ORIGINAL RESEARCH article

Front. Neurosci., 12 February 2024

Sec. Auditory Cognitive Neuroscience

Volume 18 - 2024 | https://doi.org/10.3389/fnins.2024.1340854

Transcriptomic analysis reveals prolonged neurodegeneration in the hippocampus of adult C57BL/6N mouse deafened by noise

  • 1. Department of Otorhinolaryngology, Seoul National University College of Medicine, Seoul, Republic of Korea

  • 2. Department of Otorhinolaryngology, Boramae Medical Center, Seoul Metropolitan Government-Seoul National University, Seoul, Republic of Korea

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Abstract

Introduction:

Several studies have reported a significant correlation between noise-induced hearing loss and cognitive decline. However, comprehensive analyses of this relationship are rare. This study aimed to assess the influence of hearing impairment on cognitive functions by analyzing organ samples in the afferent auditory pathway of deafened mice using mRNA sequencing.

Methods:

We prepared 10 female 12-week-old C57BL/6N mice as the experimental and control groups in equal numbers. Mice in the experimental group were deafened with 120 dB sound pressure level (SPL) wideband noise for 2 h. Cochlea, auditory cortex, and hippocampus were obtained from all mice. After constructing cDNA libraries for the extracted RNA from the samples, we performed next-generation sequencing. Subsequently, we analyzed the results using gene ontologies (GOs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases for differentially expressed genes (DEGs) of each organ.

Results:

Our results revealed 102, 89, and 176 DEGs for cochlea, auditory cortex, and hippocampus, respectively. We identified 294, 203, and 211 GOs; 10, 7, and 17 KEGG pathways in the cochlea, auditory cortex, and hippocampus, respectively. In the long term (12 weeks) from noise-induced hearing loss, GOs and KEGG pathways related to apoptosis or inflammation persisted more actively in the order of hippocampus, auditory cortex, and cochlea.

Discussion:

This implies that the neurodegenerative effects of noise exposure persist more longer time in the central regions.

Introduction

Dementia is one of the biggest global healthcare problems, affecting 55 million people worldwide (Gauthier et al., 2022). Since there is no effective disease-modifying treatment option for dementia progression (Tisher and Salardini, 2019), the prevention of early-stage cognitive decline seems more important. Notably, it has been reported that hearing loss is associated with dementia (Lin et al., 2011; Gurgel et al., 2014). As relevant studies progressed, hearing loss has been accepted as a major modifiable risk factor for dementia (Livingston et al., 2017). It is thought that hearing loss can lead to cognitive decline, which may then serve as a “second hit,” exacerbating dementia in the presence of organic brain pathology (Lin and Albert, 2014).

Several independent studies have suggested underlying mechanisms by which hearing impairment leads to cognitive decline. Some studies suggest that the increased cognitive demands resulting from hearing loss can evoke cognitive decline (Lin and Albert, 2014; Cardin, 2016). Other researchers suggest that the oxidative stress triggered by hearing loss may adversely affect the hippocampus by increasing neuroinflammation, accelerating apoptosis, and reducing neurogenesis (Gonzalez-Perez et al., 2011; Paciello et al., 2023). Abnormalities in the neurotransmitter system, such as the N-methyl-d-aspartic acid receptor 2B, have been reported to be related to cognitive decline (Cui et al., 2013). Disorders of Schaffer collateral-1 long-term potentiation through depressed levels of brain-derived neurotrophic factors due to high-intensity sound are known to cause cognitive decline (de Deus et al., 2021). Additionally, it has been proposed that stress hormones and corticosteroids produced in response to hearing loss-induced stress can impact neuronal function in the hippocampus, ultimately leading to cognitive decline (Jin et al., 2017; Kurioka et al., 2021).

These studies provide an overview of how hearing impairment affects cognitive function. However, it is difficult to draw a comprehensive explanation because each study reveals only discrete aspects of the phenomenon from the researcher's viewpoint. In this case, omics studies can provide a broader understanding. An omics study quantitatively analyzes the whole set of specific biomolecules, such as the genome, transcriptome, proteome, or metabolome, in a given time (Vailati-Riboni et al., 2017; Subedi et al., 2022). This enables us to obtain a balanced insight into complex genetic mechanisms. Gene expression patterns can be primarily understood by mRNA sequencing.

Recent studies using omics technologies have uncovered the hidden mechanisms of hearing loss progresses at the molecular level. A previous study showed that noise exposure immediately evokes global cochlear protein ubiquitylation and upregulates ribosomal proteins in the cochlea (Jongkamonwiwat et al., 2020). Another study found that acoustic trauma modulates the expression of inflammation- and immunity-related genes (Maeda et al., 2021; Miao et al., 2021). However, few omics studies have investigated the relationship between hearing loss and cognitive decline. A transcriptome-wide association study has shown that age-related hearing loss impairs the glutamatergic synapse pathway in the hippocampus of BXD-recombinant inbred mice (Deng et al., 2021). Except this, we could not find out any omics study on the relationship between hearing loss and cognitive decline.

From this perspective, the current study was designed to evaluate the impact of auditory impairment on cognitive functioning. This was achieved by analyzing tissue samples from key components of the afferent auditory pathway—specifically the cochlea, the auditory cortex, and the hippocampus—through mRNA sequencing techniques. The auditory cortex was selected for its critical role in the processing and interpretation of auditory signals. The hippocampus was included due to its integral function in spatial and episodic memory; noteworthily, hippocampal impairment has been identified as a hallmark feature in a range of cognitive disorders, including dementia (Nadhimi and Llano, 2021; Billig et al., 2022). Furthermore, we used gene ontologies (GOs) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases to examine the types of events that occur after noise-induced hearing loss.

Materials and methods

Animal management

Ten female C57BL/6N mice were used. To prevent sex act as a confounding factor, we used only female mice. Mice were placed in cage with freely access to distilled water and chow under pathogen free condition (12 h light/dark cycle, 23°C, 50% humidity). They were divided into experimental (n = 5) and control (n = 5) groups. Mice in the experimental group were deafened with a single exposure of 120 dB sound pressure level (SPL) wideband noise for 2 h in an audiometric booth at the age of 12 weeks. To confirm the audiometric state, we checked the hearing thresholds of both ears just before noise exposure, 2 weeks post-exposure, and at the age of 24 weeks just before sacrifice.

All experimental procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of Boramae Hospital (IACUC number 2022-0133).

