Prevalence of hearing loss increases with age. Its estimated prevalence is 40–50 % in people over 75 years of age. Recent studies agree that declinein hearing threshold contribute to deterioration in sociality, sensitivity, cognition, and quality of life for elderly subjects.The aim of the study presented in this paper is to verify whether or not rehabilitation using first time applied Hearing Aids (HA) in a cohort of old people with hearing impairment improves both speech perception in a noisy environment over time and the overall health-related quality of life.The monocentric, prospective, repeated measurements, single-subject, clinical observational study is to recruit 100 older adults, first-time HA recipients (≥ 65 years).The evaluation protocol is designed to analyze changes in specific measurement tools a year after the first HA usage in comparison with the evaluation before HA fitting.Evaluations will consist of multiparametric details collected through self-report questionnaires completed by the recipients and a series of commonly used audiometric measures and geriatric assessment tools. The primary indicator of changes in speech perception in noise to be used is the Italian version of Oldenburg Satz (OLSA) test whereas the indicator of changes in overall quality of life will be the Assessment of Quality of Life (AQoL) and Hearing Handicap Inventory for the Elderly (HHIE) questionnaires. The Montreal Cognitive Assessment (MoCA) will help in screening the cognitive state of the subjects.The protocol is designed to make use of measurement tools that have already been applied to the hearing-impaired population in order to compare the effects of HA rehabilitation in the older adults immediately before first HA usage (Pre) and after 1 year of experience (Post). This broad approach will lead to a greater understanding of how useful hearing influences the quality of life in older individuals, and therefore improves potentials for healthy aging. The data is to be analyzed by using an intrasubject endpoint comparison. Outcomes will be described and analyzed in detail.This research was retrospectively registered underno. NCT04333043at ClinicalTrials.gov  on the 26 March 2020. This research has been registered with the Ethics Committee of the Area Vasta Emilia Nord under number 104, date of approval 17/07/2017. Hearing loss is one of the most common disabilities in the world. Prevalence of this disease increases with age and its estimated prevalence is 40–50 % in people older than 75 years .The term presbyacusis refers to decreased hearing sensitivity in older adults subjects. Nevertheless, this term has a broader meaning including all types of hearing loss that occur in the older adults. Presbyacusis also involves genetic age-related cumulative effects and potential secondary injuries to the auditory system .Presbyacusis is the main cause of stable hearing loss and the most common disablingdisease in the older adults. .Presbycusis is characterized by a bilateral symmetrical neurosensorial hearing loss that initially involves high frequencies and secondarily medium and low frequencies. Auditory threshold modifications are related to variations in detection, localization and speech discrimination especially in noisy environments , 4. ThesHowever, cognitive abilities can benefit from the hearing-impaired subject using a hearing aid (HA). It is clear that hearing loss and cognitive abilities are linked and influence each other , 9.Periodic monitoring of the hearing threshold is essential in early identification of communication disability or handicap, and the initiation of early treatment.Since presbycusis is a multifactorial disease, the decline of the auditory threshold can be reduced by limited noise exposure, medical treatment in case of systemic or ear diseases or, in most cases, by using a HA –12.Various studies have shown that HA improve quality of life (QoL) and cognitive functions in patients with presbycusis , 13–15. In most studies, HA benefit is investigated using traditional clinical audiometric tests or self-assessed outcomes . Only a In a recent study, Tognola et al. investigated HA benefit in the older adults using complete multi-parametric protocol. This study shows a significant correlation between auditory outcomes, hearing impairment, and cognition but it examined subjects once (a year after the first fitting of HA) and it did not investigate the role played by HA use in maintaining long-term health outcomes. .The present paper describes the first application of the protocol design of a monocentric prospective longitudinal study in a large cohort of older adults with HA. Its unique features include severaltests that include evaluation of hearing aid benefit in a noisy environment and simultaneous assessments of the long-term evaluation of the mental, psychosocial, and QoL domains.The primary aim of this study is to identify whether or not there is any significant improvement in speech recognition in noise in older adults patients ( > = 65 years) who are using HA for the first time. This improvement is to be verified by using a Signal to Noise Ratio (SNR) reduction which will be evaluated using the OLSA test.The secondary aim is to process the correlations between the use of the HA and quality of life and cognitive status in older adults. Any improvement or deterioration can be seen in higher or lower scores in auto-assessment questionnaires.This is a prospective observational, repeated