Echolocation call frequency variation in horseshoe bats: molecular basis revealed by comparative transcriptomics

Abstract

Recently diverged taxa with contrasting phenotypes offer opportunities for unravelling the genetic basis of phenotypic variation in nature. Horseshoe bats are a speciose group that exhibit a derived form of high-duty cycle echolocation in which the inner ear is finely tuned to echoes of the narrowband call frequency. Here, by focusing on three recently diverged subspecies of the intermediate horseshoe bat (Rhinolophus affinis) that display divergent echolocation call frequencies, we aim to identify candidate loci putatively involved in hearing frequency variation. We used de novo transcriptome sequencing of two mainland taxa (himalayanus and macrurus) and one island taxon (hainanus) to compare expression profiles of thousands of genes. By comparing taxa with divergent call frequencies (around 15 kHz difference), we identified 252 differentially expressed genes, of which six have been shown to be involved in hearing or deafness in human/mouse. To obtain further validation of these results, we applied quantitative reverse transcription–PCR to the candidate gene FBXL15 and found a broad association between the level of expression and call frequency across taxa. The genes identified here represent strong candidate loci associated with hearing frequency variation in bats.

1. Introduction

Understanding the genetic basis of phenotypic variation in wild populations is a central goal in evolutionary biology [1,2]. Clades of recently diverged taxa with contrasting traits offer promising systems in which to identify loci underpinning phenotypic variation. In such cases, these loci are expected to appear as outliers against an overall low background level of genetic divergence among genomes (reviewed in [3]). Aside from changes in coding genes, several studies have suggested that adaptive evolution might proceed more rapidly due to changes in gene expression (e.g. [4,5]). This implies that loci encoding adaptive phenotypic variation will show differential expression due to regulatory changes [6,7]. Compared with DNA sequence changes, gene expression differences may be particularly important for adaptive phenotypic variation under strong selection pressures over short timescales [8].

Comparative transcriptomics provides a powerful means of investigating divergence in expression of thousands of genes and has been applied to comparisons of different taxa (e.g. [9,10]). Transcriptomics-based approaches have been used to uncover candidate loci or pathways underlying the genetic basis of adaptations to a range of environmental conditions, such as thermal tolerance responses in Daphnia magna [11] and shifts to freshwater in Alosa pseudoharengus [12]. These and other studies have supported ideas that gene expression can underpin phenotypic variation (reviewed in [13]).

Here, we apply transcriptomics to identify candidate genes underpinning echolocation call frequency variation in bats. Although echolocation has been extensively studied since its discovery in the 1940s [14,15], its genetic basis remains poorly understood [16]. To date, most efforts to identify echolocation genes have examined coding sequence differences between echolocating and non-echolocating taxa, with a focus on genes implicated in audition or deafness in humans and other model organisms [17–19]. Results from these studies and, more recently, genome-wide screens have revealed that multiple hearing genes show molecular adaptation in lineages of echolocators, in some cases involving convergent amino acid replacements [20–24]. Despite insights from such comparative sequencing studies, little is known about the genetic basis of divergence in echolocation call frequencies among populations and closely related species of bats, including the role of gene expression difference (but see [25]).

Our study focuses on call frequency variation among closely related lineages of the intermediate horseshoe bat Rhinolophus affinis. Horseshoe bats emit calls and process their echoes to orient and hunt in low light conditions or complete darkness [26], and possess a derived form of sonar, termed ‘constant-frequency echolocation’, in which the cochlea is finely tuned to the frequency of the emitted call at rest [27]. As such, the echolocation call frequency in these bats reflects the peak sensitivity of their hearing. Horseshoe bats number over 100 species, across which call frequency is negatively correlated with body size [28–30]. In addition, there is considerable geographical variation in call frequency within species [31–33] and divergence in call frequency has been linked to reproductive isolation and speciation [34].