Tissue preparation

All the mice were sacrificed at 24 weeks of age. They were fully anesthetized by isoflurane inhalation in an airtight cage. Cardiac perfusion was performed using phosphate-buffered saline (PBS) to remove red blood cells from the tissues. After decapitation, we harvested the bilateral cochleae, auditory cortical areas, and hippocampus of each mouse by referencing an anatomy atlas (Franklin and Paxinos, 2013). All right and left specimens were combined into one sample for each mouse organ.

mRNA sequencing

The samples were lysed using the RNeasy Mini Kit (Qiagen, Germany) following the manufacturer's instructions for total RNA extraction. Total RNA quantity and quality were determined by UV/Vis spectrophotometer (NanoDrop 2000, ThermoFisher Scientific, USA). All RNA samples showed suitable A260/A280 and 28s/18s ratios. A cDNA library was subsequently constructed, and next-generation sequencing was performed. The entire NGS procedure was performed using an Illumina NovaSeq 6000 system (Macrogen, South Korea).

Statistical analysis on expressed genes

To reduce bias in the results, we conducted quality control analysis of the raw sequencing reads. This involved removing low-quality data, adaptor sequences, contaminant DNA, PCR duplicates, and other artifacts. After confirming all samples satisfied quality control criteria, we selected three sets out of five sets in analyzing samples of cochlea, auditory cortex, and hippocampus for experimental and control groups.

The preprocessed reads were then mapped to the reference genome using HISAT2 version 2.1.0 (https://ccb.jhu.edu/software/hisat2/index.shtml/) to generate aligned reads. Subsequently, transcript assembly was performed using StringTie version 2.1.3b (https://ccb.jhu.edu/software/stringtie/). Expression profiles were extracted through transcript quantification for each sample, and fragments per kilobase of transcript per million mapped reads (FPKM), reads per kilobase of transcript per million mapped reads (RPKM), and transcripts per kilobase million (TPM) values were calculated. Differentially expressed genes (DEGs) were selected using this process, and functional annotation and gene-set enrichment analyses were performed using the GOs and KEGG databases. We set |fold change| of ≥2.0 and p-value of < 0.05 as statistically significant. For the selection of differentially expressed genes (DEGs), we utilized the raw p-values obtained from the direct gene comparisons between control and experimental groups. In contrast, for the identification of GOs and KEGG pathways, we employed adjusted p-values derived from hypergeometric testing and multiple testing corrections to control the false discovery rate. The sample size was determined to ensure sufficient quantity, taking into account cell type, tissue specificity, and library preparation methods. Since all samples met the quality control standards, we required only three samples per group.

We used GOnet (https://tools.dice-database.org/GOnet/) and REVIGO (http://revigo.irb.hr/) to visualize the relationships among genes and their ontologies. In the GOnet, we performed “GO term annotation” analysis using “generic GO slim” subset. In the REVIGO, we set resulting list size as “medium,” and chose “SimRel” semantic similarity measure.

Results

Audiometric tests

Prior to noise exposure, all the mice exhibited normal hearing thresholds in both ears during the auditory brainstem response (ABR) tests using click, 8 and 16 kHz tone-burst sound. Two weeks after noise exposure, it was found that none of the mice in the experimental group had an ABR response to sound stimulations of 90 dB SPL in either ear. In contrast, all the mice in the control group maintained normal thresholds in both ears. The hearing status of all mice was assessed prior to sacrifice, and it was confirmed that there had been no changes since the last hearing test.

Data quality control

We excluded genes that were not detected in at least one of the 18 samples. Of the 45,777 genes detected, 27,122 were not detected in at least one sample and were excluded. Thus, 18,655 genes were analyzed in this study.

To reduce systemic bias in sample comparisons, we performed relative log-expression normalization prior to statistical analysis. The necessary size factor was estimated using the read count data. Additionally, we performed multidimensional scaling and hierarchical clustering analyses to check for the presence of outlier samples and similarity in expression patterns among biological replicates and confirmed that there were no discrepancies.

Differentially expressed genes

To compare the experimental and control groups, we used the up-regulated and down-regulated genes that were differentially expressed in each organ. In the cochlea, there were 57 up-regulated and 45 down-regulated DEGs, while in the auditory cortex, there were 62 up-regulated and 27 down-regulated DEGs. In the hippocampus, 141 DEGS were up-regulated, and 35 were down-regulated (Figure 1).

Figure 1

Figure 1

Number of DEGs for each organ. The numbers are calculated for the genes whose fold changes are >2 and raw p-values are within 0.05.

However, these results included not only protein-coding genes but also pseudogenes, lncRNAs, snoRNAs, and miRNAs. We excluded the non-protein-coding genes to determine the functions of the RNAs. When only the protein-coding genes were considered, the number of DEGs reduced. In the cochlea, there were 16 up-regulated and 32 down-regulated protein-coding DEGs (Table 1), while in the auditory cortex, there were 31 up-regulated and 21 down-regulated protein-coding DEGs (Table 2). In the hippocampus, 99 up-regulated and 15 down-regulated protein-coding DEGs were expressed (Table 3). In total, there are 146 up-regulated protein-coding DEGs and 68 down-regulated protein-coding DEGs (Figure 2). This transcriptomic data can be accessed in SRA database of NCBI site (reference number: PRJNA1061000).

