz, R. J. & Ecker, J. R. MethylC-seq library preparation for base-resolution whole-genome bisulfite sequencing. Nat. Protoc. 10, 475–483 (2015).
Article CAS PubMed PubMed Central Google Scholar
Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).
Article CAS PubMed PubMed Central Google Scholar
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Article PubMed PubMed Central Google Scholar
Kishore, K. et al. methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data. BMC Bioinformatics 16, 313 (2015).
Article PubMed PubMed Central Google Scholar
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Article CAS PubMed PubMed Central Google Scholar
Corradin, O. et al. Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits. Genome Res. 24, 1–13 (2014).
Article CAS PubMed PubMed Central Google Scholar
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Article CAS PubMed PubMed Central Google Scholar
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Article CAS PubMed PubMed Central Google Scholar
Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2008).
Article Google Scholar
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
Article CAS PubMed PubMed Central Google Scholar
Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518, 365–369 (2015).
Article ADS CAS PubMed PubMed Central Google Scholar
Wang, X. et al. Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures. eLife 5, e10557 (2016).
Article PubMed PubMed Central Google Scholar
Labun, K., Montague, T. G., Gagnon, J. A., Thyme, S. B. & Valen, E. CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering. Nucleic Acids Res. 44, W272–W276 (2016).
Article CAS PubMed PubMed Central Google Scholar

Download references

Acknowledgements

This work was supported by the United States-Israel Binational Science Foundation grant no. 2013027 (K.B.A. and R.D.H.); the Israel Science Foundation grant no. 2033/16 (K.B.A.); the National Institutes of Health/NIDCD R01DC011835 (K.B.A.); NIH/NIAMS R01AR065952 (R.D.H.); NIH/NIDDK R01DK103667 (R.D.H.); Action on Hearing Loss Flexi Grant (K.B.A.) and I-CORE Program of the Planning and Budgeting Committee and The Israel Science Foundation no. 41/11 (K.B.A.). K.B.A. is an incumbent of the Drs. Sarah and Felix Dumont Chair for Research of Hearing Disorders. This work was performed in partial fulfillment of the requirements for a Ph.D. degree by Ofer Yizhar-Barnea, recipient of the Prof. Dr. Heinrich Neumann von Hethars Doctoral Scholarship, at the Sackler Faculty of Medicine, Tel Aviv University, Israel.

Author information

Ofer Yizhar-Barnea and Cristina Valensisi contributed equally

Authors and Affiliations

Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
Ofer Yizhar-Barnea, Tal Koffler-Brill, Kathy Ushakov, Kobi Perl, Yael Noy, Yoni Bhonker & Karen B. Avraham
Division of Medical Genetics, Department of Medicine, Department of Genome Sciences, Institute for Stem Cell and Regenerative Medicine, University of Washington School of Medicine, Seattle, WA, 98195, USA
Cristina Valensisi, Naresh Doni Jayavelu, Colin Andrus & R. David Hawkins
Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia, Milano, 20139, Italy
Kamal Kishore & Mattia Pelizzola

Authors

Ofer Yizhar-Barnea
View author publications
You can also search for this author in PubMed Google Scholar
Cristina Valensisi
View author publications
You can also search for this author in PubMed Google Scholar
Naresh Doni Jayavelu
View author publications
You can also search for this author in PubMed Google Scholar
Kamal Kishore
View author publications
You can also search for this author in PubMed Google Scholar
Colin Andrus
View author publications
You can also search for this author in PubMed Google Scholar
Tal Koffler-Brill
View author publications
You can also search for this author in PubMed Google Scholar
Kathy Ushakov
View author publications
You can also search for this author in PubMed Google Scholar
Kobi Perl
View author publications
You can also search for this author in PubMed Google Scholar
Yael Noy
View author publications
You can also search for this author in PubMed Google Scholar
Yoni Bhonker
View author publications
You can also search for this author in PubMed Google Scholar
Mattia Pelizzola
View author publications
You can also search for this author in PubMed Google Scholar
R. David Hawkins
View author publications
You can also search for this author in PubMed Google Scholar
Karen B. Avraham
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

O.Y.-B., K.B.A. and R.D.H. conceived the project and together with C.V., designed experiments, analyzed the data, and wrote the manuscript, with input from all authors. N.D.J., K.K., C.A., M.P., O.Y.-B., C.V. and K.P. performed the bioinformatics and data analysis. O.Y.-B., C.V., T.K.-B., K.U., Y.N. and Y.B. performed the laboratory experiments. All authors have read and approved the manuscript for publication.

Corresponding authors

Correspondence to R. David Hawkins or Karen B. Avraham.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Supplementary Information

Supplementary Table S1

Supplementary Table S2

Supplementary Table S3

Supplementary Table S4

Supplementary Table S5

Supplementary Table S6

Supplementary Table S7

Supplementary Table S8

Supplementary Table S9

Supplementary Table S10

Supplementary Table S11

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Yizhar-Barnea, O., Valensisi, C., Jayavelu, N.D. et al. DNA methylation dynamics during embryonic development and postnatal maturation of the mouse auditory sensory epithelium. Sci Rep 8, 17348 (2018). https://doi.org/10.1038/s41598-018-35587-x

Download citation

Received: 26 June 2018
Accepted: 08 November 2018
Published: 26 November 2018
DOI: https://doi.org/10.1038/s41598-018-35587-x

Keywords

This article is cited by

Changes in DNA methylation hallmark alterations in chromatin accessibility and gene expression for eye lens differentiation
- Joshua Disatham
- Lisa Brennan
- Marc Kantorow
Epigenetics & Chromatin (2022)
The noncoding genome and hearing loss
- Karen B. Avraham
- Lama Khalaily
- Shahar Taiber
Human Genetics (2022)
Novel gene discovery for hearing loss and other routes to increased diagnostic rates
- Hannie Kremer
Human Genetics (2022)
Scrutinizing pathogenicity of the USH2A c.2276 G > T; p.(Cys759Phe) variant
- Janine Reurink
- Erik de Vrieze
- Erwin van Wijk
npj Genomic Medicine (2022)
Hearing loss genes reveal patterns of adaptive evolution at the coding and non-coding levels in mammals
- Anabella P. Trigila
- Francisco Pisciottano
- Lucía F. Franchini
BMC Biology (2021)

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.