Proteome profile of patients with excellent and poor speech intelligibility after cochlear implantation: Can perilymph proteins predict performance?

Modern proteomic analysis and reliable surgical access to gain liquid inner ear biopsies have enabled in depth molecular characterization of the cochlea microenvironment. In order to clarify whether the protein composition of the perilymph can provide new insights into individual hearing performance after cochlear implantation (CI), computational analysis in correlation to clinical performance after CI were performed based on the proteome profile derived from perilymph samples (liquid biopsies). Perilymph samples from cochlear implant recipients have been analyzed by mass spectrometry (MS). The proteins were identified using the shot-gun proteomics method and quantified and analyzed using Max Quant, Perseus and IPA software. A total of 75 perilymph samples from 68 (adults and children) patients were included in the analysis. Speech perception data one year after implantation were available for 45 patients and these were used for subsequent analysis. According to their hearing performance, patients with excellent (n = 22) and poor (n = 14) performance one year after CI were identified and used for further analysis. The protein composition and statistically significant differences in the two groups were detected by relative quantification of the perilymph proteins. With this procedure, a selection of 287 proteins were identified in at least eight samples in both groups. In the perilymph of the patients with excellent and poor performance, five and six significantly elevated proteins were identified respectively. These proteins seem to be involved in different immunological processes in excellent and poor performer. Further analysis on the role of specific proteins as predictors for poor or excellent performance among CI recipients are mandatory. Combinatory analysis of molecular inner ear profiles and clinical performance data using bioinformatics analysis may open up new possibilities for patient stratification. The impact of such prediction algorithms on diagnosis and treatment needs to be established in further studies.

Introduction

Computational biomedicine integrating clinical and omics data is a modern and powerful approach for the development of novel, molecular and individualized diagnosis and treatment regimen. Specifically for organ systems, for which molecular knowledge is poor or if present was derived from animal models, computational biomedicine can give access to unexplored avenues in personalized medicine [1].

Despite rapid technological improvement and implementation to the field, the human inner ear remains a black box. When the inner ear is affected from disease, both organ functions, i.e., hearing and balance, rapidly deteriorate and in many cases do not recover. Indeed, severe hearing loss affects more than 466 million people worldwide and this number is expected to rise dramatically within the next decades [2]. State of the art treatment of hearing loss is cochlear implantation, the insertion of an electrode array into the cochlea for direct electrical activation of the auditory nerve [3]. With this approach, the damaged sensory epithelium of the cochlea, which is responsible for translating sound into electrical signals, is bypassed.

Although it is the clinically most successful neuroprosthetic device, the high inter-individual variability of therapeutic success of the cochlear implant is one of the unanswered key questions [4-6]. Indeed, a significant portion of the patients (up to 40%) experiences a less than expected hearing benefit with the device. Many factors such as age, duration of deafness, genetics, variability in cochlear anatomy, surgical technique and device characteristics, neuronal survival, electrode position or general cognitive and central processing abilities may be attributed for the variation in speech perception [7-10]. Despite the many factors that may influence the outcome of cochlear implantation, less than 20% of the variability can be actually explained and nearly none of them can be targeted therapeutically [10]. For example, patients with the same inner ear disease and implanted with the same electrode array share a wide range of outcomes [11]. Thus, outcome prediction is one of the most challenging topics in clinical cochlear implant-related research. Novel approaches in neuroscience, data analysis, molecular biology and computational analysis are required to understand the wide variability in performance amongst cochlear implant users.

For biomolecular analysis, the accessibility of the cochlea during surgical procedures offers the unique opportunity to gain a “fluid biopsy” by perilymph sampling. In previous work, we and others provided solid proof of the safety and feasibility of this method [12-15]. For example, we were able to define the cochlear microenvironment not only by analysing the proteome [16], but also the inflammasome [17] and miRNA profile [18] in human perilymph from hearing impaired patients.

Based on the idea that apart from the monogenetic disorders most of the diseases leading to hearing loss are the consequence of complex molecular changes challenging the physiological steady state of the inner ear, a single marker may not aid in the precise molecular diagnosis or even as predictor of performance. Therefore, we sought to identify comprehensive and distinctive marker profiles of the cochlear microenvironment as measured prior to implantation in patients who one year after cochlear implantation proved to be good or poor performers in speech intelligibility.

Materials and methods

Cochlear implant recipients (n = 75 implanted ears), who were previously analysed in regard to the proteome profiles of their perilymph, were used for retrospective analysis of clinical data on hearing performance and speech intelligibility one year after cochlear implant surgery. The 45 patients who met the inclusion criteria were used to correlate the proteome profile from the perilymph analysis to the hearing performance data. Demographic data of the 45 patients are summarised in Table 1.

Table 1

Demographic data of the patients undergoing CI surgery with perilymph sampling and one year postoperatively audiologic test data.