Table 1

Gene ID Gene symbol Description Fold change (deaf/control) Raw p-value (deaf/control)
a. Up-regulated genes
104362 Meig1 Meiosis expressed gene 1 2.72451 0.00115
15564 Htr5b 5-hydroxytryptamine (serotonin) receptor 5B 2.55739 0.00696
217698 Acot5 Acyl-CoA thioesterase 5 2.53157 0.00317
100034363 Tmsb15b2 Thymosin beta 15b2 2.48637 0.00089
238680 Cntnap3 Contactin associated protein-like 3 2.47875 0.00331
20716 Serpina3n Serine (or cysteine) peptidase inhibitor, clade A, member 3N 2.45358 0.00000
67483 1700028P14Rik RIKEN cDNA 1700028P14 gene 2.42483 0.01373
102633301 Gm31160 Predicted gene, 31160, transcript variant X5 2.38870 0.00004
277353 Tcfl5 Transcription factor-like 5 (basic helix-loop-helix) 2.32988 0.00345
231727 B3gnt4 UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 4 2.30858 0.00169
192164 Pcdha12 Protocadherin alpha 12 2.27350 0.00193
74369 Mei1 Meiotic double-stranded break formation protein 1 2.20177 0.00046
75040 Efcab10 EF-hand calcium binding domain 10 2.17077 0.00534
14038 Wfdc18 WAP four-disulfide core domain 18 2.13102 0.01006
21331 T2 Brachyury 2 2.12875 0.00004
74230 1700016K19Rik RIKEN cDNA 1700016K19 gene 2.09831 0.00397
b. Down-regulated genes
57814 Kcne4 Potassium voltage-gated channel, Isk-related subfamily, gene 4 −2.02804 0.01304
215418 Csrnp1 Cysteine-serine-rich nuclear protein 1 −2.04434 0.00124
15936 Ier2 Immediate early response 2 −2.05776 0.00506
57738 Slc15a2 Solute carrier family 15 (H+/peptide transporter), member 2 −2.06791 0.00000
115488282 LOC115488282 Uncharacterized LOC115488282, transcript variant X1 −2.08478 0.00443
74155 Errfi1 ERBB receptor feedback inhibitor 1 −2.10587 0.00413
13537 Dusp2 Dual specificity phosphatase 2 −2.12057 0.00014
320145 Sp8 Trans-acting transcription factor 8 −2.12946 0.02599
16364 Irf4 Interferon regulatory factor 4 −2.13488 0.00023
12014 Bach2 BTB and CNC homology, basic leucine zipper transcription factor 2 −2.14126 0.00001
381823 Apold1 Apolipoprotein L domain containing 1 −2.16870 0.03508
21334 Tac2 Tachykinin 2 −2.17265 0.00348
19683 Rdh16 Retinol dehydrogenase 16 −2.19381 0.01938
12702 Socs3 Suppressor of cytokine signaling 3 −2.24148 0.00516
100037278 Fam129c Family with sequence similarity 129, member C −2.27732 0.00000
17691 Sik1 Salt inducible kinase 1 −2.29470 0.00112
22695 Zfp36 Zinc finger protein 36 −2.44989 0.00618
12227 Btg2 B cell translocation gene 2, anti-proliferative −2.45579 0.00008
637079 Iqcn IQ motif containing N −2.46003 0.01062
18227 Nr4a2 Nuclear receptor subfamily 4, group A, member 2 −2.52660 0.00069
83885 Slc25a2 Solute carrier family 25 (mitochondrial carrier, ornithine transporter) member 2 −2.74525 0.03672
19252 Dusp1 Dual specificity phosphatase 1 −2.80397 0.00000
16007 Ccn1 Cellular communication network factor 1 −2.86252 0.01801
380728 Kcnh4 Potassium voltage-gated channel, subfamily H (eag-related), member 4 −2.86412 0.00003
19373 Rag1 Recombination activating 1 −2.97390 0.00000
16477 Junb Jun B proto-oncogene −3.01155 0.00040
83379 Klb Klotho beta −3.17551 0.01020
19225 Ptgs2 Prostaglandin-endoperoxide synthase 2 −3.25476 0.00418
11910 Atf3 Activating transcription factor 3 −3.60211 0.00123
13653 Egr1 Early growth response 1 −4.08707 0.00151
15370 Nr4a1 Nuclear receptor subfamily 4, group A, member 1 −5.27951 0.00002
14281 Fos FBJ osteosarcoma oncogene −8.35415 0.00000

Differentially expressed genes in the cochlea.

Table 2

Gene ID Gene symbol Description Fold change (deaf/control) Raw p-value (deaf/control)
a. Up-regulated genes
14559 Gdf1 Growth differentiation factor 1 16.52491 0.00010
11540 Adora2a Adenosine A2a receptor 3.56210 0.00062
13489 Drd2 Dopamine receptor D2 3.51378 0.00426
66722 Spag16 Sperm associated antigen 16 3.07310 0.01554
73712 Dmkn Dermokine 2.73336 0.00029
19144 Klk6 Kallikrein related-peptidase 6 2.71373 0.00722
211135 D130040H23Rik RIKEN cDNA D130040H23 gene 2.69533 0.00246
103655 Sec14l4 SEC14-like lipid binding 4 2.60984 0.00482
278795 Lrrc10b Leucine rich repeat containing 10B 2.56226 0.00062
242474 Tmem245 Transmembrane protein 245 2.42146 0.00380
666907 Ms4a4a Membrane-spanning 4-domains, subfamily A, member 4A 2.40839 0.00888
232984 B3gnt8 UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 8 2.33381 0.00027
230779 Serinc2 Serine incorporator 2 2.33204 0.00005
18491 Pappa Pregnancy-associated plasma protein A 2.33128 0.01320
236643 Sytl5 Synaptotagmin-like 5 2.30804 0.00168
75556 Cfap161 Cilia and flagella associated protein 161 2.30577 0.00229
333670 Gm867 Predicted gene 867 2.25836 0.01556
69398 Cdhr4 Cadherin-related family member 4 2.24019 0.00583
14580 Gfap Glial fibrillary acidic protein 2.17004 0.00029
74717 Spata17 Spermatogenesis associated 17 2.14429 0.04395
18619 Penk Preproenkephalin 2.13625 0.01126
240327 Gm4951 Predicted gene 4951 2.13048 0.04154
18733 Pirb Paired Ig-like receptor B 2.09880 0.00292
71738 Mamdc2 MAM domain containing 2 2.07577 0.00016
100861668 Gm21119 Predicted gene, 21119 2.05938 0.04798
18198 Musk Muscle, skeletal, receptor tyrosine kinase 2.05913 0.00261
69032 Lyzl4 Lysozyme-like 4 2.05672 0.01272
70274 Ly6g6e Lymphocyte antigen 6 complex, locus G6E 2.05624 0.00089
223650 Eppk1 Epiplakin 1 2.05213 0.01360
105246824 Gm42048 Predicted gene, 42048 2.01962 0.03546
58223 Mmp19 Matrix metallopeptidase 19 2.01409 0.00299
b. Down-regulated genes
665622 Hist1h2br Histone cluster 1 H2br −2.00065 0.03103
208372 Asb18 Ankyrin repeat and SOCS box-containing 18 −2.00749 0.03440
229320 Clrn1 Clarin 1 −2.03603 0.00422
12363 Casp4 Caspase 4, apoptosis-related cysteine peptidase −2.04280 0.01282
19252 Dusp1 Dual specificity phosphatase 1 −2.04784 0.00063
11596 Ager Advanced glycosylation end product-specific receptor −2.05349 0.00429
170720 Card14 Caspase recruitment domain family, member 14 −2.13673 0.02004
217143 Gpr179 G protein-coupled receptor 179 −2.16473 0.00713
102871 Radx RPA1 related single stranded DNA binding protein, X-linked −2.18001 0.00881
109245 Lrrc39 Leucine rich repeat containing 39 −2.28429 0.00044
14281 Fos FBJ osteosarcoma oncogene −2.30223 0.04435
21667 Tdgf1 Teratocarcinoma-derived growth factor 1 −2.37906 0.04659
239250 Slitrk6 SLIT and NTRK-like family, member 6 −2.42304 0.00278
97122 H4c14 Histone cluster 2, H4 −2.43449 0.02622
11924 Neurog2 Neurogenin 2 −2.67322 0.00954
102633345 Gm9922 Predicted gene 9922 −2.73599 0.00258
22127 Tsx Testis specific X-linked gene −2.83551 0.01123
12227 Btg2 B cell translocation gene 2, anti-proliferative −3.00434 0.00000
100169864 Gm44504 Predicted readthrough transcript (NMD candidate), 44504 −3.62548 0.04615
16007 Ccn1 Cellular communication network factor 1 −3.63716 0.00380
319164 H2ac6 Histone cluster 1, H2ac −5.58621 0.01429

Differentially expressed genes in the auditory cortex.

