Abstract
The microRNA miR-96 is important for hearing, as point mutations in humans and mice result in dominant progressive hearing loss. Mir96 is expressed in sensory cells along with Mir182 and Mir183, but the roles of these closely-linked microRNAs are as yet unknown. Here we analyse mice carrying null alleles of Mir182, and of Mir183 and Mir96 together to investigate their roles in hearing. We found that Mir183/96 heterozygous mice had normal hearing and homozygotes were completely deaf with abnormal hair cell stereocilia bundles and reduced numbers of inner hair cell synapses at four weeks old. Mir182 knockout mice developed normal hearing then exhibited progressive hearing loss. Our transcriptional analyses revealed significant changes in a range of other genes, but surprisingly there were fewer genes with altered expression in the organ of Corti of Mir183/96 null mice compared with our previous findings in Mir96Dmdo mutants, which have a point mutation in the miR-96 seed region. This suggests the more severe phenotype of Mir96Dmdo mutants compared with Mir183/96 mutants, including progressive hearing loss in Mir96Dmdo heterozygotes, is likely to be mediated by the gain of novel target genes in addition to the loss of its normal targets. We propose three mechanisms of action of mutant miRNAs; loss of targets that are normally completely repressed, loss of targets whose transcription is normally buffered by the miRNA, and gain of novel targets. Any of these mechanisms could lead to a partial loss of a robust cellular identity and consequent dysfunction.
Introduction
The microRNAs miR-96, miR-182 and miR-183 are expressed together on a single transcript in sensory cells, including the retina and the hair cells of the inner ear [1, 2]. Point mutations in Mir96 cause rapidly progressive hearing loss in the diminuendo mouse mutant (Mir96Dmdo [3]) and progressive hearing loss with later onset in human families [4, 5], and the diminuendo mutation has also been shown to delay maturation of the central auditory system [6]. In homozygous Mir96Dmdo mice, most of the cochlear hair cells die by 28 days after birth. However, this is not the cause of the hearing loss; even before the onset of normal hearing, homozygote hair cells fail to mature both morphologically and physiologically, remaining in their immature state, and heterozygote hair cells show a developmental delay. miR-96 is thus thought to be responsible for coordinating hair cell maturation [7, 8].
Overexpression of the three microRNAs also results in cochlear defects and hearing loss [9]. The complete loss of all mature miRNAs from the inner ear results in very early developmental defects including a severely truncated cochlear duct [10, 11]. miR-96, miR-182 and miR-183 have also been implicated in other diseases, including glaucoma [12], ischemic injury [13, 14] and spinal cord injury [15].
MicroRNAs regulate the expression of many other genes by targeting specific sequences in their mRNAs, leading to transcript destabilisation or translational inhibition. Transcriptome analyses of the Mir96Dmdo mouse organ of Corti showed that many genes were dysregulated in homozygotes, including several known to be important for hearing which appear to contribute to specific aspects of the diminuendo phenotype [3, 7, 8, 16]. However, the diminuendo mutation is a single base pair change in the seed region of the microRNA which is critical for correct targeting, and it is not clear to what extent the diminuendo mutant phenotype is the result of the loss of normal targets of miR-96, and how much is due to the gain of novel targets. We previously suggested that the progressive hearing loss was most likely due to the loss of normal target repression because all three point mutations in mouse and human Mir96 lead to a similar phenotype which seems unlikely if the gain of novel targets is the main mechanism involved.
The regulatory network generated from Mir96Dmdo expression data [16] includes a number of genes known to be involved in deafness such as Ptprq, Gfi1, Kcna10 and Slc26a5 as well as new candidate genes. Manipulating this network may be a useful therapeutic approach to treating hearing loss due to hair cell dysfunction triggered by a broad range of factors, including both genetic variants and environmental insults. For example, Trp53, Hif1a and Nfe2l2 are in the Mir96Dmdo network and are involved in cellular responses to stress [17]. In order to focus our translational efforts, it is important to understand better the molecular basis of the network. For this reason, we have analysed a second mutation of Mir96 in this report, a double knockout of Mir96 and Mir183, as well as a knockout of the closely linked Mir182 gene, generated through a mouse miRNA knockout program [18]. We carried out Auditory Brainstem Response (ABR) and Distortion Product Otoacoustic Emission (DPOAE) tests to characterise their hearing, scanning electron and confocal microscopy to examine their hair cells and synapses, and mRNA sequencing (RNA-seq) to study their transcriptomes. While both new mouse mutants exhibit hearing loss, their electrophysiological and transcriptional phenotypes differ from the diminuendo mouse, with no sign of hearing loss in the heterozygotes, suggesting that the more severe phenotype of Mir96Dmdo mutants is likely to be mediated by the gain of novel target genes in addition to the loss of its normal targets.
Results
Mir183/96 and Mir182 knockout mice
Two mouse lines were used in this study; a knockout of Mir182 (Mir182tm1Hmpr/Wtsi, referred to from here on as Mir182ko) and a double knockout of both Mir183 and Mir96 (Mirc40tm1Hmpr/WtsiOulu, referred to from here on as Mir183/96dko), which are only 116bp apart, making it technically challenging to generate two separate knockouts. The mice were generated and maintained on the C57BL/6N genetic background. C57BL/6 mice are known to have age-related hearing loss, partly due to the Cdh23ahl allele [19]. Higher frequencies are affected first, after four weeks of age, while the lower frequencies remain unaffected up to 6 months old [20]. We observed a similar pattern in wildtype mice from both the Mir183/96dko and Mir182ko lines, which exhibited mild progressive hearing loss at 24-42kHz from 8 weeks old but retained good hearing sensitivity at frequencies between 3-12kHz up to six months old (Fig. 1).
For the Mir183/96dko mice, 43 out of 242 mice (17.8%) from heterozygote by heterozygote matings were homozygous for the Mir183/96 null allele, which is lower than the expected production (25%), suggesting that the absence of Mir96 and/or Mir183 has a small impact on viability (p=0.029, chi-squared test). For the Mir182ko mice, 42 homozygotes out of 152 pups in total (27.6%) were produced from heterozygote by heterozygote matings, which is consistent with the mutation having no impact on viability.
Complete knockout of miRNA expression in the mutant organ of Corti
We carried out qPCR to test the expression levels of the three microRNAs in the organs of Corti of wildtypes, heterozygotes and homozygotes of each knockout. In Mir183/96dko homozygotes, there was no detectable expression of Mir183 or Mir96. Likewise, in Mir182ko homozygotes there was no detectable expression of Mir182 (Supplementary Fig. S1). The levels of expression in heterozygotes of both knockouts was variable, as was the expression of Mir182 in Mir183/96dko homozygotes, and Mir183 and Mir96 in Mir182ko homozygotes. It is likely that this is because we lack a proper microRNA control for sensory tissue in the inner ear. We used Mir99a, which is expressed in almost all cell types in the cochlea, including hair and supporting cells [10], but because Mir183, Mir182 and Mir96 are also expressed in hair cells, it is possible that the mutant alleles affect the expression of Mir99a in hair cells, making it an unreliable calibrator between wildtype, heterozygote and homozygote. A better calibrator would be a microRNA expressed only in supporting cells and not in hair cells.
Impaired auditory responses in homozygotes but normal thresholds in heterozygotes
Mir183/96dko homozygous mice were profoundly deaf, with most showing no response at the highest sound level tested (95dB SPL) at any of the ages tested (14 days to six months old). In contrast, Mir182ko homozygotes exhibited only a mild hearing loss starting at higher frequencies and progressing with age to lower frequencies (Fig. 1). The ABR thresholds of heterozygotes were normal at all ages tested (Fig. 1, see Supplementary Figures S2, S3 for individually plotted traces). ABR waveforms of Mir183/96dko heterozygotes and Mir182ko homozygotes were also similar to those of wildtype littermates at the equivalent sound pressure level above threshold (sensation level, SL) (Supplementary Fig. S4). We measured DPOAEs at 8 weeks old and found no difference in the amplitudes or thresholds between wildtype and heterozygous Mir183/96dko mice, while homozygotes had severely abnormal responses (Supplementary Fig. S6). Mir182ko homozygotes had raised DPOAE thresholds at high frequencies compared to wildtypes (Supplementary Fig. S6), which matched the difference in their ABR thresholds at 8 weeks (Fig. 1). Mir182ko mutant mice showed no sign of a vestibular defect (circling, head-bobbing or hyperactivity) up to six months old. However, Mir183/96dko homozygotes did show increasing incidence of hyperactivity with age (Supplementary Fig. S5).