Demographic data	Age (mean in years)	n* (%)
Patients	52.8	45 (100)
Male	50.3	22 (48.9)
Female	60	23 (51.1)
Good Performer	53.7	22 (61.1)
Bad Performer	50.95	14 (38.9)
Children (0–18 years)	6	3 (6.7
Adults (19–80)	58.6	42 (91.7)

Perilymph sampling

Human perilymph was collected with a modified micro glass capillary during inner ear surgeries from 68 patients (75 cochleae) as already described in our previous studies [12, 19, 20]. Part of the data has been already published [12, 20]. Using modified micro glass capillary, the round window membrane was punctured directly before the insertion of a cochlear implant electrode array to obtain the perilymph samples. The protocol for collection of the perilymph samples was approved by the Ethics Committee of Hannover Medical School for perilymph by cochlear implantation (approval no. 1883–2013 and 2403–2014). Written informed consent was obtained from every patient or parent or legal guardian in case of children included in this study. The inclusion criteria for perilymph sampling is the presence of a fluid-filled cochlea as determined by magnetic resonance tomography. Every patient undergoing CI surgery was offered to participate in the study/perilymph sampling. Signed informed consent for the study and for the CI surgery was obtained. The proteome analysis was performed at Hanover Medical School.

Proteomic analysis

In a prior study, an intraoperative perilymph sampling method and analysis by an in-depth shot-gun proteomics approach were established allowing the analysis of hundreds of proteins simultaneously in very small sample sizes in a microliters range [12]. The method for protein analysis has been published previously (e.g., in [19]). Perilymph samples were prepared for LC-MS/MS analysis by alkylation and separated using sodium dodecyl sulfate polyacrylamide gel electrophoresis as previously described [19]. Peptide samples were separated with a nano-flow ultra-high pressure liquid chromatography system (RSLC, Thermo Scientific) equipped with a trapping column (3 μm C18 particle, 2 cm length, 75 μm ID, Acclaim PepMap, Thermo Scientific) and a 50 cm long separation column (2 μm C18 particle, 75 μm ID, Acclaim PepMap, Thermo Scientific). The RSLC system was coupled online via a Nano Spray Flex Ion Soure II (Thermo Scientific) to an LTQ-Orbitrap Velos mass spectrometer. Metal-coated fused-silica emitters (SilicaTip, 10 μm i.d., New Objectives) and a voltage of 1.3 kV were used for the electrospray. Overview scans were acquired at a resolution of 60k in a mass range of m/z 300–1600 in the orbitrap analyser and stored in profile mode. The top 10 most intensive ions of charges two or three and a minimum intensity of 2000 counts were selected for CID fragmentation with a normalized collision energy of 38.0, an activation time of 10 ms and an activation Q of 0.250 in the LTQ. Fragment ion mass spectra were recorded in the LTQ at normal scan rate and stored as centroid m/z value and intensity pairs. Active exclusion was activated so that ions fragmented once were excluded from further fragmentation for 70 s within a mass window of 10 ppm of the specific m/z value. The relative protein quantification was performed by label-free quantification (LFQ) and was determined as LFQ intensity [19].

Additionally, proteins were subjected to classification by Gene Ontology Annotations (GOA) using UniProt. The Gene Ontology (GO) classification allows a mapping of the proteins into the categories molecular function, biological process and cellular compartment. Proteins were described using a standardized vocabulary of the UniProt Knowledgebase by uploading the uniprot IDs of the proteins to the UniProt website [19].

Interactome analysis

All proteins obtained from MS analysis were compared for further information with the STRING database (https://string-db.org) whereas text mining was excluded using a confidence level between 0.15–0.4 enabling up to 50 interactors and full network. Candidates with the highest score were used for further comparison.

Audiology: Classification of patients by speech intelligibility

Postoperative hearing performance with a cochlear implant one year after implantation of the 45 patients with perilymph sampling was analyzed. Therefore, data of two audiologic tests (HSM sentence test in noise at 10dB, Freiburg monosyllable word test) one year after implantation were included.

Impedance analysis

All post-operative impedance measurements were acquired after for time points: first fitting, 3, 6 and 12 month. In order to be able to compare the impedances of different cochlear implants, the relative impedance change over time were used. For this purpose, the impedances of the individual electrodes (E1 to Ex) of each implant were normalized to the impedance of the associated electrode at the time point of first fitting (Ex, time point / Ex, first fitting).

Statistical analysis

Mass spectrometric raw data were processed using Max Quant software (version 1.4) and human entries of Swissprot/Uniprot database. As previously described, the threshold for protein identification was set to 0.01 on peptide and protein level [19].

Proteomics data were analysed and compared by Perseus software and ingenuity pathway analysis (IPA, Qiagen Bioinformatics, http://www.ingenuity.com) software. By statistical analysis for the groups excellent and poor performers, significant differences in the levels of numerous proteins were determined. Proteins detected in at least 8 samples of a performance group and after imputation of data (replacement of missing values by normal distribution) were used for statistical analysis. Student’s T-test was performed (p < 0.05) for identification of proteins, with significant differences in the quantification of proteins detected in the two groups.

Results

A total of 75 perilymph samples from 68 patients were analysed. By mass spectrometry, 935 proteins were identified. The results of audiological speech intelligibility one year after cochlear implantation were available for 45 of the 68 patients. These patients were divided into two groups according to their performance data (Fig 1): Patients with good performance (n = 22) were defined with HSM sentence test in noise 10 dB > 30% and Freiburg monosyllables test > 65%, those with poor performance (n = 14) were defined with HSM sentence test in noise 10 dB < 30% and Freiburg monosyllables test < 65%. The good performer group scored a mean of 60.0% +/-18.9 on the HS + 10 dB and 78.2% +/-8.1 on the Freiburg monosyllables test. Demographic data including aetiology are depicted in Table 2. The poor performer scored a mean of 5.8% +/-6.5 on the HSM + 10 dB and of 27.5% +/-21.7 on the Freiburg monosyllables test. Demographic data of the patients with poor performance including aetiology are depicted in Table 3. The remaining patients have not met both selection criteria and were defined as average performer. The proteome of the patients with the worse performance was then compared to the proteome of the patients with the best performance to identify differences among the groups.