Table 3

Gene ID Gene symbol Description Fold change (deaf/control) Raw p-value (deaf/control)
a. Up-regulated genes
14067 F5 Coagulation factor V 11.32650 0.00051
100040591 Kcnj13 Potassium inwardly-rectifying channel, subfamily J, member 13 8.65329 0.01092
11826 Aqp1 Aquaporin 1 7.30121 0.01005
19116 Prlr Prolactin receptor 6.15455 0.00349
12837 Col8a1 Collagen, type VIII, alpha 1 5.81586 0.00660
73608 Marveld3 MARVEL (membrane-associating) domain containing 3 4.63891 0.00105
96875 Prg4 Proteoglycan 4 (megakaryocyte stimulating factor, articular superficial zone protein) 4.44379 0.00000
434223 Gm1966 Predicted gene 1966 4.08558 0.00000
63873 Trpv4 Transient receptor potential cation channel, subfamily V, member 4 4.08263 0.02017
54612 Sfrp5 Secreted frizzled-related sequence protein 5 4.06183 0.02991
16668 Krt18 Keratin 18 3.99511 0.01639
338403 Cndp1 Carnosine dipeptidase 1 (metallopeptidase M20 family) 3.92104 0.00727
12374 Casr Calcium-sensing receptor 3.91579 0.00079
14960 H2-Aa Histocompatibility 2, class II antigen A, alpha 3.75704 0.00265
100169864 Gm44504 Predicted readthrough transcript (NMD candidate), 44504 3.62006 0.04651
12828 Col4a3 Collagen, type IV, alpha 3 3.55651 0.01753
245945 Rbm47 RNA binding motif protein 47 3.55321 0.03480
18606 Enpp2 Ectonucleotide pyrophosphatase/phosphodiesterase 2 3.42590 0.01823
18784 Pla2g5 Phospholipase A2, group V 3.31853 0.03922
16149 Cd74 CD74 3.22550 0.01522
71355 Col24a1 Collagen, type XXIV, alpha 1 3.17166 0.00002
329941 Col8a2 Collagen, type VIII, alpha 2 3.17105 0.02224
246048 Chodl Chondrolectin 3.08834 0.04821
18542 Pcolce Procollagen C-endopeptidase enhancer protein 3.05446 0.00968
12737 Cldn1 Claudin 1 3.00102 0.02733
20347 Sema3b Sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3B 2.98934 0.01241
102639145 LOC102639145 Transcription factor SKN7 2.97922 0.00561
208890 Slc26a7 Solute carrier family 26, member 7 2.97441 0.00013
330830 Drc7 Dynein regulatory complex subunit 7 2.86807 0.03430
14089 Fap Fibroblast activation protein 2.85711 0.02732
100038882 Isg15 ISG15 ubiquitin-like modifier 2.85010 0.00456
16591 Kl Klotho 2.82507 0.02258
268970 Arhgap28 Rho GTPase activating protein 28 2.81550 0.00376
277328 Trpa1 Transient receptor potential cation channel, subfamily A, member 1 2.79628 0.00108
74732 Stx11 Syntaxin 11 2.76440 0.00776
18400 Slc22a18 Solute carrier family 22 (organic cation transporter), member 18 2.74952 0.00940
217169 Tns4 Tensin 4 2.74538 0.04620
625286 Tmem236 Transmembrane protein 236 2.74531 0.01272
16847 Lepr Leptin receptor 2.63457 0.02200
16159 Il12a Interleukin 12a 2.60059 0.00039
243755 Slc13a4 Solute carrier family 13 (sodium/sulfate symporters), member 4 2.58458 0.03987
211577 Mrgprf MAS-related GPR, member F 2.55384 0.00442
71889 Epn3 Epsin 3 2.54936 0.03085
208943 Myo5c Myosin VC 2.52633 0.00544
56072 Lgals12 Lectin, galactose binding, soluble 12 2.52565 0.01337
270097 Vat1l Vesicle amine transport protein 1 like 2.52434 0.00503
74071 Lmntd1 Lamin tail domain containing 1 2.51746 0.00012
217430 Pqlc3 PQ loop repeat containing 2.49585 0.00191
19682 Rdh5 Retinol dehydrogenase 5 2.47215 0.01969
57890 Il17re Interleukin 17 receptor E 2.44971 0.04852
226691 Ifi207 Interferon activated gene 207 2.44593 0.00009
192212 Prom2 Prominin 2 2.42215 0.00144
66898 Baiap2l1 BAI1-associated protein 2-like 1 2.41073 0.01571
244416 Ppp1r3b Protein phosphatase 1, regulatory subunit 3B 2.40559 0.00342
353169 Slc2a12 Solute carrier family 2 (facilitated glucose transporter), member 12 2.39779 0.01277
235631 Prss50 Protease, serine 50 2.36875 0.00626
105246303 Gm41607 Predicted gene, 41607 2.35532 0.00008
269823 Pon3 Paraoxonase 3 2.35186 0.00767
242509 Bnc2 Basonuclin 2 2.34916 0.00009
64378 Gpr88 G-protein coupled receptor 88 2.33412 0.00001
94352 Loxl2 Lysyl oxidase-like 2 2.33005 0.01761
277353 Tcfl5 Transcription factor-like 5 (basic helix-loop-helix) 2.32594 0.02125
22626 Slc23a3 Solute carrier family 23 (nucleobase transporters), member 3 2.32299 0.00865
24110 Usp18 Ubiquitin specific peptidase 18 2.29958 0.02345
12841 Col9a3 Collagen, type IX, alpha 3 2.29593 0.00937
102631705 Gm29975 Predicted gene, 29975, transcript variant X1 2.27515 0.00175
20698 Sphk1 Sphingosine kinase 1 2.27148 0.00111
21956 Tnnt2 Troponin T2, cardiac 2.26911 0.00001
11768 Ap1m2 Adaptor protein complex AP-1, mu 2 subunit 2.26132 0.01163
319239 Npsr1 Neuropeptide S receptor 1 2.24119 0.01661
20271 Scn5a Sodium channel, voltage-gated, type V, alpha 2.20943 0.01370
269120 Optc Opticin 2.19504 0.01620
243634 Ano2 Anoctamin 2 2.18771 0.00009
667803 H2-T-ps Histocompatibility 2, T region locus, pseudogene 2.18066 0.02451
23962 Oasl2 2′-5′ oligoadenylate synthetase-like 2 2.17714 0.01093
15957 Ifit1 Interferon-induced protein with tetratricopeptide repeats 1 2.17630 0.01729
217325 Llgl2 LLGL2 scribble cell polarity complex component 2.17085 0.00156
54123 Irf7 Interferon regulatory factor 7 2.16945 0.02250
64058 Perp PERP, TP53 apoptosis effector 2.16029 0.02229
11856 Arhgap6 Rho GTPase activating protein 6 2.15633 0.00255
12577 Cdkn1c Cyclin-dependent kinase inhibitor 1C (P57) 2.15592 0.01175
67473 Slc47a1 Solute carrier family 47, member 1 2.15002 0.00787
64242 Ngb Neuroglobin 2.13014 0.04284
237759 Col23a1 Collagen, type XXIII, alpha 1 2.12340 0.01515
15959 Ifit3 Interferon-induced protein with tetratricopeptide repeats 3 2.12169 0.00910
27375 Tjp3 Tight junction protein 3 2.09354 0.01509
105247220 Gm42355 Predicted gene, 42355, transcript variant X29 2.09040 0.00952
73338 Itpripl1 Inositol 1,4,5-triphosphate receptor interacting protein-like 1 2.09034 0.02630
22420 Wnt6 Wingless-type MMTV integration site family, member 6 2.09021 0.00848
12904 Crabp2 Cellular retinoic acid binding protein II 2.08715 0.00121
105734727 Gm27021 Predicted gene, 27021 2.08179 0.04292
74424 Tmc5 Transmembrane channel-like gene family 5 2.07842 0.02560
14264 Fmod Fibromodulin 2.07183 0.00313
234582 Ccdc102a Coiled-coil domain containing 102A 2.06606 0.01392
69550 Bst2 Bone marrow stromal cell antigen 2 2.05917 0.00037
667370 Ifit3b Interferon-induced protein with tetratricopeptide repeats 3B 2.02357 0.01225
20289 Scx Scleraxis 2.01667 0.01186
6543 Mdfic MyoD family inhibitor domain containing 2.01167 0.01726
12835 Col6a3 Collagen, type VI, alpha 3 2.00305 0.00554
b. Down-regulated genes
217216 BC030867 cDNA sequence BC030867 −2.11057 0.00374
211135 D130040H23Rik RIKEN cDNA D130040H23 gene −2.11174 0.01995
18071 Nhlh1 Nescient helix loop helix 1 −2.14504 0.00259
240879 Mettl11b Methyltransferase like 11B −2.19592 0.02249
12939 Pcdha7 Protocadherin alpha 7 −2.21782 0.04278
15442 Hpse Heparanase −2.25788 0.00477
74589 Kbtbd12 Kelch repeat and BTB (POZ) domain containing 12 −2.27407 0.03156
210853 Zfp947 Zinc finger protein 947 −2.28188 0.00010
116903 Calcb Calcitonin-related polypeptide, beta −2.31538 0.02406
320604 Ccdc169 Coiled-coil domain containing 169 −2.37904 0.02867
69852 Tcf23 Transcription factor 23 −2.44860 0.00361
75015 4930503B20Rik RIKEN cDNA 4930503B20 gene −2.44869 0.02793
97122 H4c14 Histone cluster 2, H4 −2.50098 0.02006
383592 Kif28 Kinesin family member 28 −2.90396 0.01641
115488671 LOC115488671 Uncharacterized LOC115488671 −3.20361 0.02140