Heterozygous Mir183/96dko mice recover normally from noise exposure
As heterozygous Mir183/96dko mice showed no auditory deficit, in contrast to the hearing loss seen in diminuendo heterozygotes and in humans carrying one mutant MIR96 allele, we asked if these heterozygous mice might be more sensitive to noise-induced damage. One day after noise exposure (2 hours of 8-16kHz octave-band noise at 96dB SPL) at 8 weeks old, both Mir183/96dko heterozygous and wildtype mice showed a marked increase in thresholds at 12kHz and above compared to unexposed control littermates (Fig. 2). Three days after noise exposure the 12kHz thresholds had recovered, but there was still a noticeable elevation at higher frequencies. By 7 days after exposure, all thresholds had recovered completely (Fig. 2). We measured the amplitude of wave 1 of the ABR waveform to look for a reduced neural response, which has been reported in CBA/CaJ mice after noise exposure [21] and is thought to be due to neuronal loss in the cochlea, but no difference was observed at 12 kHz (Fig. 2). At 24kHz, we observed a much greater effect, such that in most animals wave 1 was too poorly defined to measure the amplitude one day after exposure. However, both wildtype and heterozygote wave 1 amplitudes had recovered by 28 days after exposure, and did not look any different to mice which had not been exposed (Fig. 2; Supplementary Fig. S7).
Since there was a significant difference in the higher frequencies between the unexposed heterozygotes and wildtypes at 8 weeks old (Fig. 2, top left panel), but we did not see any difference in our original ABR tests (Fig. 1, P56), we compared the ABR thresholds from all mice tested at 8 weeks old (Supplementary Fig. S8). At high frequencies (30-42kHz) the thresholds were very variable in both heterozygotes and wildtypes. The differences in the means at 36kHz and 42kHz are statistically significant (P = 0.001, P=0.000; mixed linear model pairwise comparison), but given the variability we suggest that this is not biologically relevant.
Hair cells and innervation
We examined the organ of Corti using scanning electron microscopy and found that hair cells in homozygous Mir183/96dko mice were severely affected at four weeks old (Fig. 3), with many hair bundles missing entirely. Where present, the stereocilia bundles of both outer and inner hair cells show splaying and fusion. The inner hair cells of Mir183/96dko heterozygotes are unaffected, but the outer hair cells’ upper surface appear slightly smaller and rounder in shape than the wildtype outer hair cells (Fig. 3). Their stereocilia bundles also appear smaller and more rounded than normal, with more pronounced tapering in height and overlap of shorter stereocilia rows with taller rows towards the two ends of each bundle.
Four week old Mir182ko heterozygotes and homozygotes showed no abnormalities of hair cells by scanning electron microscopy at either the 12kHz or 24kHz regions (Supplementary Fig. S9), corresponding to their normal ABR thresholds at that age. At eight weeks old, when hearing loss is evident at 24kHz and higher (Fig. 1), we also saw no systematic differences between wildtypes, heterozygotes and homozygotes (Fig. 4).
The distribution of unmyelinated neurons appeared normal in both mutants using anti-neurofilament labelling (Supplementary Fig. S10). Synapses were examined using anti-Ribeye antibody to mark presynaptic ribbons and anti-GluR2 to mark postsynaptic densities, and a significant reduction was found in the number of colocalised pre- and postsynaptic markers in Mir183/96dko homozygotes (Fig. 5, p=0.016, one way ANOVA). No difference in synapse counts was observed in Mir182ko homozygotes.
Transcriptome analysis reveals misregulation of gene expression in mutants
To investigate the impact of the Mir182ko and Mir183/96dko mutations on gene expression we carried out RNA-seq of isolated organ of Corti preparations from postnatal day (P)4 homozygotes and sex-matched littermate wildtype controls. This age was chosen to ensure all hair cells were still present and to facilitate comparison with our previous transcriptome data from Mir96Dmdo mice [3]. Thirty-four genes were identified as significantly misregulated (FDR < 0.05) in Mir183/96dko homozygotes, 22 upregulated and 12 downregulated compared with wildtype littermates. Many of the upregulated genes have sequences complementary to either the miR-96 seed region or the miR-183 seed region in their 3’UTRs (Table 1). Of this list of 34 genes, only Hspa2, Ocm, Myo3a, Slc26a5, Slc52a3, St8sia3 and Sema3e were previously found to be misregulated in Mir96Dmdo mice at P4 and/or P0 [3, 16], and in each case the misregulation is in the same direction (Table 1). We tested 19 genes of the 34, selected because they were reported to show a large difference in expression levels between sensory and non-sensory cells in the organ of Corti (http://www.umgear.org; [22, 23]). All but three were confirmed correct (Table 1, Supplementary Fig. S11); those that were not confirmed by qPCR were either not misregulated or failed the significance test, and no genes were significantly misregulated in the opposite direction. Of the genes misregulated in Mir183/96dko homozygotes, five are known deafness genes; Myo3a, Slc26a5 and Tmc1 underlie deafness in mice and humans [24–29], while mutations in Sema3e cause deafness in people [30], and mice homozygous for a mutant allele of Ocm exhibit progressive hearing loss [31].
It is possible that the difference between the transcriptomes of Mir183/96dko knockout mice and Mir96Dmdo mice is due to the different backgrounds. We looked for differences in predicted targets in the 3’UTRs of the C57BL/6NJ and C3H/HeJ genome sequences [32], which were the closest genome sequences to our mutant backgrounds available, and found that 1585 genes had the same number of seed matches, and 13 genes had seed matches in both strains, but not the same number of matches. 36 genes had no seed matches in the C57BL/6NJ sequence but had one or more match in the C3H/HeJ sequence, and 47 genes had no seed matches in the C3H/HeJ sequence, but one or more in the C57BL/6NJ sequence (Supplementary Table S1).
Three genes were found to be significantly upregulated (FDR < 0.05) in the Mir182ko homozygotes by RNA-seq, one of which, Ppm1l, has sequences complementary to the seed region of miR-182 (Table 1). The other two, Grp and Ccer2, were also upregulated in the Mir183/96dko homozygotes. No genes were significantly downregulated in Mir182ko. We tested the upregulated genes by qPCR, and also tested Slc26a5 and Ocm (which were strongly downregulated in Mir96Dmdo homozygotes [3]) and found that only the upregulation of Grp was confirmed. Ccer2 and Ppm1l were upregulated but not significantly, and Slc26a5 and Ocm were downregulated but again, not significantly (Table 1, Supplementary Fig. S11).
To assess the impact of these miRNA knockouts on a genome-wide level, we used Sylamer [33] to measure the enrichment and depletion of all possible heptamers in the 3’UTRs of each total gene list, ranked from most upregulated to most downregulated irrespective of significance. In the Mir183/96dko gene list, the sequence complementary to the seed region of miR-96 was markedly enriched in the upregulated genes (red line, Fig. 6A), and the sequence complementary to the seed region of miR-183 has a small peak towards the centre of the graph (dark blue line, Fig. 6A). While the targets of miR-183 are not notably misregulated in this dataset, its signal is still distinct from all other miRNAs (blue line, Fig. 6A). There were no miRNA seed region heptamers enriched in the Mir182ko gene list (Fig. 6B), but the TATTTAT heptamer which is enriched in the Mir182ko downregulated genes (yellow line, Fig. 6B) resembles a portion of an AU-rich element. These are 50-150bp sequence elements found in 3’UTRs, and are typically involved in mRNA destabilising via a deadenylation-dependent mechanism (reviewed in [34, 35]). AU-rich elements, and TATTTAT in particular, are enriched in 3’UTR regions which have multiple RNA-binding protein binding sites, and it has been suggested that RNA-binding proteins compete with the RISC complex to bind to miRNA target sites within these regions [36]. It is possible that this TATTTAT signal, which is enriched in the genes downregulated in Mir182ko homozygote hair cells, is the result of a change in the binding of RNA-binding proteins in the absence of miR-182.
No evidence for differential splicing
Splicing patterns can be affected by a microRNA if it targets a splicing factor, as has been shown for miR-133a and nPTB [37], so we analysed the Mir183/96dko and Mir182ko RNA-seq data for evidence of alternative splicing events. Tools which test RNA-seq data for differential splicing of mRNAs are not as well established as those for measuring differential expression, and there is great variability in the results depending on which method is used [38]. We therefore used three different tools to test for differential splicing, which all take a different approach. Cuffdiff (from the Cufflinks package [39]) maps reads to individual transcripts in order to estimate the relative abundance of each isoform. This can introduce uncertainty in the case of reads which map to exonic regions shared by more than one transcript. JunctionSeq [40], is a count-based tool which tests read counts belonging to exonic regions specific to individual transcripts, avoiding the problem of shared regions but potentially missing isoforms which have no unique exons. Leafcutter, a very recently developed algorithm, tests for differential splicing by using reads which span exon-exon junctions, in effect focussing on the excised intron [41].