Fig 1

Hearing performance of patients.

Patients were categorized by one year postoperative audiologic measurements into two groups. Excellent hearing performance was defined with ≥65% in Freiburger test and simultaneously ≥30% in HSM. Excellent performers (n = 22) are marked in green. Poor hearing performance was defined with <65% in Freiburger test and simultaneously <30% in HSM. Poor performers (n = 14) are depicted in blue.

Table 2

Demographic data of good performers including aetiology.

Demographic data of good performers (n = 22; 61.1%)	Age (mean in years)	n* (%)
Patients	53.7	22 (100)
Male (n)	50.2	13 (59.1)
Female (n)	59.5	9 (40.9)
Children (0–18 years)	5.1	1 (4.5)
Adults (19–80)	56.0	21 (95.5)
Time of hearing loss before CI surgery	6.7	22 (100)
Etiology
EVA		1 (4,5)
Menière’s disease		6 (27,3)
Otosclerosis		3 (13,6)
Unknown		10 (45,5)
Rubella embryopathy		1 (4.5)
Meningitis		1 (4.5)

Table 3

Demographic data of the patients with bad performer including aetiology.

Demographic data of bad performers (n = 14; 38.9)	Age (mean in years)	n* (%)
Patients	51.0	14 (100)
Male (n)	44.4	4 (28.6)
Female (n)	53.6	10 (71.4)
Children (0–18 years)	6.5	2 (14.3)
Adults (19–80)	58.4	12 (85.7)
Time of hearing loss before CI surgery	11.8	14 (100)
Etiology
EVA		4 (28.6)
Menière’s disease		1 (7.1)
Otosclerosis		4 (28.6)
Unknown		4 (28.6)
CMV		1 (7.1)

Differentially expressed proteins in excellent and poor performer

The protein composition and statistically significant differences in the 2 groups were detected by relative quantification (label free quantification, LFQ intensity) of the perilymph proteins. Therefore, 287 proteins were compared, which were identified in at least eight samples in both groups (Fig 2). In the excellent hearing performance group, five proteins were identified with significantly higher abundance. In the poor hearing performance group, six proteins were identified with significantly higher abundance (Fig 2).

Fig 2

Protein quantification.

Label free quantification of detected proteins in the two patients groups was performed by Max Quant software. The boxes mark the proteins with significant differences between the two hearing performance groups (p = 0.05, t-test). Marked in green are proteins significantly higher abundant in the group “excellent performers”. Marked in blue are proteins significantly lower abundant in the group “poor performers”.

All proteins assigned to the cochlear signatures of excellent performer are involved in protective, inflammatory and stress regulation pathways. More specifically, extracellular regulated kinase (ERK) is activated and brain-derived neurotrophic factor (BDNF), pro-survival genes as well as antioxidative enzymes are up-regulated by the proteome signature found in patients who reached excellent performance after cochlear implantation. Each of the proteins are individually explained in the discussion. Additionally, data were analyzed by IPA. Proteins that exhibit significantly different abundances in the comparison of the two performance groups (Table 4) were further analysed by IPA software to get a functional annotation of the proteins with different abundances, visualized in diseases and functions heat maps and networks (Fig 3 and Fig 4).

Table 4

Significantly higher abundant proteins in the groups excellent and poor performers.

Protein IDs	Protein names	Gene names	Location
P06312	Ig kappa chain V-IV region	IGKV4-1	Extracellular space
O75882	Attractin	ATRN	Extracellular space
Q13228	Selenium-binding protein 1	SELENBP1	Cytoplasm
P03952	Plasma kallikrein	KLKB1	Extracellular space
P07357	Complement component C8 alpha chain	C8A	Extracellular space
P0DMV9	Heat shock 70 kDa protein 1A/B	HSPA1B	Cytoplasm
P05164	Myeloperoxidase	MPO	Cytoplasm
P23083	Ig heavy chain V-I region V35	IGHV1-2	Other
P01743	Ig heavy chain V-I region HG3	IGHV1-46	Other
A0A0C4DH24	Immunoglobulin kappa variable 6–21	IGKV6-21	Other
P48764	Sodium/hydrogen exchanger 3	SLC9A3	Plasma membrane

Shown are significantly higher abundant proteins of the group excellent HP in green, of the group poor HP in blue. Each protein is shown in Fig 2 as single point.

Fig 3

Networks of higher abundant proteins in the group excellent hearing performance.

Higher abundant proteins identified in the two hearing performance groups were uploaded and analyzed by IPA software. Shown are the main networks in which the proteins are involved in. Shown is the network in which all 5 higher abundant proteins (marked in green) in the group excellent hearing performance are involved. The function of this network is described by IPA software with Developmental Disorder, Hereditary Disorder, Immunological Disease.

Fig 4

Networks of higher abundant proteins in the group of the poor hearing performer.

Higher abundant proteins identified in the two hearing performance groups were uploaded and analyzed by IPA software. Shown are the main networks in which the proteins are involved in. Shown is the network in which 5 of 6 higher abundant proteins (marked in blue) in the group poor hearing performance are involved. The function of this network is described by IPA software with Carbohydrate Metabolism, Molecular Transport, Small Molecule Biochemistry.