Differentially expressed genes in the hippocampus.

Figure 2

Figure 2

Protein-coding DEGs of the cochlea, audigory cortex, and hippocampus. In the cochlea, there were 16 up-regulated and 32 down-regulated protein-coding DEGs, while in the auditory cortex, there were 31 up-regulated and 21 down-regulated protein-coding DEGs. In the hippocampus, 99 up-regulated and 15 down-regulated protein-coding DEGs were expressed.

Results of GO analysis

Cochlea

When we analyzed the cochlea, 294 GOs were identified as statistically significant (biological process, 259; molecular function, 34; cellular component, 1). The highly enriched GOs in the biological processes of the cochlea were mostly networked with down-regulated genes (Figure 3A). When the GOs of the biological process were classified into upper categories by the treemap technique, many categories were related to cell proliferation (regulation of lipid biosynthetic process, skeletal muscle cell differentiation, transcription by RNA polymerase II, response to fibroblast growth factor, and regulation of keratinocyte differentiation). There were other categories related to apoptosis [positive regulation of the apoptotic process, negative regulation of the mitogen-activated protein kinase (MAPK) cascade, regulation of cell death, and cell death; Figure 3B, Supplementary Table 1].

Figure 3

Figure 3

GO analysis on biological process of the cochlea. (A) Term annotation analysis from GOnet. Many genes are turned out down-regulated. (B) Treemap analysis from REVIGO. Many GOs can be categorized into cell proliferation and apoptosis.

Auditory cortex

In the auditory cortex, 211 GOs were identified (biological processes, 193; molecular functions, 1; and cell components, 17). In the biological processes of the auditory cortex, the up-regulated and down-regulated genes play similar roles (Figure 4A). GOs of the biological process can be classified as behavior-concerning (behavior and regulation of behavior), synapse-related (regulation of long-term synaptic potentiation and synaptic signaling), cell-signaling (response to purine-containing compound and regulation of kinase activity), cell metabolism (response to purine-containing compound, regulation of kinase activity, and protein dephosphorylation), and apoptosis (regulation of neuron death) categories, among others (Figure 4B, Supplementary Table 2).

Figure 4

Figure 4

GO analysis on biological process of the auditory cortex. (A) Term annotation analysis from GOnet. Up-regulated genes and down-regulated genes are mixed to a similar degree. (B) Treemap analysis from REVIGO. GOs can be classified into behavior-related, synapse-related, cell signaling, cell metabolism, and apoptosis categories.

Hippocampus

In the hippocampus, 203 GOs were identified (biological processes, 137; molecular functions, 40; and cell components, 26). In the biological process of the hippocampus, most key genes were upregulated (Figure 5A). GOs of the biological process can be classified as inflammation-related (response to cytokine, regulation of cytokine-mediated signaling pathway, and positive regulation of fibroblast proliferation) and viral infection-related categories (negative regulation of the viral process, regulation of viral process, extracellular matrix organization, secretion, and regulation of hydrolase activity, among others; Figure 5B, Supplementary Table 3).

Figure 5

Figure 5

GO analysis on biological process of the hippocampus. (A) Term annotation analysis from GOnet. Most genes are up-regulated. (B) Treemap analysis from REVIGO. GOs can be classified into inflammation-related and viral infection-related categories.