Horseshoe bats are characterized by enlarged cochleae for their body size [35], and several molecular evolution studies have reported positive selection in hearing genes horseshoe bats specifically (e.g. [17,18,23,36]). At the intra-specific level, Zhao et al. [25] were the first to study the genetic basis of variation in echolocation call frequencies in three geographical populations of the greater horseshoe bat (R. ferrumequinum) using comparative transcriptome sequencing. However, in this study, variation of call frequencies among the focal populations was slight (less than 8 kHz), potentially limiting the sensitivity of such an approach for distinguishing true candidates.

Here, we examine three R. affinis subspecies that were previously shown to have divergent echolocation call frequencies [37]. Specifically, himalayanus (87.12 ± 2.04 kHz) has a much higher call frequency than both hainanus (70.85 ± 0.94 kHz) and macrurus (73.68 ± 0.74 kHz). An advantage of our study system is that the three subspecies diverged very recently (later than 1 Ma), and their phylogenetic relationships and colonization histories have been resolved [37,38]. Previous studies indicated that hainanus formed when himalayanus colonized Hainan during a period of glaciation, and then hainanus recolonized the mainland to form macrurus [37–39]. A signature of recent population expansion has been detected in macrurus, along with a secondary contact between macrurus and the parental himalayanus [37,38], with evidence of recent introgressive hybridization [37,39]. Interestingly, despite exchanging genes along a hybrid zone, himalayanus and macrurus still maintain highly divergent call frequencies. Thus, the three focal subspecies provide a good system to identify candidate loci that encode the call frequency variation.

To test for taxon-specific differences in gene expression, we examined cochlea transcriptomes among the three focal subspecies. Because the magnitude in call frequency difference between himalayanus and each of the two other related subspecies (macrurus and hainanus) was comparable, we expected to find similar numbers of differentially expressed genes (DEGs) in both pairwise comparisons (himalayanus versus macrurus and himalayanus versus hainanus). We further predicted that the relatively smaller difference of call frequency between macrurus and hainanus would be reflected in a lower number of DEGs identified between them than in the above two comparisons. We further hypothesized that genes showing differential expression would have known roles in high-frequency hearing, and to this end we compared our set of genes to curated lists of known candidate hearing genes, in particular those that have been tested to be significantly associated with high-frequency hearing in mice.

2. Material and methods

(a). Sampling

Twelve male adults of R. affinis, four of each subspecies, were captured (figure 1a and table 1). Echolocation calls were recorded from handheld individual bats using Avisoft UltraSoundGate 116 Hnb kit (Avisoft, Berlin) and analysed using BatSound (Fast Fourier Transformation size 1024, Hanning window). For each bat, the constant frequency of the second harmonic was extracted. Bats were euthanized by cervical dislocation, and their cochleae were disconnected and immediately frozen in liquid nitrogen in the field, before transfer to a −80°C laboratory freezer.

Figure 1.

Sampling and phylogenetic tree reconstructed based on nuclear sequences. (a) Map showing the current distribution range of the three R. affinis subspecies in China, modified from [38]. Black dots indicate the sampling locations in this study. (b) ML tree based on 1815 concatenated nuclear orthologous genes. Numbers below the nodes indicate bootstrap values. Data of call frequency for each subspecies were cited from [39]. (Online version in colour.)

Table 1.

Detailed information of samples used in this study.

sample ID	taxon	call frequency (kHz)	sex	locality
Him-1	himalayanus	88.0	male	Anhui, China
Him-2	himalayanus	89.0	male	Anhui, China
Him-3	himalayanus	88.0	male	Anhui, China
Him-4	himalayanus	87.6	male	Anhui, China
Mac-1	macrurus	73.0	male	Guangdong, China
Mac-2	macrurus	73.2	male	Guangdong, China
Mac-3	macrurus	73.4	male	Guangdong, China
Mac-4	macrurus	73.1	male	Guangdong, China
Hai-1	hainanus	70.9	male	Hainan, China
Hai-2	hainanus	71.4	male	Hainan, China
Hai-3	hainanus	71.1	male	Hainan, China
Hai-4	hainanus	71.6	male	Hainan, China

(b). RNA extraction, sequencing and trimming

Total RNA extraction, cDNA library construction (average insert size = 300 bp) and sequencing on an Illumina HiSeq X Ten sequencer (150 bp paired-end) were conducted by Novogene and OE BioTech (electronic supplementary material, table S1). Raw reads were trimmed with TRIMMOMATIC [40] using a sliding window of 4 bp with a minimum average PHRED quality score of 20 and minimum reads length of 50 bp. Filtered reads were deposited to NCBI's Sequence Read Archive database (SRA) under accession numbers SRA12145327–12145338 (BioProject ID: PRJNA644044; electronic supplementary material, table S1).