All three tools detected significant differential splicing in Mir183/96dko homozygotes compared to their wildtype littermates (Supplementary Table S2), but only three genes were identified by more than one tool; Ppp3cb and Zfp618 (by Leafcutter and JunctionSeq), and Il17re (by Leafcutter and Cuffdiff). One of the advantages of Leafcutter and JunctionSeq is that they identify the sites at which they detect differential splicing occurring, which allowed us to test their results. We designed primers to cover the junctions and/or exons which were highly differentially spliced (15 genes; Supplementary Table S3) and sequenced PCR reactions from cDNA made from organ of Corti RNA from Mir183/96dko homozygotes and wildtypes. We found no evidence of the differential splicing detected by either tool, but evidence of a novel isoform of Stard9 was detected by JunctionSeq. The novel isoform includes an exon (ENSMUSE00001437951) previously assigned to a transcript subject to nonsense-mediated decay (transcript ENSMUST00000140843), but the exons following ENSMUSE00001437951 in our results belong to the protein-coding transcript (ENSMUST00000180041). Exon ENSMUSE00001437951 is 72bp long, so does not introduce a frameshift, and its inclusion may result in a functional Stard9 protein with 24 extra amino acids. The novel splicing was present in both wildtype and homozygous Mir183/96dko RNA (Supplementary Fig. S12).
JunctionSeq did not predict any significant differential splicing in Mir182ko homozygotes. Leafcutter and Cuffdiff identified some genes with differential splicing (Supplementary Table S2), but there were no genes common to both predictors. Two genes, Ube4a and Nrxn1, were predicted to have alternative splicing present in the homozygote and not in the wildtype, but when we sequenced their mRNA from Mir182ko homozygotes and wildtypes, we found no differences in the splicing pattern.
Immunohistochemistry confirms downregulation of Ocm
We carried out antibody stains on sections from the inner ear at P4, to check for the presence of Ocm (oncomodulin) and Slc26a5 (prestin) protein in the hair cells. Ocm staining is faint at P4, stronger in the basal turn of the cochlea, but prestin staining is clearly visible at that stage in wildtypes. We observed prestin staining in Mir183/96dko homozygotes, but no stain for Ocm, while both proteins were present in the wildtype littermate controls (Supplementary Fig. S13). Although immunohistochemistry is not a quantitative technique, this correlates with the qPCR results, which showed that Ocm RNA was nearly absent in Mir183/96dko homozygotes, while Slc26a5 RNA levels were about 30% of wildtype levels (Supplementary Fig. S11). Ocm and Prestin staining was visible in Mir182ko homozygotes and their wildtype littermate controls (Supplementary Fig. S13).
Network analysis suggests upstream regulators
We took three approaches to network analysis: Ingenuity Pathway Analysis, WGCNA, and network creation using existing publicly available regulatory data. First, we used Ingenuity Pathway Analysis to construct putative causal networks using the misregulated genes in each dataset with FDR<0.05, as follows. Upstream regulators which could explain the observed misregulation were identified through the Ingenuity Knowledge Base, a manually curated set of observations from the literature and from third-party databases. The activation or inhibition of each upstream regulator was calculated based on the observed misregulation of its target genes. Causal networks were then constructed to link the upstream regulators through a “root” regulator further upstream, again using the Ingenuity Knowledge Base (described in detail in [42]). The resulting networks were filtered based on their activation z-score, which is a measure of the match of observed and predicted misregulation patterns. The z-score is not just a measure of significance but also a prediction of the activation of the upstream root regulator [42]; scores below -2 are classed as significant by Ingenuity Pathway Analysis and indicate predicted downregulation or reduced activation of the root regulator, while scores above 2 are also considered significant and indicate predicted upregulation or increased activation of the root regulator. For the Mir183/96dko homozygotes, only four upstream regulators were given a z-score < -2, three more had z-scores < −1.7 and one had a z-score > 1.7. These eight upstream regulators, Bdnf, Cshl1, H2afx, Kcna3, Kit, Myocd, Prkaca and Pth, explain between them the misregulation of 14 genes (Supplementary Fig. S14), although they themselves are not misregulated (from the RNA-seq data). Most of the genes are predicted to be inhibited, so they cannot be directly linked to miR-96 (although Bdnf is a known target of miR-96 [43], which does not fit the predicted inhibition). Only Kcna3 is predicted to be activated (orange in Fig. 7), and it does not have any matches to the miR-96 or miR-183 seed regions in its 3’ UTR. Of these eight potential regulators, Bdnf and Kit are both known deafness genes [44, 45]. There were too few genes significantly misregulated in the Mir182ko RNA-seq data to obtain any causal networks with a significant activation z-score, but Ret and Adcyap1 were identified as immediate upstream regulators of Grp.
Gene clustering using weighted gene correlation network analysis (WGCNA) suggests further regulators
Weighted gene correlation network analysis is a method of analysing transcriptome data to cluster genes into modules based on their expression across a number of individual samples without reference to the sample traits [46]. We used a Pearson correlation to cluster genes across all 24 samples (Mir183/96dko and Mir182ko) and obtained 29 consensus modules (including the reserved “grey” module, which consists of genes outside of all the other modules) (Supplementary Table S4).
These consensus modules are groups of genes with highly correlated expression profiles. For further analysis, we obtained each module’s eigengene, which represents all the genes within the module, and we calculated the correlation of each eigengene with the traits of the mice used for RNA-seq (Supplementary Fig. S15). We then clustered the eigengenes to identify meta-modules where eigengenes were highly correlated with each other (Supplementary Fig. S16). Of the consensus module eigengenes, three were highly correlated and clustered with the wildtype vs. Mir183/96dko homozygote trait (green, black, royal blue, Supplementary Fig. S16A). This means that the expression levels of the genes of the green, black and royal blue modules are correlated with each other and the genotypes of the Mir183/96dko mice. One module was highly correlated and clustered with the wildtype vs. Mir182ko homozygote trait (salmon, Supplementary Fig. S16B)
We chose nine modules for further exploration which had significant correlation with the wildtype vs. Mir183/96dko homozygote or wildtype vs. Mir182ko homozygote traits (Supplementary Fig. S15; black, green, royal blue, salmon, purple, grey60, light green and dark green modules). We also investigated the blue module, which contained the most differentially expressed genes (13, Supplementary Table S4)). We carried out enrichment analysis using PANTHER v14 [47], comparing the module genes to the GO biological process listing [48, 49] and the Reactome pathway database [50]. We found five of the modules had significant enrichment of GO and/or Reactome gene sets, defined as enrichment score ≥ 5 and corrected p-value (FDR) < 0.05. Genes in the black module were enriched for GO terms such as cytoplasmic translational initiation (GO:0002183) and positive regulation of mRNA catabolic process (GO:0061014), suggesting genes in this module are involved in mRNA processing. Genes in the light green module are also enriched in GO terms involved in translation (eg negative regulation of cytoplasmic translation (GO:2000766) and negative regulation of transcription by competitive promoter binding (GO:0010944)). Green module genes were enriched for GO terms involved in cell metabolism, such as mitochondrial respiratory chain complex I assembly (GO:0032981) and ATP synthesis coupled proton transport (GO:0015986), and the Reactome pathway enrichment shows a similar set of terms (eg Formation of ATP by chemiosmotic coupling (R-MMU-163210)). Blue module genes were enriched for just two GO terms (inner ear receptor cell fate commitment (GO:0060120) and auditory receptor cell fate commitment (GO:0009912)) and one Reactome pathway (Regulation of lipid metabolism by Peroxisome proliferator-activated receptor alpha (PPARalpha) (R-MMU-400206)). The purple module genes are enriched for the Reactome Rho GTPase pathways and for GO terms involved in development and transcription (eg neural tube closure (GO:0001843), regulation of neuron apoptotic process (GO:0043523) and negative regulation of transcription by RNA polymerase II (GO:0000122)) (see Supplementary Tables S5, S6 for full listings of enriched GO terms and Reactome pathways).