KEGG pathway results

Cochlea

Ten KEGG pathways were statistically significant. These included pathways related to immune responses, such as the tumor necrosis factor (TNF) signaling pathway, and those related to apoptosis, such as the MAPK signaling pathway. Twelve genes were included in these pathways, most of which were down-regulated (Table 4).

Table 4

Map ID Map name p-value Gene ID Gene symbol Description Fold change (deaf/control) Raw p-value (deaf/control)
04668 TNF signaling pathway < 0.001 12702 Socs3 Suppressor of cytokine signaling 3 −2.241 0.005
14281 Fos FBJ osteosarcoma oncogene −8.354 < 0.001
16477 Junb Jun B proto-oncogene −3.012 < 0.001
19225 Ptgs2 Prostaglandin-endoperoxide synthase 2 −3.255 0.004
04010 MAPK signaling pathway 0.001 13537 Dusp2 Dual specificity phosphatase 2 −2.121 < 0.001
14281 Fos FBJ osteosarcoma oncogene −8.354 < 0.001
15370 Nr4a1 Nuclear receptor subfamily 4, group A, member 1 −5.280 < 0.001
19252 Dusp1 Dual specificity phosphatase 1 −2.804 < 0.001
04935 Growth hormone synthesis, secretion and action 0.003 12702 Socs3 Suppressor of cytokine signaling 3 −2.241 0.005
14281 Fos FBJ osteosarcoma oncogene −8.354 < 0.001
16477 Junb Jun B proto-oncogene −3.012 < 0.001
04380 Osteoclast differentiation 0.004 12702 Socs3 Suppressor of cytokine signaling 3 −2.241 0.005
14281 Fos FBJ osteosarcoma oncogene −8.354 < 0.001
16477 Junb Jun B proto-oncogene −3.012 < 0.001
04928 Parathyroid hormone synthesis, secretion and action 0.003 13653 Egr1 Early growth response 1 −4.087 0.002
14281 Fos FBJ osteosarcoma oncogene −8.354 < 0.001
18227 Nr4a2 Nuclear receptor subfamily 4, group A, member 2 −2.527 0.001
04726 Serotonergic synapse 0.004 15564 Htr5b 5-hydroxytryptamine (serotonin) receptor 5B 2.557 0.007
19225 Ptgs2 Prostaglandin-endoperoxide synthase 2 −3.255 0.004
19252 Dusp1 Dual specificity phosphatase 1 −2.804 < 0.001
05167 Kaposi sarcoma-associated herpesvirus infection 0.011 14281 Fos FBJ osteosarcoma oncogene −8.354 < 0.001
19225 Ptgs2 Prostaglandin-endoperoxide synthase 2 −3.255 0.004
22695 Zfp36 Zinc finger protein 36 −2.450 0.006
05166 Human T-cell leukemia virus 1 infection 0.014 13653 Egr1 Early growth response 1 −4.087 0.002
14281 Fos FBJ osteosarcoma oncogene −8.354 < 0.001
22695 Zfp36 Zinc finger protein 36 −2.450 0.006
05140 Leishmaniasis 0.050 14281 Fos FBJ osteosarcoma oncogene −8.354 < 0.001
19225 Ptgs2 Prostaglandin-endoperoxide synthase 2 −3.255 0.004
04913 Ovarian steroidogenesis 0.045 19225 Ptgs2 Prostaglandin-endoperoxide synthase 2 −3.255 0.004
217698 Acot5 Acyl-CoA thioesterase 5 2.532 0.003

Statistically significant KEGG pathways in the cochlea.

Auditory cortex

Seven KEGG pathways were statistically significant. Pathways related to immune reactions (neutrophil extracellular trap formation and B cell receptor signaling pathway), cell death [cyclic adenosine monophosphate (cAMP) signaling pathway], and neuronal synapses (neuroactive ligand-receptor interaction) were expressed. Eleven genes were found in these pathways, and the up-regulated and downregulated genes were combined (Table 5).

Table 5

Map ID Map name p-value Gene ID Gene symbol Description Fold change (deaf/control) Raw p-value (deaf/control)
05034 Alcoholism < 0.001 11540 Adora2a Adenosine A2a receptor 3.562 0.001
13489 Drd2 Dopamine receptor D2 3.514 0.004
97122 H4c14 Histone cluster 2, H4 −2.434 0.026
319164 H2ac6 Histone cluster 1, H2ac −5.586 0.014
665622 Hist1h2br HISTONE cluster 1 H2br −2.001 0.031
04613 Neutrophil extracellular trap formation < 0.001 11596 Ager Advanced glycosylation end product-specific receptor −2.053 0.004
12363 Casp4 Caspase 4, apoptosis-related cysteine peptidase −2.043 0.013
97122 H4c14 Histone cluster 2, H4 −2.434 0.026
319164 H2ac6 Histone cluster 1, H2ac −5.586 0.014
665622 Hist1h2br Histone cluster 1 H2br −2.001 0.031
05322 Systemic lupus erythematosus 0.003 97122 H4c14 Histone cluster 2, H4 −2.434 0.026
319164 H2ac6 Histone cluster 1, H2ac −5.586 0.014
665622 Hist1h2br Histone cluster 1 H2br −2.001 0.031
04024 cAMP signaling pathway 0.006 11540 Adora2a Adenosine A2a receptor 3.562 0.001
13489 Drd2 Dopamine receptor D2 3.514 0.004
14281 Fos FBJ osteosarcoma oncogene −2.302 0.044
05012 Parkinson's disease 0.009 11540 Adora2a Adenosine A2a receptor 3.562 0.001
13489 Drd2 Dopamine receptor D2 3.514 0.004
19252 Dusp1 Dual specificity phosphatase 1 −2.048 0.001
04080 Neuroactive ligand-receptor interaction 0.017 11540 Adora2a Adenosine A2a receptor 3.562 0.001
13489 Drd2 Dopamine receptor D2 3.514 0.004
18619 Penk Preproenkephalin 2.136 0.011
04662 B cell receptor signaling pathway 0.043 14281 Fos FBJ osteosarcoma oncogene −2.302 0.044
18733 Pirb Paired Ig-like receptor B 2.099 0.003

Statistically significant KEGG pathways in the auditory cortex.

Hippocampus

Seventeen KEGG pathways were statistically significant. These include inflammatory pathways [cytokine-cytokine receptor interaction and the Janus kinases-signal transducer and activator of transcription proteins (JAK-STAT) signaling pathway] and viral infection-related pathways. Twenty-seven genes were involved in these pathways, most of which were up-regulated (Table 6).