measurement, single-subject design study.Changes in speech recognition by noise score, health-related quality of life, and overall well-being are assesses.The study design is monocentric and is conducted at the outpatient service of the ENT Department of the “Saliceto” Hospital in Piacenza, Italy.All the materials used are appropriate for the italian language.This study is registered with the Ethics Committee of the Area Vasta Emilia Nord under number 104; date of approval 17/07/2017.The study protocol assessments coincides with the routine clinical examinations. Full test-battery and questionnaires are administered in both examinations: one before the HA fitting (Pre) and the other after one year of HA use (Post).A short description of the procedure is shown in Table Baseline (t0):Signature of the Patient giving Informed Consent..First assessment (Pre):otoscopy and cleaning of the external auditory canal (if required).unaided pure tone audiometry (right and left ear).unaided speech audiometry in quiet (right and left ear).Italian version of Oldenburg Satztest (OLSA test) .Self-assessment questionnaires:Montreal Cognitive Assessment (MoCA) .Hearing Handicap Inventory in the Elderly Screening test (HHIE-S) .(AQoL) [Assessment of Quality of Life (AQoL) .Aid Benefit (APHAB) [Abbreviated Profile of Hearing  (APHAB) ..Second assessment (Post):otoscopy and cleaning of the external auditory canal (if required).test of the HA functioning.unaided pure tone audiometry (right and left ear).aided speech audiometry in quiet (in free field).Italian version of OLSA test.Self-assessment questionnaires:MoCA.HHIE-SAQoL.International Outcome Inventory for Hearing Aids (IOI-HA) .APHAB in aided condition.To complete all the measures takes approximately one hour: 30 minutes for performing the standard self-report questionnaires and 30 minutes for the other tests.Study subjects who use unilateral or bilateral hearing aids for the first time are to be included. Eligible participants are consecutively identified on the HA registry of the Local Unit of the National Health System (NHS). The Italian NHS is a welfare system which totally or partially funds the HA costs of the patients selected.Subjects are > = 65 years old using unilateral or bilateral HA for the first time. According to local NHS threshold-criteria for funding adult HAs, the best ear of the patients must have a Pure-Tone Average (PTA) > = 45 dB HL. PTA is the average air-tonal threshold at 500, 1000, 2000, and 4000 Hz frequencies.Only the subjects who signed the Patient Informed Consent Form prior to the first assessment  are eligible for the clinical investigation.Approximately 100 individuals will be included. The sample size of 100 people was recruited according to a previous preliminary study by the authors  and depending on olderpopulation eligible to utilize HA. Indeed, eligible participants are consecutively identified in the HA registry of the Local Unit of the National Health System (NHS).Inclusion:Age > = 65 years.First use of unilateral or bilateral HA.HA partially or totally funded by the Italian National Health System.Willingness to participate in.Willingness to comply with all study procedures.Fluency in italian language used to assess clinical performance.Able to decide on study participation personally.Able to independently sign their consent..Exclusion:Unilateral hearing loss.Previous use of HA.Significantly/severely dependent or fragile.Unable to provide consent personally.Unable to complete questionnaires for self-assessment independently.Significant comorbidities preventing study participation.Subjects with unrealistic expectations on benefits, risks and limitations concerning the procedure and prosthetic device..Changes in the hearing aid benefit and the overall health status of the older adults at post-HA use are evaluated by a selection of observational clinical assessment tools frequently used in audiology and/or geriatric practices.All questionnaires have been validated for the Italian language.Expert personnel have been designated to manage the administration of the questionnaires in order to understand whether or not the subject clearly understands what the task involves.A short description of the administered assessments is listed alphabetically below (see Table Abbreviated profile of Hearing Aid Benefit (APHAB) is the abbreviated version of the Profile of Hearing Aid Benefit (66 items).APHAB is a self-assessment questionnaire composed of 24 questions that assess the advantage and the communicative problems of HA use .The data obtained provides information about several typical workday situations considering four subscales: Ease of Communication, Reverberation, Background Noise, and Aversiveness of Sounds.A low APHAB score indicates better performances. A measurement of the benefit will be calculated by subtracting APHAB scores in Post evaluation (with HA) from Pre evaluation.https://harlmemphis.org/wp content/uploads/2020/05/ITALIAN.pdf) [The Italian translation of the APHAB questionnaire is available on the Hearing Aid Research Lab (HARL) web site at (IAN.pdf) .The Assessment of Quality of Life (AQoL) was designed for use across health conditions to enable evaluation studies of health economics. AQoL assesses the impact of interventions on health-related quality of life by comparing different disease settings and monitoring longitudinal changes in a broad range of health conditions –28.The AQoL-8D consists of five psychosocial and three physical