We also investigated potential transcription factors which could be responsible for the altered regulation of the genes within each module using oPOSSUM [51], which detects over-represented transcription factor binding sites in a set of genes. We obtained 29 transcription factor binding site profiles across the 9 modules with Z and Fisher scores above our chosen threshold of the mean + 1 standard deviation (Supplementary Fig. S17, Supplementary Table S4) [51]. Several of the profiles were shared between modules (Supplementary Fig. S18). Transcription factors implicated in the black, green, blue and light green modules include one deafness gene (Foxi1 [52]) and several genes with miR-183/96/182 seed region matches, although none have yet been experimentally shown to be targets of any of the three microRNAs (Supplementary Table S7). Transcription factors implicated in the purple, royal blue and dark green modules also include genes with seed region matches, and Myc, which is also a deafness gene [53], is a potential regulator of the genes in the grey 60 module (Supplementary Table S7). None of the transcription factors were found to be misregulated in the RNA-seq data.
Networks created using publicly available regulatory data
The IPA networks did not connect the misregulated genes to any of the three microRNAs, probably because each microRNA controls a regulatory cascade, and the IPA causal networks have a maximum depth of 3, which may not be enough to reach the level of direct targets. The WGCNA analysis identified modules of co-expressed genes and highlighted transcription factors which may be involved, but did not connect these directly to either the microRNAs or the misregulated genes.
In our previous study on genes misregulated in Mir96Dmdo, we used regulatory interactions described in the literature to create an internally consistent network of regulatory interactions connecting miR-96 to as many of the misregulated genes as possible [16]. For the current study, we automated the procedure using a perl script (available at https://github.com/moraglewis/PoPCoRN) and made use of publicly available regulatory data in addition to the manually curated regulatory data from the literature we compiled before.
The resulting Mir183/96dko network consists of 114 genes and 416 links. Thirty misregulated genes have been included, 13 of them predicted targets of either miR-183 or miR-96 or both. Two misregulated genes were left out completely due to having no known upstream regulators in our compiled regulatory interactions (Ccer2, and Rn7s1) (Fig. 7A). We carried out a network analysis using the Cytoscape Network Analyser tool to calculate the degree and betweenness centrality of each node (gene). A node’s degree is the number of edges connecting it to other nodes, and its betweenness centrality measures how important it is for connecting distant parts of the network. The node with the highest degree is Trp53, with 23 edges. Most nodes have 9 or fewer edges (Fig. 7A), but 34 have 10 or more edges, including seven which are direct targets of miR-96, miR-183 or both (listed in Table 2). None of these 34 nodes with 10 or more edges are known to be misregulated in Mir183/96dko homozygotes, but eight are known deafness genes (Fos [54], Foxo3 [55], Kit [45], Mir96, Nfkb1 [56], Pkd1 [57], Rest [58] and Tnf [59]. Among these 34 nodes are Trp53, Sp1, Tgfb1, Tnf and Fos, which were also genes of interest in the manually-created network based on the Mir96Dmdo microarray data [16].
The Mir182ko network is very small, being based on three input genes: Ppm1l, Ccer2 and Grp. Ccer2 could not be included, due to the lack of any known regulators, and Ppm1l is a predicted direct target of miR-182. Multiple potential pathways link Grp to miR-182, but all of them work through Hnf4a, which upregulates Grp (Fig. 7B).
To validate the network analyses, we carried out qRTPCR on 11 genes from the Mir183/96dko network and two from the Mir182ko network (Fig. 7). Our choice was based on several factors, including their importance within the network, their edge count and close links to important genes (eg Ocm and Slc26a5). Fos and Trp53 were also predicted to be involved by the IPA analysis (Supplementary Fig. S14). However, we found all the genes had very variable expression levels in homozygotes when compared to littermate wildtypes (Supplementary Fig. S11). It’s possible that this is the result of expression in different areas of the inner ear. We controlled for the quantity of sensory tissue using Jag1, but the inner ear is a complex organ with many cellular subtypes, and if a gene is expressed in multiple cell types, controlling for overall tissue quantity (using Hprt) and sensory tissue quantity (using Jag1) may not be sufficient to permit detection of small changes in mRNA levels in the sensory tissue. In addition, qRTPCR can only detect mRNA levels, and can’t directly measure protein levels or protein activity.
Discussion
Mir183/96dko mice have a less severe phenotype than Mir96Dmdo mice
Mice heterozygous for the Mir96Dmdo point mutation exhibit early-onset rapidly progressive hearing loss; even at P15 they have very raised thresholds [8]. We initially concluded that this was more likely to be due to haploinsufficiency than to the gain of novel targets, because humans heterozygous for different point mutations also had progressive hearing loss [4]. However, heterozygous Mir183/96dko mice have ABR thresholds and DPOAE responses resembling the wildtype, and there is no difference in thresholds or wave 1 amplitudes between heterozygotes and wildtypes after noise exposure. Two recent studies of different knockout alleles targeting the entire microRNA cluster (miR-183/96/182) found that heterozygotes also exhibited normal hearing [60, 61]. It is possible that the more severe phenotype seen in Mir96Dmdo heterozygotes is due to the acquisition of new targets by the mutant microRNA, or it could be an effect of the different background as the Mir96Dmdo allele was generated by ENU mutagenesis on a C3HeB/FeJ background in contrast to the C57BL/6N background of the Mir183/96dko allele or the reported knockouts of the entire cluster, which were created in 129S2 [62] or 129SV6 [60] ES cells which were then crossed onto the C57BL/6J background [60, 62]. Mice homozygous for the Mir183/96dko allele showed no auditory brainstem responses at all ages tested from 14 days old onwards, and in this they resemble the Mir96Dmdo homozygotes, where the compound action potentials recorded from the round window of the cochlea were undetectable at four weeks old [3].
The vestibular phenotype is also different; Mir96Dmdo homozygotes circle by three weeks old, whereas a milder phenotype of variable hyperactivity was observed only in some Mir183/96dko homozygotes, the prevalence increasing with age (Supplementary Fig. S5). Circling is a more severe manifestation of vestibular dysfunction than hyperactivity. This could again be due to the C3HeB/FeJ background, because Mir96Dmdo mice carry the Pde6brd1 mutation causing retinal degeneration [63] and are blind by adulthood. The Mir183/96dko allele was generated on a C57BL/6N background, which lacks the Pde6brd1 mutation but has the Crb1rd8 mutation, which leads to variably penetrant retinal dysplasia [64]. Other ocular abnormalities have also been observed in C57BL/6N mice, such as lens abnormalities and vitreous crystalline deposits [64]. However, no ocular abnormalities have been reported for these mice in the IMPC pipeline (https://www.mousephenotype.org/data/genes/MGI:3619440), so it is likely that they have sufficient vision to partially compensate for the lack of vestibular sensory input, reducing the severity of the observed phenotype. It is notable that mice lacking the entire miR-183/96/182 cluster (on a mixed background of C57BL/6J and either 129S2 or 129SV6) exhibit both persistent circling behaviour and retinal defects [60, 62].
Stereocilia bundles and innervation in Mir183/96dko mice and Mir96Dmdo mice
The hair cells of Mir96Dmdo homozygous mice are present at four days old but appear abnormal, and by 28 days old have degenerated almost completely [3]. In Mir183/96dko homozygotes, however, some hair cell stereocilia bundles are still visible at P28, although they are severely disorganised (Fig. 3). In Mir183/96dko heterozygotes, IHC stereocilia are mostly normal and the OHC stereocilia bundles appear to be slightly rounded in arrangement (Fig. 3), but this is not reflected in their ABR thresholds or DPOAE responses, which are normal (Fig. 1 and Supplementary Fig. S5). Mir96Dmdo heterozygotes, which are deaf by P28, have a more severe phenotype, with fused stereocilia and hair cell degeneration as well as misshapen stereocilia bundles in outer hair cells, and smaller stereocilia bundles in inner hair cells [3].
Mir96Dmdo homozygotes exhibit disorganised innervation [8], but we did not see that degree of disorganisation in neurofilament-labelled preparations of Mir183/96dko homozygotes (Supplementary Fig. S10). However, we found significantly fewer colocalised pre- and postsynaptic densities under inner hair cells of Mir183/96dko homozygotes indicating synaptic defects (Fig. 5). Heterozygote Mir183/96dko mice showed no differences in innervation or synapse counts compared to wildtypes (Supplementary Fig. S10, Fig. 5). Similar to the physiological phenotype, the structural phenotype of Mir183/96dko heterozygotes is much less severe than that of Mir96Dmdo heterozygotes.