(c). Reference transcriptome assembly and annotation

We performed de novo transcriptome assembly based on all trimmed reads using TRINITY [41] with default parameters. Redundant transcripts were removed using CD-HIT-EST [42] based on a 95% sequence similarity threshold. Transcripts with the longest open reading frames (ORFs) were kept using TRANSDECODER (http://transdecoder.github.io). Functional annotation was conducted using the TRINOTATE r20190821 pipeline (http://trinotate.github.io/) by searching against UniProtKB/Swiss-Prot and Pfam database (accessed 2 February 2019) using BLASTp and BLASTx [43] (E-value < 10⁻⁵). Gene ontology (GO) terms were retrieved for transcripts with a positive BLAST hit. Only transcripts functionally annotated were included in the final reference transcriptome. For comparison with our reference transcriptome above, we also generated three separate transcriptomes using sequencing reads from each of three subspecies with same procedures as described above.

(d). Differential expression analysis

Filtered reads from each sample were mapped to the reference transcriptome using BOWTIE 2 aligner [44] and gene-level abundance estimation across samples was conducted using RSEM [45]. The expected read count of each gene across all samples was combined into a matrix and normalized using TMM protocol [46]. Low expressed genes with a mean CPM (counts per million) across samples of less than 2 were removed from the reference transcriptome. Effects of batch were quantified and adjusted using the R package SVA [47]. Three surrogate variables were quantified by comparing a full model matrix (∼three focal subspecies) and a null model matrix (∼intercept term).

Prior to DE analysis, we performed a principal component analysis (PCA) in the R package (R Core Team 2015) based on the adjusted gene expression matrix to explore the similarity of expression patterns across samples. Then DE analyses were conducted at a gene level in pairwise comparisons of the three subspecies using DESeq2 [48] and edgeR [49]. Genes with significant results in both methods (FDR < 0.05 in edgeR and p_adj < 0.05 in DESeq2) and |log₂ (fold change)| > 1 were identified as DEGs in each comparison. To explore the grouping of individuals based on DE patterns [41], the TMM-normalized gene expression matrix was used to perform a hierarchical clustering analysis for those DEGs. To determine the biological function of these DEGs, we performed functional enrichment analysis on GO in the R/BIOCONDUCTOR package clusterProfiler [50] using all annotated reference genes as the background list (adjusted p < 0.05, [51]).

(e). Candidate gene approach

To further identify genes associated with hearing frequency variation, we used a candidate gene approach. Specifically, we compared our list of DEGs with a well-curated list of auditory genes that are known to be involved in deafness and/or the perception of sound in either human or mouse. We collected such 438 genes from MGI (Mouse Genome Informatics, accessed on 30 January in 2019) (electronic supplementary material, table S2). In addition, we also included 166 genes from the International Mouse Phenotyping Consortium (IMPC) which have been shown to be significantly associated with hearing frequency in auditory brainstem response (ABR) test under different frequency sound evoked (electronic supplementary material, table S3).