dimensions. The three physical dimensions are related to a single construct  and the five psychosocial dimensions are related to a single construct .The test includes 35 items with 5 possible answers.www.aqol.com.au) [The Italian translation of the AQoL questionnaire is available on the AQoL web site (.com.au) .These include standard threshold measurements for frequencies of 250–8000 Hz (pure tone audiogram) and speech discrimination (speech audiometry) in a quiet situation. The pure tone audiometryis performed in a soundproof room using a Madsen Astera  audiometer and TDH39 supra-aural earphones.The pure tone audiometryunder earphone is assessed in Pre and Post evaluations, and in unaided (dB HL) condition. Speech audiometry is assessed in Pre-evaluation under earphone whereas it is assessed in the free field in aided (dB SPL) condition in Post evaluation.Speech audiometry shows the Speech Reception Threshold (SRT), defined as the level of speech corresponding to 50 % of correct answers. In the SRT test, the speech stimuli (list of disyllabic words)  are pre-The subject is asked to repeat a list of 20 words  and the percentage of correctly repeated words is evaluated for each intensity-level presented. The intensity ranges between 30 and 80 dB HL and every word list is presented at an intensity of 10 dB higher than the previous one. An intelligibility curve is obtained and SRT is derived by curve interpolation.The Hearing Handicap Inventory for the Elderly Screening Version (HHIE-S) is a self-assessment scale developed to assess the effects of hearing impairment on emotional and social adjustment in everyday life of the older adults –33.This test contains a 10-item questionnaire. A low HHIE-S score indicates fewer perceived problems.The Italian version of this material is under publication .The International Outcome Inventory for Hearing Aids (IOI-HA) is a self-report questionnaire developed to quantify the satisfaction of hearing aid users and the impact these devices have on their lives. The IOI-HA contains seven domains:(1) the time for whichHAhave been used;(2) benefit; (3) residual limitation in daily life activities; (4) satisfaction; (5) residual restrictions to participation; (6) impact on other people; (7) quality of life. The answers to each question range from poor performance (1) to best performance (5) , 35.A high score is correlated with a good outcome in aided conditions.Cox et al. were responsible for the Italian validation of this questionnaire .Montreal Cognitive Assessment (MoCA) is a cognitive screening instrument used to detect mild cognitive impairment, a high-risk condition for dementia forms , 36–38.It is composed of 12 subtasks assessing different cognitive skills, including short-term and delayed verbal memory, executive function, and attention. The total MoCA score ranges from 0 (worst performance) to 30 (best performance). Mild cognitive dysfunction is suspected when the final score is less than 26.Different strategies will be integrate by clinicians in order to avoid a misdiagnosis or overdiagnosis of cognitive impairment. Personnel will be trainedto administrationof this tool in patients with hearing loss. In particular, they will assess the correct use of hearing aids; they will ensure that the test environments meet standards for ambient noise levels and they can use alternative presentation modality of the items with auditorycontents  .SantangeThe OLSA test  is a versatile examination that is structured into two randomized lists of 30 sentences of five-word, semantically unpredictable . This toThe test yields the Signal to Noise Ratio (SNR) measurement in dB at which the subject recognizes 50 % of the words presented.The test is administered using the S0N0 presentation setup .The background noise is presented at 65 dB SPL whereas the speech level is adaptively adjusted depending on the subjects’ response to obtain the SNR.Puglisi et al. (2015) described the reference ranges and standard deviations of the OLSA test for the Italian language by determining different levels to identify at-risk patients .The cutoAn intrasubject endpoint comparison is to be used for primary and secondary study objectives. The pairwise comparisons are of interest: previous use of HA (Pre)-to-12 months HA use (Post).Investigation will apply multivariate exploratory factor analysis to reduce the number of variables and identify possible latent factors, followed by Spearman correlation to analyze the relationships between the variables and the factors.Finally regression analysis will apply to investigate how factors and variables relate to each other and how they predict outcomes.Data will be analyzed by nonparametric tests for categorical variables and by t-test for continuous variables.Questionnaires analysis will be performed by using a nonparametric test.This study aims to show the potential positive effect of HAs on auditory skills and on quality of life of older adults with hearing loss. In particular, the efficacy of HA in speech perception in noisy environments and the improvement of quality of life in the older adults using HA for the first time is to be evaluated. The broad approach will lead to a greater understanding of how hearing impacts the quality of life in older adults thereby improving healthy aging.Since the older adults are increasing in number in the Italian population, a greater number of hearing aids will be required to treat this growing presbycusic population. This will strongly affect