The Mir183/96dko transcriptome shows fewer genes are affected than in the Mir96Dmdo transcriptome
The Mir183/96dko transcriptome bears some resemblance to that of the Mir96Dmdo transcriptome from our previous studies [3, 16], but, as with the physiological and structural phenotypes, the effect of missing both miR-183 and miR-96 appears to be milder than the effect of a point mutation in the miR-96 seed region. Only 34 genes were identified as significantly misregulated by RNA-seq in the current study of Mir183/96dko, compared to 86 genes found to be significantly misregulated in the Mir96Dmdo P4 microarray [3], and only seven genes are similarly misregulated in both datasets: Hspa2, Ocm, Myo3a, Slc26a5, Slc52a3, St8sia3 and Sema3e (Table 1). Some of the differences will be due to certain genes not being represented on the microarray assay when it was carried out, such as Ccer2, and some may be the result of the different genetic background. We aimed to minimise this by use of the sex-matched wildtype littermates as controls, but it isn’t possible to entirely eliminate the effect of genetic background; there are 83 genes which lack miR-96 seed region matches in one background compared to the other (Supplementary Table S1), and any of those may be contributing to the phenotype. Much of the difference, however, is likely to be due to the loss of miR-183 in the Mir183/96dko homozygotes and the presence of the mutant miR-96 in Mir96Dmdo homozygotes. We were unable to confirm any of the predicted differential mRNA splicing in Mir183/96dko homozygotes, suggesting that differential splicing is unlikely to play a large part in the mutant phenotypes.
Network analyses of the Mir183/96dko transcriptome suggest multiple potential regulators
We took three approaches to find intermediate regulators, and obtained 8 upstream regulators from the Ingenuity Pathway Analysis causal network analysis, 29 transcription factor profiles from the WGCNA module analyses (Supplementary Table S7), and 34 highly connected nodes from our regulatory data-based network construction (Table 2). There are no genes suggested by all three approaches, although each one suggests multiple plausible candidates, such as Fos, Kit, Foxi1, Myc, Zeb1 and Foxo1. However, when we tested a selection of intermediate genes, we found their expression was very variable between different homozygotes (Supplementary Fig. S11). This was true even of Ikzf2, which is known to directly regulate Ocm and Slc26a5 [65], two critical genes for outer hair cell function which are strongly downregulated in both Mir96Dmdo [3, 16] and Mir183/96dko.
Identifying candidate direct targets of miR-96 in hair cells
The only network approach which suggested direct targets of miR-96 in the inner ear was our regulatory data-based approach, which identifies candidate targets that have been experimentally confirmed. There were 21 direct targets of miR-96 in total, 10 of which were genes found to be upregulated in the RNA-seq data. We used the gEAR dataset comparison tool [66] and data from mouse hair cells compared to the rest of the cochlear duct at p0 [22] to identify which of the 21 direct targets were excluded from hair cells but present in the rest of the cochlear duct. We found eight which showed this pattern of expression: Snai2, Zeb1, Irs1, Nr3c1, Foxo1, Alk, Eln and Rad51. Three of these are known deafness genes: Snai2, which is involved in melanocyte development [67], Zeb1, which is required for the specification of mesenchymal identity and repression of epithelial identity [68], and Irs1 [69]. The precise role of Irs1 in the function of the cochlea has not yet been elucidated, but since Zeb1 and Snai2 are known to be required for the development of non-sensory cells in the cochlear duct, it may be that miR-96 plays a role in establishing and/or maintaining the repression of non-hair cell genes in developing hair cells.
Nr3c1 and Foxo1 have the most downstream links of the 21 direct targets, predicted to regulate 9 and 12 genes respectively. Foxo1 encodes a forkhead family transcription factor, and the Nr3c1 gene encodes the glucocorticoid receptor GR, which is known to be expressed in the inner ear [70, 71]. No hearing or vestibular phenotypes have been reported on the Mouse Genome Informatics resource (http://www.informatics.jax.org) [72] for mice carrying mutations in either gene, but it is likely that the hearing of Nr3c1 mutants has simply never been checked. The hearing of Foxo1 knockout heterozygotes has been tested through the IMPC phenotyping pipeline and they were found to have normal hearing (http://www.mousephenotype.org, [73]) but since the knockout is homozygous lethal, there is no data for the effect of the absence of Foxo1 on the inner ear.
Only one of these eight genes was found to be upregulated in Mir183/96dko homozygotes in the RNA-seq data. Eln has two matches to the miR-96 seed region in its 3’UTR, and is thus a potential direct target of miR-96. It is a connective tissue protein and a component of the extracellular matrix [74], and it plays a regulatory role in controlling vascular smooth muscle cells via a GPCR pathway [75], but its downstream targets, and its role in the inner ear, are not yet known.
The gain of novel targets plays an important role in the phenotype resulting from a point mutation in Mir96
We originally hypothesised that the major effect of the Mir96Dmdo mutation on hearing was most likely mediated by a reduced level of downregulation of the normal target mRNAs [3], because two different point mutations in the human MIR96 seed region also led to hearing loss but each of the three seed region point mutations are predicted to have different novel targets. However, the difference in the transcriptomes, along with the less severe phenotype including normal ABR thresholds of Mir183/96dko heterozygotes, suggest that the gain of novel target mRNAs is also important for the diminuendo phenotype. From the microarray carried out on Mir96Dmdo P4 organ of Corti, we found 19 genes which were significantly downregulated in the mutant and which bore matches to the mutant seed region in their 3’ UTR [3]. The list includes one known deafness gene, Ptprq [76], as well as Chrna1, which is expressed in the organ of Corti from early postnatal stages onwards [77]. The hair cells of mice homozygous for a null allele of Ptprq closely resemble those seen in the Mir96Dmdo homozygotes and heterozygotes at P4 [7], so it’s possible that the more severe phenotype seen in Mir96Dmdo homozygotes is due in part to the downregulation of Ptprq by the mutant miR-96.
A model for mechanisms of action of mutant microRNAs
It has been suggested that miRNAs act in two ways; first, they repress targets to prevent translation, resulting in mutually exclusive expression of the miRNA and the target, and second, they buffer transcriptional noise, in which case the miRNA and its targets are co-expressed in the same cell [78]. Our approach should be able to detect both effects, but if a target is highly expressed in the non-sensory epithelial cells, the difference in expression between wildtype and homozygote due to derepression in the hair cells may not be detectable. Our current transcriptome data, therefore, is more likely to highlight targets which are being buffered rather than targets which are completely repressed by miR-96 [68]. This may explain why likely target genes like Zeb1, Foxo1 and Nr3c1 have not been found to be significantly misregulated in our transcriptome data. It may also explain the variability of the network genes we tested (Fig. S11E,F) - in the absence of the miRNA buffering, transcriptional noise has increased. This effect would be exacerbated by a mutated seed region, such as in the Mir96Dmdo mutant; not only would the normal buffering effect be gone, but multiple other genes would be misregulated within the hair cell, further disrupting normal cell function, as indeed we observed in the Mir96Dmdo transcriptome analyses [3, 16].
We suggest that the consistent misregulation of Ocm, Slc26a5, Myo3a, Sema3e and Slc52a3 observed in both the Mir96Dmdo [3, 16] and Mir183/96dko homozygotes is the result of the lack of repression of genes that would usually not be expressed in hair cells at all, for example Zeb1, Foxo1, and Nr3c1. The links between the direct targets of miR-96 and the consistently misregulated downstream genes are yet to be discovered, but we suggest Ikzf2 and Fos are likely to be involved (Fig. 8A, B).
There are multiple genes which are known or potential miR-96 targets, are upregulated in miR-96 mutants, and are expressed in hair cells, such as Hspa2, St8sia3 and Grk1. We suggest these are examples of genes whose expression is buffered by miR-96, not fully repressed but maintained at a consistent level. The loss of this buffering results in variable expression and transcriptional noise, not just of these genes (which will all be upregulated to varying degrees) but also of any genes which they regulate. This transcriptional noise is less likely to be consistent (at the gene level) between mice but will nonetheless contribute to the degraded functionality of the hair cell (Fig. 8A, B).
Finally, genes which are novel targets of a mutant microRNA have the potential to fulfil both roles. In the case of Mir96Dmdo, Ptprq is downregulated and it’s possible this is due to the mutant microRNA, since Ptprq bears a complementary match to the Mir96Dmdo seed region in its 3’ UTR. A reduction in Ptprq in the hair cells will contribute directly to the failure of the hair cells to mature properly. Other novel targets may also be directly affecting the hair cells, or may be adding to the overall transcriptional noise, or both (Fig. 8C).