(f). Validation of differential gene expression using qPCR

To validate the relationship between expression and call frequency, we applied quantitative reverse transcription–PCR (qPCR) to one of the candidate genes FBXL15 because this gene has been shown to be significantly associated with high hearing frequency in the ABR test (IMPC). Expression was quantified in himalayanus and hainanus and, to test for a wider relationship between expression and call frequency variation, we also examined three other species for which call frequencies were known (R. pearsoni: 66.4 kHz; R. sinicus: 86.6 kHz; R. pusillus: 105.4 kHz; figure 3a). To determine whether our qPCR results might reflect phylogenetic patterns rather than differences in hearing, we reconstructed phylogenetic relationships among the five taxa included based on sequences of concatenated 13 mitochondrial protein-coding (MPC) genes using a maximum-likelihood (ML) tree implemented in RAxML [52] (−m GTRGAMMA -# 1000). Sequences of all 13 MPC genes for R. pearsoni, R. sinicus and R. pusillus were retrieved from NCBI GenBank. For himalayanus and hainanus, we obtained sequences of all 13 MPC genes by performing BLASTN (the E-value < 10⁻⁵) using assemblies of each subspecies (see below) as queries and corresponding sequences of R. sinicus as a reference. In this analysis, Hipposideros armiger were used as the outgroup (NCBI accession number: NC_018540.1).

Figure 3.

Results of qPCR test and phylogenetic reconstruction of taxa included in qPCR test. (a) Relative quantification of FBXL15 expression using qPCR in five taxa of horseshoe bats with divergent call frequencies. Five taxa are two R. affinis subspecies, R. a. himalayanus and R. a. hainanus, and three other horseshoe bats, R. pearsoni, R. sinicus and R. pusillus. (b) ML tree based on 13 concatenated mitochondrial protein-coding genes. Numbers above the nodes indicate bootstrap values.

Total RNA extraction from cochlear and cDNA preparation were performed following the same procedures described above. The software Primer-BLAST (NCBI) was used to design primers for FBXL15 (Forward: GTCAGGACATTGGCCGAGTA; Reverse: TCATAGTCAGGCCCCGTCA). The 18S rRNA gene was selected as a house-keeping gene for normalization, as described in [23]. qRT–PCR was performed on an ABI 7300 qPCR system using SYBR Green Real-time PCR Master Mix (TaKaRa Biotechnology) and PCR cycling was performed as follows: 95 °C, pre-denaturation for 2 min, 40 cycles of 95 °C for 5 s followed by 60 °C for 27 s, then one cycle of 95 °C for 15 s, followed by 60 °C for 1 min, 95 °C for 15 s and 60 °C for 15 s. At least three independent biological replicates, each with four technical replicates, were performed. A relative quantification method (2^−ΔΔCt) [53] was used to evaluate the expression variation. Significance of the relative expression difference in pairwise comparisons of the five taxa was assessed using non-parametric t-tests.

(g). Phylogenetic analysis

Three separate transcriptomes were assembled for each subspecies using the same procedures as above and the longest transcript of each gene was retrieved. Orthologous genes among the three subspecies were predicted using OrthoMCL [54] with all-versus-all BLASTP (E-value < 10⁻⁵). A final set of 5265 1-to-1 orthologues was shared by all three subspecies. Orthologous CDSs of each individual were obtained by performing BLASTN searches against 5265 orthologues using TRINITY assemblies of each individual as queries. MAFFT [55] and TRIMAL [56] were used to generate 2021 orthologues across all individuals. In this phylogenetic analysis, H. armiger was included as an outgroup. Using orthologues across all individuals as queries, orthologues from H. armiger were obtained by blasting against the protein datasets (genome assembly: ASM189008v1). All sequences were aligned using MAFFT and trimmed using TRIMAL, yielding 1815 CDSs (>300 bp) shared by R. affinis and H. armiger. Phylogenetic relationships among the three focal taxa were reconstructed based on concatenated 1815 CDSs using an ML tree with RAxML [52] (-m GTRGAMMA -# 1000).

3. Results

(a). Reference transcriptome assembly and annotation

A total of 188 Gb filtered reads were obtained from 12 individuals with an average of 23.9 million reads per individual (see details in electronic supplementary material, table S1). All filtered reads were combined into a single dataset and used to de novo assemble a reference transcriptome. After filtering using functional annotation, the final reference transcriptome contained 14 112 genes and 18 722 transcripts, of which 18 286 transcripts (97.67%) were assigned with GO terms. We further assessed the quality of the reference transcriptome by searching against single-copy orthologues (4104 genes shared by 50 mammal species; http://busco.ezlab.org) using BUSCO [57]. The results showed that 3527 (85.9%) complete or partial BUSCOs were included in the current reference transcriptome which was used in the following differential expression analysis. For comparison, we also generated a single transcriptome for each of three subspecies, based on pooling reads from their respective libraries. We found that our reference transcriptome was more complete than the three individual subspecies’ transcriptomes in terms of both the number of annotated transcripts (or genes) and the total proportion of BUSCO genes (see electronic supplementary material, table S4). In addition, our final reference transcriptome included over 90% transcripts found in each of the three subspecies transcriptomes (see electronic supplementary material, table S4).