Since we use different CI implants from various manufacturers, the impedances of one array to another cannot easily be compared. This is based on the individual design of the electrode array, which differs, for example, in the number of electrode contacts and thus also in their separation from basal to apical in the cochlea. In the present work, 14 different types of arrays are included and to compare the electrode impedances, the impedance values were normalized based on electrode impedances at the first fitting time point (dashed green line in Fig 5, Fig 6). Fig 5 represents the quantitative analysis of electrode impedance change at three different time points after implantation for good and bad performer. There is a wide dispersion of relative impedance changes as shown in Fig 5. When concentrating on the mean values of impedances changes, a slight increase in the impedance change can be seen from the first fitting to the follow up visits in the group of the poor performer when compared to the good performer. However, the impedances remain stable over time in both groups. The electrodes that largely coincide in their position in 14 different implants are the first basal and the last apical electrode and the relative impedance change in the most apical and the most basal electrode contact over time is depicted in Fig 6. Here again, a wide dispersion of relative impedance changes among the patients of both groups is obvious. While a slight decrease in the mean impedance change of the apical electrode is prominent in both, the poor and good performers (Fig 6B), the impedance changes on the basal electrode increases (Fig 6A). This increase is more prominent in the group of the poor performer than in the group of the good performer.

Fig 5

Relative impedance change (all electrodes).

Relative impedance change of 14 different electrode arrays of bad and good performers normalized on electrode impedances at first fitting time point (dashed line) for 3 time points after implantation (3 month: black; 6 month: red; 1 year: blue dots).

Fig 6

Relative impedance change (first basal / last apical electrode).

Relative impedance change of 14 different electrode arrays of bad and good performers normalized on electrode impedances at first fitting time point (dashed line) for 3 time points after implantation (3 month: black; 6 month: red; 1 year: blue dots). A) First basal electrode; B) Last apical electrode.

Discussion

This is the first report comparing human proteome data to electrophysiological audiological test results in cochlear implant recipients. With this approach, a proteome signature present in patients with excellent or poor speech comprehension performance was established. Proteins, which are assigned to the to the cochlear signature of excellent performer are individually discussed below.

The selen binding protein 1 (SBP1) senses reactive xenobiotics in the cytoplasm (String database). SBP1 has a covalently binding site for selenium and is a highly conserved protein. For many cancer types, reduced expression levels of SBP1 are associated with poor survival suggesting a tumor suppressing role of SBP1 [21]. Due to its detox function, SBP1 has relevant roles in several fundamental physiological functions, from protein degradation to redox modulation. SBP1 has a significant role in the metabolism of sulfur-containing molecules and is in interaction with glutathion peroxidase GPx1 [22]. Indeed, GPx1 is highly expressed in several cell types of the inner ear including hair and supporting cells, spiral ganglion neurons, as well as cells of the stria vascularis, and is essential for maintaining cochlear homeostasis [23]. It has been proven as a molecular target for therapies addressing noise-induced hearing loss [24] and Menière’s disease (clinical trial number NCT03325790). Pharmacological up-regulation of GPx1 [23] as induced by ebselen has been shown to prevent noise-induced hearing loss both in animal models as well in clinical trials [24]. On a cellular level, GPx1 up-regulation in the cochlea protects inner hair cells from denervation, prevents swelling of the afferent dendrites and of the stria vascularis and is regarded as an endogenous protective mechanism of the cochlea [23, 25, 26]. In obese patients with an unhealthy metabolic profile and at an increased risk to develop cardiovascular disease, SBP1 levels are reduced when compared to obese patients with a healthy metabolic profile [27]. Interaction database analysis reveals that SBP1 is in contact with PLAUR, which encodes the urokinase receptor (uPAR) [27]. This, in turn, promotes cell survival.

Attractin is encoded by the ATRN gene, which has many transcript variants. One isoform is a secreted protein involved in the initial immune cell clustering during inflammatory responses to regulate the chemotactic activity of chemokines [28]. It shows interaction with MGRN1, an E3 ubiquitin ligase. Membrane-bound attractin is anchored on the surface of neurons or glial cells and mediates the myelination signal through its extracellular domains [29]. Secreted attractin may interfere with membrane-bound attractin thereby disrupting neurite formation in differentiating cortical neural cells in vitro [30]. Interestingly, secreted attractin that is present in the circulation is prevented by the blood-brain-barrier to enter the CNS and to interfere with the membrane-bound form [30]. For the brain, attractin has been shown to play a neuroprotective role [31]. The role of attractin in the perilymph is unclear. However, since lack of attractin is associated with oxidative stress and neurodegeneration by a decrease of extracellular regulated kinase (ERK), attractin might be involved in mediating cell survival under oxidative stress [32]. The role of ERK-mediated neuroprotection in auditory neurons in vitro [33] and in vivo [34] is leading to the hypothesis that increased attractin levels in the perilymph of excellent performer might be related to an improved cochlear health at the time of implantation. However, there is no evidence whatsoever to support this hypothesis and the role of attractin in the inner ear needs further investigation.