The role of Mir183 in hearing remains unclear
The lack of a Mir183-specific mutation means that the effect of miR-183 alone is difficult to ascertain from our data. The relatively low mid-range peak for the miR-183 seed region in our Sylamer analysis of the RNA-seq data (Fig. 6A) implies that the lack of miR-183 has less of a global effect on the transcriptome than the lack of miR-96, and the literature-based regulatory network analysis reflects this; apart from its predicted targets, there are no downstream genes whose misregulation can be attributed to miR-183 alone (Fig. 7A). In that network, the only miR-183 targets which regulate downstream genes are also miR-96 targets.
Mice lacking Mir182 display mild hearing loss with no obvious changes in stereocilia bundles or hair cell innervation
The Mir182ko heterozygotes have audiograms which resemble the wildtype, while the homozygotes exhibit mild hearing loss at the higher frequencies (Fig. 1). No difference in hair cell stereocilia bundles, innervation or synapse counts was observed between the Mir182ko wildtype, heterozygous and homozygous mice at P28 (Supplementary Figures S9, S10, Fig. 5), but at that age Mir182ko homozygotes still have normal hearing. However, even at P56, when Mir182ko homozygotes exhibit high frequency hearing loss, the hair cells appear unaffected (Fig. 4). This is a completely different phenotype from either the Mir96Dmdo or the Mir183/96dko mice and may explain the paucity of significantly misregulated genes in the RNA-seq data from the P4 organ of Corti. This may be too early to see much effect of the absence of miR-182 on the transcriptome. The lack of significantly enriched heptamers corresponding to the miR-182 seed region in the Sylamer analysis for miR-182 also supports this hypothesis (Fig. 6B). The network drawn from the Mir182ko transcriptome may show the start of the misregulation cascade due to the microRNA knockout, and the transcription factors suggested by the WGCNA and IPA analyses may be involved, but further studies at a later age would be required for confirmation. None of the predicted differential mRNA splicing in Mir182ko homozygotes was confirmed.
Relevance to human MIR96 mutations
In this study we have shown that the phenotype of mice lacking miR-96 entirely is less severe than that of mice carrying a point mutation in the miR-96 seed region. Mice heterozygous for the Mir183/96dko allele display no auditory phenotype when compared to wildtypes, while Mir96Dmdo heterozygotes have rapid, severe progressive hearing loss. In our first study of the diminuendo point mutation, we concluded that the phenotype caused by the mutant miR-96 was most likely the result of loss of normal miR-96 targets, because all three mutations described (two in humans and one in the mouse) resulted in hearing loss [3, 4]. Our data from the current study, however, suggest that the gain of novel targets also plays an important role in the phenotype caused by mutations in miR-96. This has important implications for understanding the effect of mutant microRNAs in the human population. So far all three reported human mutations in miR-96 have been point mutations, two in the seed region and one in the stem region of the pre-miRNA [4, 5], and while all the individuals carrying those point mutations have exhibited some degree of progressive hearing loss, the phenotypes differ between people, as do the phenotypes of the mice carrying the Mir96Dmdo mutation and the Mir183/96dko allele. Determining the misregulated pathways common to all Mir96 mutations will be important not only for furthering our understanding of the role of this microRNA in hair cell development but also for developing therapeutic interventions, not only for people with MIR96 mutations but also more generally for hearing loss associated with hair cell defects.
Materials and Methods
Ethics approval: Mouse studies were carried out in accordance with UK Home Office regulations and the UK Animals (Scientific Procedures) Act of 1986 (ASPA) under UK Home Office licences, and the study was approved by both the Wellcome Trust Sanger Institute and the King’s College London Ethical Review Committees. Mice were culled using methods approved under these licences to minimize any possibility of suffering.
Mice
The miR-183/96 and miR-182 knockouts in C57BL/6N derived JM8.A3 ES cells were generated as previously described [18]. On chromosome 6 regions 30169424bp to 30169772bp (NCBIM38) for miR-182 and 30169424bp to 30169772bp (NCBIM38) for miR-183/96 were replaced with the PuroΔTK selection cassette. For both knockouts the PuroΔtk gene was subsequently deleted by transient transfection with Cre recombinase leading to recombination of the loxP sites that flanked the selection marker under 2-Fluoro-2-deoxy-1D-arabinofuranosyl-5-iodouracil (FIAU) selection. Heterozygous targeted ES cells were microinjected into C57BL/6N embryos for chimaera production. Mice were maintained on a C57BL/6N background. Both lines are available through EMMA (Mir183/96dko mice: EM:10856; Mir182ko mice: EM:12223).
Auditory Brainstem Response
The hearing of Mir183/96dko and Mir182ko homozygote, heterozygote and wildtype littermates was tested using the Auditory Brainstem Response, following the protocol described in [79]. Animals were sedated using a ketamine/xylazine mix (10mg ketamine and 0.1mg xylazine in 0.1ml per 10g body weight) and recovered where possible using atipamezole (0.01mg atipamezole in 0.1ml per 10g body weight) to enable recurrent testing of the same cohort of mice. All injections were intraperitoneal. Responses were recorded from three subcutaneous needle electrodes placed one over the left bulla (reference), one over the right bulla (ground) and one on the vertex (active). We used a broadband click stimulus and 3kHz, 6kHz, 12kHz, 18kHz, 24kHz, 30kHz, 36kHz and 42kHz pure tone frequencies, at sound levels from 0-95dB, in 5dB steps. 256 stimulus presentations per frequency were carried out per frequency and sound level, and responses were averaged to produce the ABR waveform. The threshold for each stimulus, the lowest intensity at which a waveform could be distinguished visually, was identified using a stack of response waveforms. Mice were tested at 14 days old (P14), P21, P28 ±1 day, P56 ±2 days, P90 ±2 days and P180 ±3 days. Any hyperactivity was noted prior to anaesthesia. Wave 1 amplitudes were calculated using ABR Notebook software (courtesy of MC Liberman, Harvard Medical School/Massachusetts Eye and Ear).
Distortion Product Otoacoustic Emission (DPOAE) measurements
We measured DPOAEs in mice aged 8 weeks old, anaesthetised with an intraperitoneal injection of 0.1ml / 10g of a solution of 20% urethane. Experiments were performed using Tucker Davis Technologies (TDT) BioSigRZ software driving a TDT RZ6 auditory processor and a pair of TDT MF1 magnetic loudspeakers. Signals were recorded via an Etymotic ER-10B+ low-noise DPOAE microphone. Stimulus tones (f1 & f2) were presented and microphone signals recorded via a closed-field acoustic system sealed into the auditory meatus of the mouse. Stimulus tones were presented at an f2:f1 ratio of 1.2. f2 tones were presented at frequencies to match ABR measurements (6, 12, 18, 24, 30 and 36 kHz). f1 was presented at levels from 0–85 dB in 5dB steps. f2 was presented at 10 dB below the level of f1. The magnitude of the 2f1-f2 DPOAE component was extracted from a fast Fourier transform of the recorded microphone signal and plotted as a function of f2 level. For each f2 level, the 20 spectral line magnitudes surrounding the 2f1-f2 frequency were averaged to form a mean noise floor estimate for each measurement. DPOAE threshold was defined as the lowest f2 stimulus level where the emission magnitude exceeded 2 standard deviations above the mean noise floor.
Noise exposure
Wildtype and heterozygous Mir183/96dko mice (P55±1 day) were subjected to an 8-16kHz octave-band noise at 96dB SPL for two hours while awake and unrestrained in separate small cages within an exposure chamber designed to provide a uniform sound field (for chamber details, see [80]). Band pass noise was generated digitally using TDT RPvdsEx software, converted to an analogue signal using a TDT RZ6 auditory processor, and amplified using a Brüel and Kjær Type 2716C power amplifier. It was delivered to a compression driver (JBL 2446H, Northridge, CA) connected to a flat front biradial horn (JBL 2380A, Northridge, CA) secured to the roof of the sound box. ABRs were carried out the day before, and 1, 3, 7, 14 and 28 days after the noise exposure. Unexposed littermates were used as controls and went through the same set of ABR measurements.
Genotyping
Mir183/96dko knockout mice were genotyped by PCR analysis using primers spanning the introduced deletion (Supplementary Table S3). The wildtype band is 841bp and the mutant band 645bp. Mir182ko mice were genotyped in a similar fashion with one of two primer sets (Supplementary Table S3). The wildtype band for the first set is 495bp and the mutant band 457bp. The wildtype band for the second set is 247bp, and the mutant band is 209bp.