(b). Phylogenetic analysis

We performed phylogenetic tree reconstruction based on 1815 nuclear orthologous genes using an ML analysis of concatenated datasets. In the ML tree, macrurus and hainanus were first classified together with 100% support rate to the exclusion of himalayanus (figure 1b).

(c). Differential expression analysis

Samples from each subspecies were clustered together in the PCA plots based on SVA-adjusted expression matrix (electronic supplementary material, figure S1). Our Shapiro–Wilk (SW) tests confirmed that the expression matrix data from pairwise comparisons among samples were normally distributed (all SW tests with p-value > 0.05). We performed Student's paired t-tests and found no significant differences among any of the pairwise comparisons (all p > 0.05) except for that of himalayanus versus hainanus (p = 0.014), implying overall consistent expression patterns. Two comparisons of bats with divergent call frequencies (himalayanus versus hainanus, comparison D1; himalayanus versus macrurus, D2) showed a similar number of DEGs (electronic supplementary material, tables S5 and S6), whereas a third comparison with similar call frequencies (hainanus versus macrurus) showed much fewer DEGs (electronic supplementary material, table S7) than the above two comparisons (figure 2a).

Figure 2.

Differential expression analysis. (a) Bar plots showing the number of DEGs identified in each of the three comparisons (himalayanus versus hainanus; himalayanus versus macrurus; hainanus versus macrurus). (b) Venn diagram showing the number of DEGs shared by comparisons of himalayanus versus hainanus and himalayanus versus macrurus. (c) Heat map showing expression patterns of 252 DEGs in all 12 individuals of the three subspecies. Expression was defined using TMM-normalized values with yellow and blue representing comparatively lower and higher expression levels, respectively. (Online version in colour.)

To reduce the false positives of identifying candidate genes associated with call/hearing frequency variation, we only considered those DEGs identified in both comparisons of bats with divergent call frequencies (D1 and D2). A total of 252 such DEGs were found (figure 2b; details in electronic supplementary material, table S8) with 157 upregulated and 95 downregulated in himalayanus relative to the other two subspecies (macrurus and hainanus). A hierarchical clustering analysis of these 252 DEGs revealed that individuals from both low call frequency taxa (macrurus and hainanus) were mixed and formed a single cluster, with himalayanus forming a second cluster characterized by a majority of upregulated loci relative to former group (figure 2c). Functional enrichment analyses revealed no significant GO terms on DEGs upregulated in himalayanus using FDR < 0.05. Six significant GO terms were found on downregulated DEGs and they are mainly related to translation and oxidoreductase activity (electronic supplementary material, table S9).

(d). Candidate gene approach

We further compared our 252 DEGs putatively associated with hearing frequency variation with curated list of auditory genes known to be involved in human/mouse deafness. Six of these auditory loci were among our set of DEGs, with four showing upregulation (FBXL15, GATA2, CLDN11 and CKB) and two downregulation (CCDC88C and SLC1A3) in himalayanus. Of these overlapping genes, FBXL15 has been shown to be associated with hearing at relatively high frequencies (above 24 kHz), as revealed by the ABR test (IMPC) (electronic supplementary material, table S3). GATA2 and CCDC88C have also been positive in ABR tests, but the former affects low frequency hearing (below 24 kHz) and the latter affects both high and low hearing frequencies (electronic supplementary material, table S3). In addition, these two genes are also significantly associated with other phenotypes (e.g. reproductive system and body size). CKB and SLC1A3 are not significantly associated with hearing in ABR tests, while CLDN11 has not been tested.