Another factor found in the perilymph of patients that later were shown to become excellent performer with their implant is complement C8, an important antibacterial immune effector [35]. Complement C8 initiates membrane penetration and coordinates membrane attack complex (MAC) pore formation leading to cell lysis [35]. It has close interaction with other complement types e.g., C6, C7, C9. However, at concentrations that are considered sublytic, bound C5b–C9 complex activates protective intracellular signaling pathways induce resistance to apoptosis and upregulation of pro-survival genes such as Bcl-2 [36].

Kallikreins are serine proteases that cleave kininogens to produce bradykinin leading to inflammation. Kallikrein 1 triggers sensory nerve stimulation [37]. In addition, kallikreins enable kinin signaling thereby promoting cell survival, reducing oxidative stress and maintaining cellular integrity [38]. Tissue kallikreins increase the expression of the neurotrophin brain- derived neurotrophic factor (BDNF) and of pro-survival Bcl-2 genes [38]. Due to its neuroprotective effect, the biomacromolecule kallikrein is specifically of interest in treating cerebral ischemia injury [39].

The immunoglobulin chain IGKV4–1 was found to be up-regulated in the patients with excellent performance, whereas IGHV1-2, IGHV1-46 and IGKV6-21 were up-regulated in patients with poor performance. Interestingly, when comparing demyelinating with remyelinating lesions of the central nervous system, the IGHV4-1 transcript that is also found in the B cell receptor transcriptome of the CSF was up-regulated [40].

Myeloperoxidase (MPO) is synthesized in promyelocytes and promyelomonocytes in the bone marrow [41]. Belonging to the haem peroxidase-cyclooxygenase superfamily, MPO can oxidize tyrosine to tyrosyl radical or chloride to hypochlorous acid (HOCl) using hydrogen peroxide as an oxidizing agent. Tyrosyl radical, a cytotoxic molecule, is released by neutrophils to kill bacteria and other pathogens [41]. Thus, patients with MPO deficiencies have a significant risk for infections [42]. On the other hand, increased levels of oxidants by MPO can damage tissue damage and are found in many diseases characterized by acute or chronic inflammation [41] such as cardiovascular and neurodegenerative diseases. Inflammation-associated oxidants such as HOCl-and tyrosyl radicals may bind to the components of the extracellular matrix and to proteins leading to the changes associated with oxidant damage such as atherosclerosis [43]. In animal models, neutrophil-derived MPO seems to be involved in the pathogenesis of Alzheimer’s like disease and inhibition of MPO might present a novel therapeutic target to combat cognitive decline [44]. Indeed, MPO-derived HOCl might induce senescence [45]. Also, MPO activity is increased in a model of graft versus host disease and is increased in the senescence-associated secretory phenotype [46]. In the inner ear, expression of MPO is associated with cochlear dysfunction probably due to MPO-catalyzed ROS accumulation and damage of the stria vascularis [47]. Whether and how MPO and poor performance after cochlear implantation might be linked is not known and needs further investigation.

Heat shock proteins such as Hsp70 have diverse functions in protein folding and restoration. For the inner ear, a protective effect of Hsp70 has been shown in several experimental models [48-50]. However, Hsp70 is also a stress marker when unfolded proteins accumulate due to proteotoxic stress [51]. In addition, Hsp70 interacts with BAG5, a chaperone regulator, to reduce ubiquitination of the client protein STIP1, a stress-induced phosphoprotein and co-chaperone of Hsp90 [52]. Cells lacking STIP1 compensate the proteasomal defect by improved protein folding [53], thereby reequilibrating the proteostatic balance in diseases such as neurodegenerative disorders [53].

The solute carrier family 9, subfamily A, member 3 (SLC9A3, NHE-3) is a sodium–hydrogen exchanger (NHE) that acts in pH regulation and plays a role in signal transduction to form a chemical gradient of ions for absorbing the sodium ion. In the gerbil inner ear, 4 isoforms of NHE, including NHE-3 are expressed. Although the other isoforms are broadly expressed in the inner ear, NHE-3 expression was limited to the apical surface of the marginal cells in the stria vascularis [54]. Based on data from the interaction database STRING, NHE-3 is linked with Rho kinase, endothelin-1, calcineurin, calmodulin signaling pathways, all pathways playing a crucial role in inner ear homeostasis and pathophysiology and presenting interesting drug targets [55-57]. For example, calcineurin activation contributes to noise induced hearing loss [57]. Endothelin induces vasospasms of the spiral modiolar artery via activation of the Rho-kinase and an increase in calcium-sensitivity [55]. Another role of SLC9A3 as an important interacting partner or modifier of the cystic fibrosis transmembrane conductance regulator (CFTR), a gene of which mutations are causative for cystic fibrosis (CF), has been described recently [58]. SLC9A3 gene variants can affect susceptibility to bacterial infections and severity of pulmonary condition by interaction with CFTR, thus providing evidence of its modular effect on CF [58]. Whether and how gene variants of SLC9A3 could affect genes important for cochlear homeostasis is unknown hitherto.