Scanning electron microscopy
The inner ears of wildtype, heterozygote and homozygote mice at P28 (Mir183/96dko, Mir182ko) and P56 (Mir182ko) were fixed in 2.5% glutaraldehyde in 0.1M sodium cacodylate buffer with 3mM CaCl2 at room temperature for two hours. Cochleae were finely dissected in PBS and processed according to the OTOTO method [81]. Samples were dehydrated using an ethanol series, critical point dried and mounted for examination. Low resolution images were taken to identify the 12kHz region of the cochlea (using the frequency-place map described by Müller et al [82]). Higher-resolution images were taken using a JEOL JSM 7800 Prime scanning electron microscope under a standard magnification (60x) to show the whole organ of Corti, and at higher magnifications to examine hair cell rows (2000x) and individual hair cells (15000-23000x). Whole images have been adjusted in Photoshop to normalise dynamic range across all panels, and rotated where necessary to present a uniform orientation.
Wholemount immunostaining and confocal microscopy
The cochleae of P28 mice were fixed in 4% paraformaldehyde in PBS, washed in PBS, and decalcified in 10x ethylenediaminetetraacetic acid (EDTA) for 2 hours. After fine dissection, samples were blocked in 5% normal horse serum (NHS), 1% bovine serum albumin (BSA) and 0.3% Triton X-100 in PBS for 45 minutes at room temperature, then immunostained in 1% NHS and 0.3% Triton X-100 in PBS, as described in [83]. The primary antibodies used were anti-NFH (Abcam, cat. no: ab4680, diluted 1:800), anti-GluR2 (Millipore, cat. no: MAB397, diluted 1:200) and anti-Ribeye (Synaptic Systems, cat. no: 192 103, diluted 1:500), and the secondary antibodies were Alexa Fluor 488 goat anti-chicken (Invitrogen, cat. no: A11039, diluted 1:300), Alexa Fluor 546 goat anti-rabbit (Invitrogen, cat. no: A11035, diluted 1:300) and Alexa Fluor 488 goat anti-mouse (Invitrogen, cat. no: A21131, diluted 1:300). Samples were mounted in ProLong Gold antifade mounting medium with DAPI (Life Technologies, cat. no: P36931), or Vectashield Mounting Medium with DAPI (Vector Laboratories, cat. no: H-1200), and imaged with a Zeiss Imager 710 confocal microscope (plan-APOCHROMAT 63x Oil DIC objective) interfaced with ZEN 2010 software, or a Nikon A1R point-scanning confocal microscope (Plan Apo VC 60x/1.4NA oil objective) using NIS Elements v4.2 software (Nikon Instruments UK). Confocal z-stacks were obtained with a z-step size of 0.25µm (for synapses) or 0.4µm (for innervation). For synapse counting, a low magnification image was taken of approximately 20 inner hair cells in the 12kHz region, then a close-up image (2.5x optical zoom) was taken from both extremes of the low magnification image to ensure no synapses were counted twice. Synapses were counted from the maximum projection of the close up images using the FIJI plugin of ImageJ, and divided by the number of hair cell nuclei visible in the field of view (between 5 and 13, depending on which microscope was used) to obtain the number of synapses/hair cell. Where synapses were counted in both ears from the same mouse, the count was averaged before inclusion in the data. Whole images were processed in Adobe Photoshop, rotated where necessary, and adjusted so that all channels were equally visible.
Dissection and RNA extraction
The organs of Corti of four-day-old (P4) mice were dissected during a fixed time window (between 6 and 7.5 hours after lights on) to avoid circadian variation, and stored at −20°C in RNAlater stabilisation reagent (Ambion). RNA was extracted from both organs of Corti using either QIAshredder columns (QIAgen, cat. no. 79654) and the RNeasy mini kit (QIAgen, cat. no. 74104), or the Lexogen SPLIT kit (Lexogen, cat. no. 008.48), following the manufacturer’s instructions. RNA concentration was measured using a Nanodrop spectrophotometer (ND-8000).
RNA-seq
RNA from both ears of six wildtype and six sex-matched homozygote mutant littermates from each mutant line were used for RNA-seq. Samples were not pooled. Strand-specific libraries were prepared using the NuGEN Ovation Mouse RNA-Seq System 1-16 kit (NuGEN, cat. no. 0348) and sequenced on an Illumina HiSeq 2500 machine as paired-end 125bp reads. The resulting reads were quality checked using FastQC 0.11.4 [84] and trimmed with Trimmomatic 0.35 [85]; adapters were removed, trailing ends were clipped where the quality was low and a sliding window approach used to control for quality across the entire read. Finally, reads with 36 basepairs or fewer were discarded, because the shorter a read, the less likely it is to map uniquely to the genome. Reads were assembled to GRCm38 using Hisat2 version 2.0.2beta [86]. Bam files were soft-clipped beyond end-of-reference alignments and MAPQ scores set to 0 for unmapped reads using Picard 2.1.0 (Broad Institute, Cambridge, Mass. http://broadinstitute.github.io/picard) and checked for quality using QoRTS [87]. The QoRTS tool also generates count data (in the same format as HTSeq), and these were used with edgeR [88] to carry out a generalized linear model likelihood ratio test. Splicing analyses were performed using Cuffdiff (Cufflinks) [39], JunctionSeq [40] and Leafcutter [41].
Transcriptome and network analysis
Sylamer 18-131 [33] was used to examine genes ranked in order of their misregulation from up- to downregulated for over- and under-represented heptamers in their 3’ UTRs. Ingenuity Pathway Analysis (IPA) was used to generate potential upstream regulators for the affected genes, using the causal network analysis module. The WGCNA R package [89] was used to carry out weighted gene correlation network analysis. Gene counts from the QoRTS tool were prepared for WGCNA using DESeq2 [90] and transformed with a variance stabilising transformation. Batch effects were controlled for using the limma R package [91]. Module enrichment analysis was carried out using PANTHER v14 [47], and oPOSSUM [51] was used to assess transcription factor binding site overrepresentation in each module. Motif detection in a set of genes can be affected by differing GC composition in the genes used as a “background” set, so background gene sets of 4,000-5,000 genes were selected for each module such that the GC content matched that of the genes of interest. oPOSSUM assigns two scores to each transcription factor profile, the Z-score (which assesses whether the rate of occurrence of a given motif in the genes of interest differs significantly from the expected rate calculated from the background genes) and the Fisher score (which compares the proportion of genes of interest which contain a given motif to the proportion of the background genes containing that motif in order to determine the probability of a non-random association between the motif and the genes of interest) [51]. We chose to use a threshold of the mean + 1 standard deviation for each score.
RTPCR and qPCR
Organ of Corti RNA was normalised to the same concentration within each litter, then treated with DNAse 1 (Sigma, cat. no: AMPD1) before cDNA creation. cDNA was made using Superscript II Reverse Transcriptase (Invitrogen, cat. no: 11904-018) or M-MLV Reverse Transcriptase (Invitrogen, cat. no: 28025-013) or Precision Reverse Transcription Premix (PrimerDesign, cat. no: RT-premix2). MicroRNA cDNA was made using the miRCURY LNA RT Kit (QIAGEN, cat. no: 339340). Primers for sequencing cDNA for testing differential splicing were designed using Primer3 [92] (Supplementary Table S3). Sanger sequencing was carried out by Source Bioscience and analysed using Gap4 [93]. Quantitative RT-PCR was carried out on a CFX Connect qPCR machine (Bio-Rad), using probes from Applied Biosystems and QIAGEN (see Supplementary Table S3 for primer/probe details) and Sso-Advanced Master Mix (Bio-Rad, cat. no: 1725281) or the miRCURY LNA SYBR Green PCR Kit (QIAGEN, cat. no: 339345) for miRNA qPCR. Relative expression levels were calculated using the 2-ΔΔct equation [94], with Hprt as an internal control for all protein-coding genes except Ocm and Slc26a5, which are specifically expressed in hair cells, for which Jag1 was used as an internal control, because it is expressed in supporting cells of the organ of Corti [95, 96]. For all other genes and microRNAs, the quantity of sensory tissue present was checked using Jag1, and pairs were only used if their Jag1 levels did not differ by more than ±20%. For the microRNA qPCR, the internal control was Mir99a, which is expressed in most cell types in the cochlea [10]. At least three technical replicates of each sample were carried out for each reaction, and at least four biological replicates were tested per probe (see legends of Supplementary Figures S1, S11 for numbers for each probe).