(e). Validation using qPCR

Our qPCR tests confirmed the RNA-seq result for FBXL15 with significant expression difference between himalayanus and hainanus (figure 3a). The fold change of expression in qPCR test was similar to that recorded from the RNA-seq. We further compared relative mRNA expression patterns in cochlear of R. affinis (himalayanus and hainanus) and three other horseshoe bats with different call frequencies (R. pearsoni, R. sinicus and R. pusillus) (figure 3a). We found similar levels of FBXL15 expression between taxa showing close call frequencies, such as between R. pearsoni and hainanus, and between R. sinicus and himalayanus (figure 3a). In addition, taxa showing relative high call frequencies (R. sinicus and himalayanus) exhibit significantly higher level of FBXL15 expression than taxa with comparatively low call frequencies (R. pearsoni and hainanus). However, R. pusillus was an exception to this trend, with the highest call frequency but only an intermediate level of FBXL15 expression (figure 3a). Phylogenetic reconstructions among the five taxa revealed that R. pusillus and two R. affinis subspecies formed a monophyletic group, with R. sinicus sister to this group, and R. pearsoni basal with respect to these taxa (figure 3b).

4. Discussion

In this study, by focusing on three recently diverged subspecies of R. affinis that have evolved divergent echolocation call frequencies (around 15 kHz difference), we aimed to investigate whether divergence in hearing frequency is associated with patterns of differential gene expression in the cochlea. We performed comparative transcriptomics analyses and found similarly high numbers of DEGs in pairwise comparisons of taxa characterized by substantial call frequency divergence (himalayanus versus macrurus and himalayanus versus hainanus), but a lower number of DEGs when comparing taxa with more similar call frequencies (macrurus versus hainanus). Thus, the magnitude of phenotypic differences within species or between closely related species may be correlated to the degree of gene expression changes in specialized tissues (see also [58]). While we are unable to fully rule out the possibility that DEGs among taxa are related to other phenotypic differences, our focus on the cochlea—a highly specialized auditory organ—means that this is unlikely.

We identified 252 DEGs shared in two comparisons taxa with divergent call frequencies (himalayanus versus hainanus and himalayanus versus macrurus). Functional enrichment analysis on these DEGs did not reveal significant GO terms associated with inner ear development, hearing or deafness. However, six DEGs were contained in a curated list of genes with known involvement in hearing or deafness in human/mouse. Below, we discuss the possible roles of these six candidate DEGs in hearing frequency.

FBXL15 has been shown to be significantly associated with high-frequency hearing above 24 kHz (IMPC) and our qPCR test confirmed the expression differences in this gene revealed by the RNA-seq analysis. In addition, we further assessed its expression pattern in three other horseshoe bats and found that the level of FBXL15 expression broadly increased with echolocation call frequency (figure 3a). Specifically, we found that pairs of taxa with similar inferred hearing frequencies (such as himalayanus and R. sinicus, and hainanus and R. pearsoni) showed correspondingly similar levels of FBXL15 expression. Moreover, taxa with higher hearing frequencies (himalayanus and R. sinicus) showed significantly higher levels of FBXL15 expression than taxa with lower hearing frequencies (hainanus and R. pearsoni). Since himalayanus and hainanus are subspecies, and R. affinis shows a closer phylogenetic relationship with R. sinicus than with R. pearsoni [59], these results cannot be attributed to phylogenetic effects. Despite this, however, R. pusillus did not fit this general pattern, exhibiting the highest hearing frequency among the five taxa but only an intermediate level of FBXL15 expression (figure 3a). Once again, this result for R. pusillus appears not to stem from phylogenetic effects, given that R. pusillus is not more closely related to R. pearsoni than to the other taxa (figure 3b). Sampling of additional taxa would therefore be needed to confirm whether or not R. pusillus is an outlier more broadly. This gene is best known for its crucial role in bone morphogenetic protein (BMP) signalling pathway [60], and thus our result expands our knowledge of FBXL15 function, pointing to a possible role in high hearing frequency in nature.