the finances of the National Health System, which bears the full or partial cost of HA in most cases. This is also a good reason to understand the real benefits of the HA application clearly. The study outcomes are intended to provide transparent and comparable evidence-based information for healthcare policymakers by supporting the provision of health services for the treatment of hearing loss.The comprehensive protocol considers a range of widely accepted, interrelated metrics associated with aging in addition to functional hearing. The use of this protocol does not propose the investigation of causal effects but rather investigation of the pre-post HA candidates by observing long-term health outcomes.The primary endpoint of this study is to identify significant improvement in speech recognition in noise. The percentage of subjects with SNR > 1.5 dB at OLSA test is to be evaluate in order to verify the endpoint.The OLSA test is the tool most frequently used to find the auditory outcome in noisy environment in the majority of the recent literature. Use of this test has overcome the traditional speech audiometry because it uses sentences consisting of a matrix of 5 semantically unpredictable words and is based on an adaptive method. This has overcome the memory capacity of HA users who perform speech audiometry several times at follow up and are able to learn the words in the lists. Furthermore, in contrast to speech audiometry with sentences of common use that have a semantic context, the OLSA test does not have a predictable semantic context.During this study two-time point, tonal unaided audiometry was used to check any auditory threshold modification and worsening of the audiometric clinical situation over time. Speech audiometry in unaided conditions in the first assessment and in aided conditions in the second assessment were used because it describes the sample and allows it to be compared to the existing literature.The second objective of this study is based on the evaluation of possible improvements in the overall health-related quality of life in the sample.To verify this aim, self-report questionnaires were chosen, which are essentially non-verbal and consequently remove challenges directly associated with utilizing new HA. The percentage of subjects who improve the questionnaire score using HA (second assessment) compared to the first evaluation in unaided conditions is to be evaluated.Improvement in quality of life can be evaluated by using different questionnaires. The AQoL questionnaire is used in this study because of its robust psychometric characteristics and wide diffusion in the health-related quality of life literature. In a review by Mihalopoulos et al.,  report that AQoL-8D “had the highest correlation with the disease-specific measurements and the best goodness-of-fit transformation properties” .The HHIE has been included in the study in order to better understand the effects of hearing impairment on emotional and social adjustment in everyday life. It is one of the most widely applied questionnaires with respect to auditory participation and has been reported with both HA and cochlear implant recipients . A signiThe MoCA questionnaire was chosen to screen the cognitive abilities of subjects. The use of this questionnaire will exclude deficits in these abilities. Using this test in Pre and Post evaluation will verify that cognitive abilities do not affect other questionnaires and HA results. The use of the MoCA questionnaire in the Pre and Post evaluation will exclude a reduction of the cognitive abilities over time. A reduction of these abilities can be a sign of the deterioration of the general state of health that may affect the execution of the other questionnaires used. The same score is expected in Pre and Post evaluation of the same subject.The APHAB questionnaire is used to assess problems in speech understanding in different listening situations. This self-assessment is one of the most used questionnaires in clinical practice. An improvement in the scores in Post evaluation compared to Pre evaluation will verify a reduction of listening problems secondary to HA usage.The IOI-HA questionnaire is only used in the Post evaluation. It will help understand the difficulties and benefits of HA use perceived by the patients. In particular, this questionnaire will give us a picture of the HA outcomes in different areas. Furthermore, being a standardized questionnaire, it will facilitate comparison of the sample with the existing literature.The strength of this study lies in evaluating the auditory and health outcomes over time which contrasts with most of the recent studies that show an overview of the outcomes at a precise moment and various other biased sources.Furthermore, as reported by Hanratty and Lawlor, people over 70 are thought to have a hearing impairment that would benefit from a HA but a high percentage of these people probably never use their aid . The stuThese outcomes should contribute to providing key actors of the health system with means of enhancing their part in a collective endeavor targeting the best care and quality of life for older adults citizens because general well-being translates into healthy aging.The single-subject, repeated-measures design allows for subjects to serve as their own controls, and thus increases statistical power. The relatively large population size helps provide a good estimate of effect size and would make the results broadly applicable.