The present work is a pilot study and shows how proteome data can be linked to functional data in cochlear implantation and correlated using artificial intelligence-based bioinformatics analysis approaches. Whether the determined molecular perilymph profile can be used as a predictive factor for individual implant performance cannot be concluded from the present results. The main limitation of the study is the time gap between determining the proteome profile of the cochlea and the outcome data of cochlear implantation. Lack of methods to collect perilymph at time intervals such as weeks or months after implantation make it impossible to correlate speech performance data to the actual perilymph profile. Thus, inflammation processes especially related to the surgery and implantation procedure were not taken into account. In the present study, we also analysed the presence of residual hearing and impedance values post implantation as additional factors that influence the outcome after implantation. However, due to the heterogeneous patient population and the different implants used, a validated association cannot be derived from the results. We merely compared the proteome profile at the time point of implantation and correlated to the later outcome as measured by speech perception tests. Whether perilymph profiles can be used as predictive factors for speech performance after cochlear implantation needs verification in multicentre and controlled studies including a higher number of patients. This is especially of importance in order to control for the different clinical factors that have been shown to predict speech perception outcome in adult cochlear implant recipients [59]. Even so, our data allow the cautious assumption that in the near future, therapeutic decision trees such as drug device combinations for patients suffering from acute or chronic inner ear disease may emerge depending on their perilymph molecular profile.

Conclusion

Statistical analysis of the perilymph proteome identified significantly elevated differential proteins in both patient groups, i.e., patients with excellent and patients with poor performance after cochlear implantation. This could open up a new possibility to make statements on the pathophysiology of the hearing disorder and to predict the individual hearing performance of patients after cochlear implantation by means of a molecular perilymph analysis or to be able to add a special local drug therapy in order to improve the performance.

24 May 2021

PONE-D-21-08371

Proteome profile of patients with excellent and poor speech intelligibility after cochlear implantation: Can perilymph proteins predict performance?

PLOS ONE

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- "Detection of BDNF-Related Proteins in Human Perilymph in Patients with Hearing Loss," doi: 10.3389/fnins.2019.00214

In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the methods section. Further consideration is dependent on these concerns being addressed.

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Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Cochlear implantation provides a unique opportunity to sample human perilymph in a safe and ethically appropriate way. The authors of this paper have previously used this sampling technique to characterise the protein composition or proteotome in order to give an insight into the composition of proteins able to be sampled from perilymph. This is potentially a very important technique for ongoing research into inner ear pathology. This study was performed retrospectively to compare the protein profiles in different Cochlear implant candidates with their performance a year after the surgery. It would certainly appears that this study was conducted as an afterthought.

As is mentioned, there are significant differences in hearing performance among Cochlear implant candidates, approximately 20% of which cannot be explained. Research into this issue is certainly welcomed. There a number of issues with this study which mean that it does little to advance our ability to answer these questions, other than suggesting the sort of investigation as a possible avenue of research. There are relatively few patients that can be matched via implant performance and proteotome, the final protein analysis involves 287 proteins and at least 8 samples in excellent and poor performing groups. Both adults and children are used which would likely increase the heterogenicity of causes of hearing loss. By necessity, the perilymph samples are only taken before electrode insertion, thus do not reflect protein changes due to the insertional trauma and subsequent inflammation. There is certainly no getting around this and this is acknowledged by the authors.

Different individual reactions to the combination of electrode insertion or trauma and foreign body reaction to the electrode are likely to be part of the variations in performance that we see. It would have been useful to know whether the was residual hearing lost in these people. It would also be useful to have data regarding electrode impedance, which may act as an indication of the extent of fibrosis around the electrode. It is not stated in the paper, but probably the patients used in the study did not have substantial residual hearing.

Overall, while I do not think any conclusions can be made about the results of this study, Due to the very early stages of this type of research, it is of interest to other groups and may stimulate further research along these same lines.

Reviewer #2: This is a nice and innovative paper.

Please increase the resolution of figures 3 and 4, as they now appear blurry.

More in-depth discussion is needed for the proteins that were changed in each group, including some speculation on why and how these are related to the outcomes (poor or good).

It would be helpful to add to the discussion some details on how the correlation might help the process of patient selection or additional treatments in the clinic.

**********

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Reviewer #1: Yes: Philip Antony Bird

Reviewer #2: Yes: Yehoash Raphael

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16 Dec 2021

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

ANTWORT

Reviewer #2: Yes

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

ANTWORT

Reviewer #2: Yes

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1:

a) Cochlear implantation provides a unique opportunity to sample human perilymph in a safe and ethically appropriate way. The authors of this paper have previously used this sampling technique to characterise the protein composition or proteotome in order to give an insight into the composition of proteins able to be sampled from perilymph. This is potentially a very important technique for ongoing research into inner ear pathology. This study was performed retrospectively to compare the protein profiles in different Cochlear implant candidates with their performance a year after the surgery. It would certainly appears that this study was conducted as an afterthought.

Response: Thank you for your kind comment. The reviewer is right; the proteome data have been previously published. However, a correlation of the proteome profile at the time point of implantation and the speech performance data with the implant has not been performed and was the focus of the present study.

b) As is mentioned, there are significant differences in hearing performance among Cochlear implant candidates, approximately 20% of which cannot be explained. Research into this issue is certainly welcomed. There a number of issues with this study which mean that it does little to advance our ability to answer these questions, other than suggesting the sort of investigation as a possible avenue of research.

Response: As stated by the reviewer, there are some issues related with our study. The number of the patients included is limited, the changes of the proteome associated with the implantation procedure cannot be, at least with the currently available technology, determined. Thus, the study shows that there might be a possibility that the state in which the CI is implanted might determine outcomes and that perilymph analysis might provide an interesting predictive tool. The limitations as also acknowledged by the reviewer are discussed in the manuscript. For more clarity, we have revised this section as follows.