Table 6

Map ID Map name p-value Gene ID Gene Symbol Description Fold change (deaf/control) Raw p-value (deaf/control)
04974 Protein digestion and absorption < 0.001 12828 Col4a3 Collagen, type IV, alpha 3 3.557 0.018
12835 Col6a3 Collagen, type VI, alpha 3 2.003 0.006
12837 Col8a1 Collagen, type VIII, alpha 1 5.816 0.007
12841 Col9a3 Collagen, type IX, alpha 3 2.296 0.009
71355 Col24a1 Collagen, type XXIV, alpha 1 3.172 0.000
237759 Col23a1 Collagen, type XXIII, alpha 1 2.123 0.015
329941 Col8a2 Collagen, type VIII, alpha 2 3.171 0.022
100040591 Kcnj13 Potassium inwardly-rectifying channel, subfamily J, member 13 8.653 0.011
05165 Human papillomavirus infection < 0.001 12828 Col4a3 Collagen, type IV, alpha 3 3.557 0.018
12835 Col6a3 Collagen, type VI, alpha 3 2.003 0.006
12841 Col9a3 Collagen, type IX, alpha 3 2.296 0.009
22420 Wnt6 Wingless-type MMTV integration site family, member 6 2.090 0.008
23962 Oasl2 2'-5' oligoadenylate synthetase-like 2 2.177 0.011
217325 Llgl2 LLGL2 scribble cell polarity complex component 2.171 0.002
667803 H2-T-ps Histocompatibility 2, T region locus, pseudogene 2.181 0.025
100038882 Isg15 ISG15 ubiquitin-like modifier 2.850 0.005
05168 Herpes simplex virus 1 infection < 0.001 14960 H2-Aa Histocompatibility 2, class II antigen A, alpha 3.757 0.003
16149 Cd74 CD74 antigen 3.225 0.015
16159 Il12a Interleukin 12a 2.601 0.000
54123 Irf7 Interferon regulatory factor 7 2.169 0.023
69550 Bst2 Bone marrow stromal cell antigen 2 2.059 0.000
210853 Zfp947 Zinc finger protein 947 −2.282 0.000
667803 H2-T-ps Histocompatibility 2, T region locus, pseudogene 2.181 0.025
04530 Tight junction 0.005 12737 Cldn1 Claudin 1 3.001 0.027
27375 Tjp3 Tight junction protein 3 2.094 0.015
73608 Marveld3 MARVEL domain containing 3 4.639 0.001
217325 Llgl2 LLGL2 scribble cell polarity complex component 2.171 0.002
05152 Tuberculosis 0.006 14960 H2-Aa Histocompatibility 2, class II antigen A, alpha 3.757 0.003
16149 Cd74 CD74 antigen 3.225 0.015
16159 Il12a Interleukin 12a 2.601 < 0.001
20698 Sphk1 Sphingosine kinase 1 2.271 0.001
05330 Allograft rejection 0.007 14960 H2-Aa Histocompatibility 2, class II antigen A, alpha 3.757 0.003
16159 Il12a Interleukin 12a 2.601 < 0.001
667803 H2-T-ps Histocompatibility 2, T region locus, pseudogene 2.181 0.025
04622 RIG-I-like receptor signaling pathway 0.009 16159 Il12a Interleukin 12a 2.601 < 0.001
54123 Irf7 Interferon regulatory factor 7 2.169 0.023
100038882 Isg15 ISG15 ubiquitin-like modifier 2.850 0.005
04940 Type I diabetes mellitus 0.009 14960 H2-Aa Histocompatibility 2, class II antigen A, alpha 3.757 0.003
16159 Il12a Interleukin 12a 2.601 < 0.001
667803 H2-T-ps Histocompatibility 2, T region locus, pseudogene 2.181 0.025
05169 Epstein-Barr virus infection 0.012 14960 H2-Aa Histocompatibility 2, class II antigen A, alpha 3.757 0.003
54123 Irf7 Interferon regulatory factor 7 2.169 0.023
667803 H2-T-ps Histocompatibility 2, T region locus, pseudogene 2.181 0.025
100038882 Isg15 ISG15 ubiquitin-like modifier 2.850 0.005
04512 ECM-receptor interaction 0.014 12828 Col4a3 Collagen, type IV, alpha 3 3.557 0.018
12835 Col6a3 Collagen, type VI, alpha 3 2.003 0.006
12841 Col9a3 Collagen, type IX, alpha 3 2.296 0.009
04612 Antigen processing and presentation 0.015 14960 H2-Aa Histocompatibility 2, class II antigen A, alpha 3.757 0.003
16149 Cd74 CD74 antigen 3.225 0.015
667803 H2-T-ps Histocompatibility 2, T region locus, pseudogene 2.181 0.025
04060 Cytokine-cytokine receptor interaction 0.022 16159 Il12a Interleukin 12a 2.601 < 0.001
16847 Lepr Leptin receptor 2.635 0.022
19116 Prlr prolactin receptor 6.155 0.003
57890 Il17re Interleukin 17 receptor E 2.450 0.049
04151 PI3K-Akt signaling pathway 0.037 12828 Col4a3 Collagen, type IV, alpha 3 3.557 0.018
12835 Col6a3 Collagen, type VI, alpha 3 2.003 0.006
12841 Col9a3 Collagen, type IX, alpha 3 2.296 0.009
19116 Prlr Prolactin receptor 6.155 0.003
05160 Hepatitis C 0.045 12737 Cldn1 Claudin 1 3.001 0.027
15957 Ifit1 Interferon-induced protein with tetratricopeptide repeats 1 2.176 0.017
54123 Irf7 Interferon regulatory factor 7 2.169 0.023
04630 JAK-STAT signaling pathway 0.046 16159 Il12a Interleukin 12a 2.601 < 0.001
16847 Lepr Leptin receptor 2.635 0.022
19116 Prlr Prolactin receptor 6.155 0.003
05164 Influenza A 0.049 14960 H2-Aa Histocompatibility 2, class II antigen A, alpha 3.757 0.003
16159 Il12a Interleukin 12a 2.601 < 0.001
54123 Irf7 Interferon regulatory factor 7 2.169 0.023
04514 Cell adhesion molecules 0.049 12737 Cldn1 Claudin 1 3.001 0.027
14960 H2-Aa Histocompatibility 2, class II antigen A, alpha 3.757 0.003
667803 H2-T-ps Histocompatibility 2, T region locus, pseudogene 2.181 0.025

Statistically significant KEGG pathways in the hippocampus.

Discussion

We conducted an omics study to determine how hearing loss affects the central auditory pathway, including cognitive organs. We examined the effects of hearing loss on the cochlea, auditory cortex, and hippocampus using mRNA sequencing. First, we compared the DEGs in these organs. The number of up-regulated genes was the lowest in the cochlea, and it increased toward the central nervous system. In contrast, the number of down-regulated genes increased as we moved toward the periphery (Tables 13, Figures 3A, 4A, 5A). Most DEGs in the cochlea exhibited an inhibitory pattern, whereas those in the hippocampus were excitatory.