Statistics
For qPCR data, the Wilcoxon rank sum test (Mann-Whitney U test) was chosen to determine significance, because it is a suitable test for small sample sizes and populations of unknown characteristics [97]. For the microRNA qPCR and synapse count data, we used a one-way ANOVA because three groups were being compared. Post-hoc test p-values were adjusted using the Bonferroni correction. For repeated ABR threshold analyses, the thresholds were not normally distributed, so the data were first transformed using the arcsine transformation then analysed using separate linear models for each frequency with a compound symmetric covariance structure and restricted Maximum Likelihood Estimation [98]. This allowed the inclusion of all available data, unlike the repeated measures ANOVA, which requires the discarding of a subject if any data points are missed (for example, if a mouse died before the final ABR measurement) [99]. For each stimulus the double interaction of genotype and age was measured, followed by Bonferroni correction for multiple testing. Wilcoxon rank sum tests were carried out using R, and the one way ANOVAs, arcsine transformation and mixed model linear pairwise comparison were done with SPSS v25.
Immunohistochemistry
Samples from P4 pups were collected, fixed in 10% formalin, embedded in paraffin wax and cut into 8μm sections. Immunohistochemistry was carried out using a Ventana Discovery machine and reagents according to the manufacturer’s instructions (DABMapTM Kit (cat.no 760-124), Hematoxylin (cat.no 760-2021), Bluing reagent (cat.no 760-2037), CC1 (cat.no 950-124), EZPrep (cat.no 950-100), LCS (cat.no 650-010), RiboWash (cat.no 760-105), Reaction Buffer (cat.no 95-300), and RiboCC (cat.no 760-107)). For each antibody, at least three wildtype/homozygote littermate pairs were tested, and from each animal, at least five mid-modiolar sections were used per antibody. Primary antibodies used were rabbit anti-Ocm (Abcam, cat. no: ab150947, diluted 1:50) and goat anti-Prestin (Slc26a5) (Santa Cruz, cat. no: sc-22692, diluted 1:50), and secondary antibodies were anti-goat (Jackson ImmunoResearch, cat.no 705-065-147, diluted 1:100), and anti-rabbit (Jackson ImmunoResearch, cat.no 711-065-152, diluted 1:100). Antibodies were diluted in staining solution (10% foetal calf serum, 0.1% Triton, 2% BSA and 0.5% sodium azide in PBS). A Zeiss Axioskop 2 microscope with a Plan Neofluar 63x 1.4NA objective was used to examine slides, and photos were taken using a Zeiss Axiocam camera and the associated Axiocam software. Images were processed in Adobe Photoshop; minimal adjustments were made, including rotation and resizing. Where image settings were altered, the adjustment was applied equally to wildtype and mutant photos and to the whole image.
Prediction of potential causal regulatory networks
In our previous analysis [16], we used interactions from the literature to connect miR-96 to as many of the misregulated genes in Mir96Dmdo homozygotes as possible. In order to automate this procedure, a custom perl script (prediction of potential causal regulatory networks, PoPCoRN) was written to make use of publically available regulatory data from ArrayExpress and the associated Expression Atlas [100, 101], ORegAnno [102], miRTarBase [103], TransmiR [104] and TRRUST [105] (Table 3). All these data are based on experimental evidence, although we did make use of human regulatory interactions by converting human gene IDs to mouse gene IDs where there was a one-to-one orthologue (using Ensembl). Interactions from our previous network [16], which were obtained from the literature using Ingenuity IPA, were also added, as were regulations reported in [106], which is not available through ArrayExpress but is a particularly relevant study, since it reports the results of microarrays carried out on RNA from mouse inner ears. MicroRNA targets confirmed in the literature were included, as were experimentally validated targets listed in miRTarBase as having “strong experimental evidence”, either reporter assay or Western blot [103], and genes predicted or confirmed to be miR-96 targets by our previous studies [3, 16]. Finally, genes upregulated in Mir183/96dko homozygotes with heptamers complementary to either the miR-96 or the miR-183 seed region in their 3’UTR were included as targets of the relevant microRNA(s) (Table 1, top). Similarly, genes upregulated in Mir182ko homozygotes with heptamers complementary to the miR-182 seed region in their 3’UTR were included as targets of miR-182 (Table 1, bottom). This resulted in a list of 97062 unique links of the form <gene A> <interaction> <gene B>.
All potential links between the ultimate regulators (Mir183 and Mir96, Mir96 alone, or Mir182) and the misregulated genes were then identified, and the direction of misregulation of intermediate genes predicted. Starting with the known misregulated genes, each upstream regulator was given a score based on the direction of regulation of the known misregulated genes (Supplementary Fig. S19). This process iterated until the gene(s) at the top of the cascade (in this case, Mir96, Mir183 or Mir182) were reached. Consistent links were kept, the shortest paths between microRNA and misregulated genes identified, and the final network was written out in the simple interaction format (sif) and viewed using Cytoscape [107] (Supplementary Fig. S18).
In order to test the PoPCoRN tool, we searched for studies which measured the misregulation of a set of genes in a system where a specific upstream regulator was either induced or silenced, and which also identified and confirmed the misregulation of an intermediate gene (where necessary, the data for these intermediate genes was removed from the input prior to network creation). We found ten suitable studies from which we were able to create networks and test the predicted misregulation of 14 intermediate genes. The tool made correct predictions for seven of the genes and incorrect predictions for three of them (Table 4). For the remaining four, it did not predict either up- or downregulation, which points to a deficiency of underlying data (Table 4). This will always be a problem for a tool based on existing data which does not attempt extrapolation.
Many similar approaches to network creation have been described previously (for example, [42, 108–110]), but they address the case where the upstream regulator or regulators remain unknown, for example when comparing samples from healthy and diseased tissues, and one of their main aims is to identify potential upstream regulators. Our implementation asks a different question, exploring the downstream regulatory cascade of a known regulator. In the case of miR-96, and potentially many other regulators involved in disease, the genes involved in mediating their effect are candidate therapeutic targets.
Strain differences in seed region matches
The C57BL/6NJ genomic sequence is available from the Mouse Genomes Project [32] via the Ensembl browser [121], but the Mir96Dmdo mice were made and maintained on the C3HeB/FeJ background, which is not one of the sequenced strains. Instead, we made use of genomic sequence from the C3H/HeJ strain, which is closely related to C3HeB/FeJ and is one of the other strains sequenced as part of the Mouse Genomes Project. We searched the 3’UTRs of each strain for the complement of the miR-96 seed region (GTGCCAA). We removed genes without an MGI ID, since without a consistent ID the corresponding gene in the other strain could not be identified. 1733 genes were left with at least one match to the miR-96 seed region in their 3’ UTRs in one or both strains. 21 genes had no 3’UTR sequence available for C57BL/6NJ, and 31 genes had no 3’UTR sequence available for C3H/HeJ.
Data and software availability
The datasets and computer code produced in this study are available in the following databases:
RNA-Seq data: ArrayExpress E-MTAB-5800 (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5800).
PoPCoRN network construction script: GitHub (https://github.com/moraglewis/PoPCoRN)
Disclosure of interest
The authors declare that they have no conflicts of interest.
Funding
This project was supported by Action on Hearing Loss (Pauline Ashley Fellowship to MAL), the MRC (KPS, MC_qA137918, G0300212) and the Wellcome Trust (098051, KPS: 100699). The Zeiss Imager Confocal Microscope is maintained with a Wellcome Trust grant (WT089622MA). No role was taken by any funding body in designing the study, in collection, analysis or interpretation of data, or in the writing of this manuscript.
Authors’ contributions
MAL and KPS conceived and designed the experiments. MAL, FDD and NJI performed the experiments. MAL, FDD, NJI and KPS analysed the data. MAL designed and wrote the PoPCoRN software. HMP contributed the Mir183/96dko and Mir182ko mice. MAL and KPS wrote the paper, and all authors reviewed it.
Acknowledgements
The RNA-Seq was carried out by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. We thank Matthew Arno and Sucharitha Balu for doing the RNA library preps and sample submission. The confocal microscopy was carried out with help from the Nikon Imaging Centre at Kings College London, and the scanning electron microscopy was done at the King’s College London Centre for Ultrastructural Imaging. We are grateful to Allan Bradley for access to the Mir183/96dko and Mir182ko mice. We thank Holly Smith for her work on the immunohistochemistry. We are very grateful to Dr Lawrence Moon for help and advice with the statistics.
Footnotes
Email addresses: FDD: francesca.di_domenico{at}kcl.ac.uk NJI: neil.ingham{at}kcl.ac.uk HMP: hmp{at}sanger.ac.uk KPS: karen.steel{at}kcl.ac.uk
Addition of Supplementary Data file containing all data underlying graphs presented as figures.
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