Line 387-390: “The present work is a pilot study and shows how proteome data can be linked to functional data in cochlear implantation and correlated using artificial intelligence-based bioinformatics analysis approaches. Whether the determined molecular perilymph profile can be used as a predictive factor for individual implant performance cannot be concluded from the present results.”

c) There are relatively few patients that can be matched via implant performance and proteotome, the final protein analysis involves 287 proteins and at least 8 samples in excellent and poor performing groups. Both adults and children are used which would likely increase the heterogenicity of causes of hearing loss. By necessity, the perilymph samples are only taken before electrode insertion, thus do not reflect protein changes due to the insertional trauma and subsequent inflammation. There is certainly no getting around this and this is acknowledged by the authors.

Response: Thank you for your kind comment. This shows how difficult research in this field can be due to limitations in the accessibility of the inner ear.

d) Different individual reactions to the combination of electrode insertion or trauma and foreign body reaction to the electrode are likely to be part of the variations in performance that we see. It would have been useful to know whether the was residual hearing lost in these people.

Response: In our previous study (Schmitt et al 2017), we have analysed residual hearing of patients to show whether perilymph sampling leads to loss of residual hearing. Additionally, we included audiology and proteomics data of patients of our recent study (Personalized Proteomics for Precision Diagnostics in Hearing Loss: Disease-Specific Analysis of Human Perilymph by Mass Spectrometry; Heike A. Schmitt*, Andreas Pich, Nils K. Prenzler, Thomas Lenarz, Jennifer Harre, Hinrich Staecker, Martin Durisin, and Athanasia Warnecke; ACS Omega 2021, 6, 33, 21241–21254; Publication Date:August 13, 2021; https://doi.org/10.1021/acsomega.1c01136).

After interpretation of the pure tone audiograms before cochlear implantation of the study patients relating to any residual hearing, we could evaluate following results: Of the 14 patients with bad performance, 11 patients had profound hearing loss and 3 patients showed some residual hearing. Of the 22 patients with good performance 12 patients had profound hearing loss and 10 patients showed some residual hearing. The results show that most of the patients with bad performance had preoperatively no residual hearing; however, nearly half of the good performance patients group had also preoperatively no residual hearing.

Although the presence of residual hearing is acknowledged as one of the known factors to influence performance with a CI, there must be more factors affecting the outcome, since not all patients without residual hearing are bad performers and not all patients with residual hearing are good performer. Whether the proteome profile alone or in combination with other factors such as residual hearing may be of use to predict poor and good performer needs further analysis. With the limited numbers from our study we simply can show that there are distinct profiles among some patient groups and that this finding needs further investigation.

e) It would also be useful to have data regarding electrode impedance, which may act as an indication of the extent of fibrosis around the electrode. It is not stated in the paper, but probably the patients used in the study did not have substantial residual hearing.

Response: Thank you for this interesting suggestion. We initially refrained from correlating perilymph data to impedances due to the heterogeneity of the implants used. As suggested by the reviewer, we (our new co-author Dr. Melanie Steffens) have performed this analysis. She also looked whether the good or bad performance was related to a specific electrode type and position in the cochlea as cofounding factor. Since the group included in the present analysis was heterogenous in terms of the implant manufacturer as well as the electrode and implant type, this analysis did not reveal any additional results to the present study. We have added the results to the manuscript and to the discussion one sentence acknowledging this fact.

Line 248-266: “Since we use different CI implants from various manufacturers, the impedances of one array to another cannot easily be compared. This is based on the individual design of the electrode array, which differs, for example, in the number of electrode contacts and thus also in their separation from basal to apical in the cochlea. In the present work, 14 different types of arrays are included and to compare the electrode impedances, the impedance values were normalized based on electrode impedances at the first fitting time point (dashed green line in Fig 5, Fig 6). Figure 5 represents the quantitative analysis of electrode impedance change at three different time points after implantation for good and bad performer. There is a wide dispersion of relative impedance changes as shown in Fig. 5. When concentrating on the mean values of impedances changes, a slight increase in the impedance change can be seen from the first fitting to the follow up visits in the group of the poor performer when compared to the good performer. However, the impedances remain stable over time in both groups. The electrodes that largely coincide in their position in 14 different implants are the first basal and the last apical electrode and the relative impedance change in the most apical and the most basal electrode contact over time is depicted in Figure 6. Here again, a wide dispersion of relative impedance changes among the patients of both groups is obvious. While a slight decrease in the mean impedance change of the apical electrode is prominent in both, the poor and good performer (Fig 6 B), the impedance changes on the basal electrode increases (Fig 6 A). This increase is more prominent in the group of the poor performer than in the group of the good performer.

Line 395-399: “In the present study, we also analysed the presence of residual hearing and impedance values post implantation as additional factors that influence the outcome after implantation. However, due to the heterogenous patient population and the different implants used, a validated association cannot be derived from the results.”

Line 268-276:

“Figure 5: Relative impedance change (all electrodes)

Figure 6: Relative impedance change (first basal / last apical electrode)

Overall, while I do not think any conclusions can be made about the results of this study, Due to the very early stages of this type of research, it is of interest to other groups and may stimulate further research along these same lines.