Another candidate showing differential expression, GATA2 is mainly expressed in vestibular and cochlear non-sensory epithelia [61] and is essential for the development of semicircular ducts and the surrounding perilymphatic space [62]. Although this gene has been shown to be significant in the ABR test, it appears to relate to low hearing frequency (below 24 kHz), as is also the same case for CCDC88C, which encodes a negative regulator in the Wnt signalling pathway [63]. CKB, creatine kinase brain isoform, is mainly expressed in the organ of Corti supporting cells and the lateral wall tissues [64]. While not reported to be significant in the ABR test, CKB plays an essential role in replenishing ATP consumed during the process of hearing in cochlea [64], which may explain why it shows the highest expression level among all 252 DEGs identified here (average log₂CPM of 11.28). A further DEG, SLC1A3, encodes the glutamate-aspartate transporter GLAST and is mainly expressed in the supporting cells around the inner hair cells. The major function of GLAST is to remove glutamate and this protein plays a protective role in noise-induced hearing loss [65,66]. It is notable that this gene is downregulated in himalayanus relative to the other two taxa (hainanus and macrurus). We propose that the upregulation of SLC1A3 in the taxa with lower hearing frequency (hainanus and macrurus) may help to protect against glutamate excitoxicity because the upregulation of this gene has been linked to age and hearing loss in mice [67]. Similar to CKB, this gene also shows an extremely high expression level with an average log₂CPM of 9.24, supporting its important role in the process of hearing in the cochlea, although SLC1A3 was also not significantly associated with hearing frequencies as measured in the ABR test.

The final candidate auditory gene in our set of DEGs was CLDN11, which showed an average log₂CPM of 7.1, and an expression level that was more than four times higher in himalayanus than the other two taxa. To our knowledge, this gene has not been examined using the ABR test, but is nevertheless implicated in deafness gene in mice [68]. Claudin-11, encoded by CLDN11, plays an important role in the generation of the endocochlear potential, which may dramatically increase hearing sensitivity and is essential for the evolution of high hearing frequency in mammals [69,70]. A loss of amplifier gain has been detected in Claudin-11-null mice, although the function of inner hair cells in these mutants is normal, suggesting an important role of Claudin-11 in cochlear amplification [68]. A similar loss of amplifier gain has also been found in the absence of the outer hair cell motor protein, prestin [71]. Interestingly, prestin has been shown to have undergone convergent evolution between lineages of echolocating bats [18] and between bats and toothed wales [19], although we found no expression difference in prestin between taxa with different hearing frequencies in this study.

(a). Comparisons with previous studies

To date, only one other study has examined gene expression in the context of echolocation call frequency variation at the intra-specific level [25]. Here, authors of [25] focused on three geographical populations of the greater horseshoe bat (R. ferrumequinum) that differed in call frequency by less than 8 kHz. Interestingly, this previous study identified much larger numbers of DEGs between pairwise comparisons of the three populations (1611, 4337 and 7039 DEGs, corresponding to call frequency differences of 2.5 kHz, 4.8 kHz and 7.3 kHz, respectively) compared to the numbers found here (39, 750 and 799 DEGs corresponding to call frequency differences of 2.8 kHz, 13.4 kHz and 16.3 kHz, respectively). The marked contrast between our results and those of [25] may in part reflect differences in the analytical pipelines used. Indeed, while we identified DEGs using both DESeq2 and edgeR, authors of [25] used only edgeR. To assess whether this is likely to account for the discrepancy, we examined our results from edgeR only, however, found that this did not markedly alter the number of DEGs detected (data not shown). A further possible explanation is that the three populations of R. ferrumequinum are more divergent than the focal subspecies of R. affinis, and thus greater levels of differential expression in the former reflects genetic drift [72–74]. Consistent with this possibility, FBXL15 was found to be positively selected in one of the three populations of R. ferrumequinum [25], while no amino acid changes have been detected in all three subspecies of R. affinis examined here, in the Dryad digital Repository: https://doi.org/10.5061/drad.z34tmpg99 [75]. We also screened the other candidate genes (GATA2, CLDN11, CKB, CCDC88C and SLC1A3) for amino acid differences, either among the three R. affinis subspecies or the three R. ferrumequinum geographical populations [75]. Again, no amino acid changes were found at these genes among the three R. affinis subspecies, whereas one amino acid changes were detected at GATA2 among the three R. ferrumequinum geographical populations.