In the cochlea, genes related to activator protein 1 (AP-1) transcription (Fos and Junb) and inflammation (Ptgs2 and Socs3) were down-regulated. AP-1 transcription factors control cell differentiation, proliferation, and apoptosis during stress and infections (Ameyar et al., 2003; Hess et al., 2004). This suggests that the peak time of inflammatory response and cell death has passed. In the auditory cortex, genes related to the guanine nucleotide-binding protein (G protein)-coupled receptor superfamily (Drd2 and Adora2a) were up-regulated. They are known to have relation with regulation of cognitive function and mood by promoting dopamine binding and dopamine neurotransmitter receptor activity (Komatsu et al., 2014; Khlghatyan et al., 2019). In the hippocampus, genes related to the major histocompatibility complex (H2-T-ps, H2-Aa, Cd74), helper T cell type 1 (Il12a), and the extracellular matrix (Col4a3, Col6a3, Col9a3) were up-regulated, implying an actively processed adaptive immune reactions and cellular changes.

When examining the GOs and KEGG pathways, we observed that the expression patterns differed for each organ. Genes related to cell proliferation and death were simultaneously expressed in the cochlea (Figure 3B, Supplementary Table 1). The TNF signaling pathway, associated with the immune response, and the MAPK signaling pathway, related to apoptosis, were also activated (Table 4). However, most gene activities were down-regulated. Considering cell proliferation, inflammation, and apoptosis occur simultaneously after hearing loss in the cochlea (Shu et al., 2019; Milon et al., 2021; Warnecke et al., 2021; Chen et al., 2022; Paciello et al., 2023), all of these processes seem to be in the finishing stage.

In the auditory cortex, the GO patterns were quite different. Genes related to behavior and neuronal synapse function were highly expressed, and those associated with cellular metabolism, cell signaling, and apoptosis were also observed (Figure 4B, Supplementary Table 2). The cAMP signaling pathway and neuroactive ligand-receptor interactions seem to be related to neuronal cell death and synaptic activity (Table 5). Therefore, it can be inferred that brain plasticity mechanisms occur through synaptic changes along with neuronal cell death, affecting cognitive function in the auditory cortex. Notably, alcoholism and Parkinson's disease appeared in the KEGG pathway, suggesting that changes in the auditory cortex following hearing loss progress in a similar pattern to these disorders.

The hippocampus differs from the other two organs in that most genes exhibited excitatory activity (Figure 5A). Inflammation-related pathways, such as cytokine-cytokine receptor interactions, were commonly activated, indicating ongoing inflammation (Figure 5B, Table 6, Supplementary Table 3). Interestingly, there are many pathways related to viral and fungal infections that seem unrelated, suggesting that when the hippocampus is challenged, it exhibits a response similar to infection. Thus, neuroinflammation persists in the hippocampus even after 3 months of hearing loss.

In conclusion, apoptosis and inflammation persisted more actively in the order of hippocampus, auditory cortex, cochlea in the long term (12 weeks) after noise-induced hearing loss. This implies that the neurodegenerative effects of noise exposure do not resolve quickly in the central regions but persist for a considerable period. Therefore, cognitive decline following noise-induced hearing loss is likely to be a progressive process rather than an instant deterioration (Figure 6). Some studies see the cognitive decline not just as elongated degeneration, but as accelerated aging (Zhuang et al., 2020; Paciello et al., 2021). In the case of normal C57BL/6J mice, no histological changes are observed in the auditory cortex or hippocampus up to 6 months of age (Dong et al., 2018). However, it appears that there are gradual changes in proteins that play significant roles in plasticity and cognitive functions, such as MMP-9 (Dong et al., 2018). Hence, proactive treatment to prevent the progression of cognitive decline remain of substantial importance even if there are little chance of auditory restoration. For example, auditory rehabilitation strategies, including the utilization of hearing aids or cochlear implants, as well as medical interventions such as antioxidant therapy, may prove beneficial for patients with hearing impairment in mitigating the risk of cognitive decline.

Figure 6

Figure 6

Proposed mechanism how noise-induced hearing loss cause mice cognitive decline. Three months after the onset of noise-induced hearing loss, processes such as cell proliferation, inflammation, and apoptosis come to finishing stage in the cochlea. However, neuronal cell death is still progressing in the auditory cortex, and neuroinflammation persists in the hippocampus. These prolonged neurodegeneration processes in the central region accelerates cognitive decline.

This study has a few limitations. First, this study was conducted only in the aspect of mRNA expression, so it may appear differently at the level of protein. Secondly, this was a cross-sectional study that included 24-week-old mice, and we analyzed its results by considering the general progression of neuroinflammation. However, a time-series study of each organ is required to determine the dynamic mechanisms of neuroinflammation accurately. Finally, the noise used in this study was large enough to evoked permanent hearing loss in a single exposure. Loud noise can affect the central auditory pathway and the hippocampus within a few days (Groschel et al., 2018; Chen et al., 2022). And long-term exposure to noise could affect central neural system differently. In particular, the effects on cognitive function may differ significantly in cases of noise stress or gradual hearing deterioration due to sustained high-intensity noise. Further studies are required to generalize these findings to the overall context of noise-induced hearing loss.

Statements

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1061000/.

Ethics statement

The animal study was approved by Institutional Animal Care and Use Committee (IACUC) of Boramae Hospital (IACUC number 2022-0133). The study was conducted in accordance with the local legislation and institutional requirements.

Author contributions

S-YL: Data curation, Formal analysis, Investigation, Software, Visualization, Writing—original draft. HL: Data curation, Investigation, Methodology, Validation, Writing—review & editing. M-HP: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing—review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Korea National Research Fund (NRF-2020R1A2C1006254; PI: M-HP). The funding body played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2024.1340854/full#supplementary-material

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Summary

Keywords

noise-induced hearing loss, transcriptomics, neurodegeneration, cochlea, auditory cortex, hippocampus

Citation

Lee S-Y, Lee HS and Park M-H (2024) Transcriptomic analysis reveals prolonged neurodegeneration in the hippocampus of adult C57BL/6N mouse deafened by noise. Front. Neurosci. 18:1340854. doi: 10.3389/fnins.2024.1340854

Received

19 November 2023

Accepted

25 January 2024

Published

12 February 2024

Volume

18 - 2024

Edited by

Gi Jung Im, Korea University, Republic of Korea

Reviewed by

Athanasia Warnecke, Hannover Medical School, Germany

Jiwon Chang, Hallym University Medical Center, Republic of Korea

Joong Ho Ahn, University of Ulsan, Republic of Korea

Updates

Copyright

*Correspondence: Min-Hyun Park

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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