Response: Thank you for your comment. As stated in the manuscript, we do not draw any conclusions form the molecular profiles. We simply observed these two different profiles in the two different groups of patients and discussed each protein individually. Whether and how the proteome profile influences implant performance needs further research. As stated by the reviewer, we simply wanted to delineate this avenue and stimulate research along this line. We hope that this now becomes much clearer from reading the revised version of the manuscript.

Reviewer #2: This is a nice and innovative paper.

a) Please increase the resolution of figures 3 and 4, as they now appear blurry.

Response: Thank you for your kind comment. The figures have been revised and replaced accordingly.

b) More in-depth discussion is needed for the proteins that were changed in each group, including some speculation on why and how these are related to the outcomes (poor or good).

Response: We have initially thought to discuss the proteins in more detail and also to include speculations why and how these could relate to cochlear health and implant performance. However, we refrained from doing so. Especially in lieu with the comments of reviewer 1 we would think that such an in- depth discussion would be misleading. We hope to be able to initiate worldwide perilymph analysis and thus be able to generate more homogenous groups for improved analysis. Indeed, we have already started international collaborations to this aim. However, where suitable, we have emphasized the neuroprotective potential of the different molecules since we speculate that specific factors may be released upon damage / hearing loss in the inner ear that serve to preserve neuronal function alongside the auditory pathway. This is very speculative and thus we have not included this speculation to the manuscript.

c) It would be helpful to add to the discussion some details on how the correlation might help the process of patient selection or additional treatments in the clinic.

Response: Thank you for your interesting suggestion. It is indeed an excellent and futuristic suggestion to use molecular correlations to performance for patient selection and additional treatments. Unfortunately, as stated in the answers above, such a speculation, as interesting and important it may be, would be too early and cannot be made based on our preliminary results. As stated above: However, where suitable, we have emphasized the neuroprotective potential of the different molecules since we speculate that specific factors may be released upon damage / hearing loss in the inner ear that serve to preserve neuronal function alongside the auditory pathway. This is very speculative and thus we have not included this speculation to the manuscript.

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Reviewer #1: Yes: Philip Antony Bird

Reviewer #2: Yes: Yehoash Raphael

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

When submitting your revision, we need you to address these additional requirements.

Response: We checked the references for correctness. We added 5 new references and refreshed one:

Reference list number 2 (refreshed)

Reference list number 19

Reference list number 20

Reference list number 31

Reference list number 39

Reference list number 40

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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Response: We made some formatting changes due to the style requirements of the journal. We changed the manuscript into grouped style and integrated the figures in the running text.

2. You indicated that you had ethical approval for your study. In your Methods section, please ensure you have also stated whether you obtained consent from parents or guardians of the minors included in the study or whether the research ethics committee or IRB specifically waived the need for their consent.

Response: n the case of children undergoing CI surgery and perilymph sampling we obtained the consent of parent or legal guardian. We added this information in the methods section.

3. In your Methods section, please provide additional information about the participant recruitment method and the demographic details of your participants. Please ensure you have provided sufficient details to replicate the analyses such as: a) a description of any inclusion/exclusion criteria that were applied to participant recruitment, b) a description of how participants were recruited, and c) descriptions of where participants were recruited and where the research took place.

Response: The demografic data of the study participants are depicted in detail in Table 1 – 3. The Tables were now integrated in the running text as required in the style requirements of the journal.

Additionally we included following information in the methods section:

The inclusion criteria for perilymph sampling is the planned procedure of a CI surgery. Every patient undergoing CI surgery has the ability to participate in the study/perilymph sampling and to sign the consent form for the study in parallel to the consent form for the CI surgery. The only exclusion criteria is the existence of any infectious disease of the patient or the impossibility of perilymph sampling due to the individual surgical procedure decided by the surgeon. The proteome analysis takes place in the Hanover Medical School.

4. Please ensure your Methods and reagents are described in sufficient detail for another researcher to reproduce the experiments described. Specifically, please ensure you have provided a brief description of perlymph sample preparation, or an appropriate reference for standard methods.

Response: In the methods section we refer to our previous publication (Proteome Analysis of Human Perilymph using an Intraoperative Sampling Method. Journal of Proteome Research. 2017 Mar 10;acs.jproteome.6b00986; Schmitt et al.). In this publication an detailed description of the sampling method, perilymph sample preparation and analysis is included.

5. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

6. We noticed you have some minor occurrence of overlapping text with the following previous publication(s), which needs to be addressed:

Response: - "Detection of BDNF-Related Proteins in Human Perilymph in Patients with Hearing Loss," doi: 10.3389/fnins.2019.00214

In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the methods section. Further consideration is dependent on these concerns being addressed.

Response: We added the missing citations in the methods section “proteomic analyses” and “statistical analyses”

Submitted filename: Responds to View Letter_PLOSONE_25-10-21.doc

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27 Jan 2022

Proteome profile of patients with excellent and poor speech intelligibility after cochlear implantation: Can perilymph proteins predict performance?

PONE-D-21-08371R1

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Reviewer #1: All comments have been addressed

**********

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**********

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Reviewer #1: Yes

**********

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Reviewer #1: Yes

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

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Reviewer #1: I'm satisfied that all of my suggestions and comments have been addressed. I am therefore happy for the paper to be accepted for publication.

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24 Feb 2022

PONE-D-21-08371R1

Proteome profile of patients with excellent and poor speech intelligibility after cochlear implantation: Can perilymph proteins predict performance?

Dear Dr. Warnecke:

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