Finally, we compared our DEGs identified at the intra-specific level to other sets of putative echolocation genes reported in previous studies. We found no overlap with the 18 genes found to show differential expression between the cochleae of echolocating and non-echolocating bats [20], perhaps suggesting that different loci may underlie high hearing frequency and the extent of hearing frequency variation. On the other hand, we found that five of our DEGs were also among the set of 426 DEGs shown to be consistently upregulated in the cochleae of constant frequency (also termed high-duty cycle) bats relative to bats that exhibit either frequency-modulated (FM) echolocation or tongue-clicking [23]. Moreover, these five (ALDH1A2, ETV4, KCNAB1, SLC6A20 and TCEAL4) were found to be differentially expressed in our comparisons of subspecies with divergent calls, representing nearly 2% of our total set of DEGs. Unfortunately, however, none of these overlapping genes are known to be involved in hearing, and nothing is known about their roles in constant-frequency echolocation.

(b). Implications of this study

Our previous studies on the three R. affinis subspecies revealed that the two mainland subspecies (himalayanus and macrurus) may form a hybrid zone in the eastern region with extensive introgression of mtDNA but with little introgression of nuclear DNA [39,76]. Despite exchanging genes along the hybrid zone, himalayanus and macrurus still maintain highly divergent call frequencies, suggesting that any loci that encode the call frequency variation might show resistance to introgression across taxonomic boundaries. In horseshoe bats, echolocation call frequency divergence may lead to assortative mating and even reproductive isolation [34]. It is thus plausible that the six candidate loci whose expression differences are associated with call frequency variation may contribute to reproductive isolation between the mainland subspecies. Although divergence of gene expression between taxa has been suggested to play an important role in ecological speciation [77] and hybrid dysfunction [78], horseshoe bats are slowly reproducing and highly sensitive, and thus they are not suitable for breeding experiments.

(c). Future work

Our comparative transcriptomics approach successfully identified six candidate auditory genes that may play important roles in determining and/or maintaining call frequency differences among recently diverged subspecies of R. affinis in China. Moreover, qPCR conducted on one of these genes, FBXL15, further supported to link between expression and call frequency across horseshoe bats. Mammals show a wide range of hearing frequency sensitivities from 10 kHz to beyond 100 kHz [79,80], while geographical variation in acoustic signals is also widely seen across diverse vertebrate groups (fishes [81]; frogs [82]; birds [83]). Further taxonomic sampling is thus needed to establish whether the level of expression of the loci identified in this study is more widely associated with hearing frequency variation, both among bat species and across mammals. Future comparisons of gene expression across taxa and populations will also benefit from the ongoing development of single-cell transcriptomics approaches, which allow the targeting of specific cell types [84]. This is likely to be very important in studies of the cochlea, which contain multiple cell types, only some of which will be pertinent to hearing frequency [85].

Ethics

All our sampling procedures were conducted under the guidelines of Regulations for the Administration of Laboratory Animals and approved by the National Animal Research Authority of East China Normal University (approval ID: bf20190301).

Data accessibility

Filtered reads were deposited to NCBI's Sequence Read Archive database (SRA) under accession numbers SRA12145327–12145338 (BioProject ID: PRJNA644044). Assembly of the reference transcriptome and alignments of candidate genes are available from the Dryad Digital Repository: https://doi.org/10.5061/drad.z34tmpg99 [75].

Authors' contributions

H.S., S.J.R. and X.M. conceived the project; H.S. and J.W. analysed the data; W.C. conducted the qPCR experiment; H.S., S.J.R. and X.M. wrote the draft of the manuscript; S.J.R. and X.M. edited the manuscript; X.M. provided the grant for this project.

Competing interests

We declare we have no competing interests.

Funding

This work was supported by the National Natural Science Foundation of China (grant no. 31570378) awarded to X.M. S.R. was supported by a European Research Council Starting Grant 310482 (EVOGENO).