Noise-induced hearing loss (NIHL) is a substantial, often unrecognized, health problem. Various learning environments and activities in school settings are loud. Researchers have reported the prevalence of NIHL among U.S. adolescents ranging between 12.8% and 17.5%, suggesting that one in every six to eight middle and high school students (aged 12–19 years) has measurable hearing loss likely resulting from excessive noise exposure  and school districts (61.3%) require students to use HPDs during classes or activities in which they are exposed to potentially unsafe noise levels  and then adjusted for the following seven factors: youth gender, youth age, household income, youth race/ethnicity, number of teenagers in the household, census region, and metro status. Personal identifiers were not included in the data file. Three questions were included related to this study. Participants were asked to indicate their responses with a forced choice scale . For analysis, researchers dichotomized student answers into two categories for student exposure (every day or two to four times per week/never or every few months), provision of HPDs , and hearing protection education . Analyses were conducted using SAS software . The PROC SURVEYFREQ procedure of SAS was used for descriptive analysis. Multivariable logistic regressions were used to calculate adjusted odds ratios (aORs), 95% confidence intervals (CIs), and p-values (α = 0.05).A total of 817 youths  qualified and completed the survey, for a response rate of 48.1%. Among the youths surveyed, 73.6% reported exposure to loud sound at school for >15 minutes a day and nearly one half (46.5%) reported exposure every day or two to four times per week . Of thosThis study suggests that approximately three in four students are exposed to loud sounds at school with nearly one half (46.5%) exposed on a routine basis. Among the students reporting exposure , 85.9% reported that they were not provided hearing protection during class or activities where they were exposed. Among all students responding, fewer than one out of three (29.6%) reported being taught how to protect their hearing during noisy events or activities. A loud sound level in a classroom is not just an annoyance; it can also disrupt academic performance and educational activities  report exposure on a regular basis. However, most report that their school did not provide hearing protection equipment or teach preventive techniques to reduce their risk of permanent noise-induced hearing loss.Health care providers and educators have resources available to prevent noise-induced hearing loss among school-aged children. Increasing youths’ awareness about adverse health effects of excessive noise exposure and simple preventive measures to reduce risk can help prevent or reduce noise-induced hearing loss.


Fabaceae plants appear to contain larger numbers of subclade IVa basic-helix-loop-helix (bHLH) transcription factors than other plant families, and some members of this subclade have been identified as saponin biosynthesis regulators. We aimed to systematically elucidate the diversification of this subclade and obtain insights into the evolutionary history of saponin biosynthesis regulation in Fabaceae.Glycine max, Medicago truncatula, and Lotus japonicus, orthologues of MtTSAR1 (the first identified soyasaponin biosynthesis regulatory transcription factor) were not expressed in the same tissues, suggesting that group 1 members have gained different expression patterns and contributions to saponin biosynthesis during their duplication and divergence. On the other hand, groups 2 and 3 possessed fewer members, and their phylogenetic relationships and expression patterns were highly conserved, indicating that their activities may be conserved across Fabaceae.In this study, we collected sequences of subclade IVa bHLH proteins from 40 species, including fabids and other plants, and found greater numbers of subclade IVa bHLHs in Fabaceae. We confirmed conservation of the bHLH domain, C-terminal ACT-like domain, and exon-intron organisation among almost all subclade IVa members in model legumes, supporting the results of our classification. Phylogenetic tree-based classification of subclade IVa revealed the presence of three different groups. Interestingly, most Fabaceae subclade IVa bHLHs fell into group 1, which contained all legume saponin biosynthesis regulators identified to date. These observations support the co-occurrence and Fabaceae-specific diversification of saponin biosynthesis regulators. Comparing the expression of orthologous genes in This study suggests subdivision and diversification of subclade IVa bHLHs in Fabaceae plants. The results will be useful for candidate selection of unidentified saponin biosynthesis regulators. Furthermore, the functions of groups 2 and 3 members are interesting targets for clarifying the evolution of subclade IVa bHLH transcription factors in Fabaceae.The online version contains supplementary material available at 10.1186/s12870-021-02887-w. Avena produce antifungal saponins, known as avenacins in the roots for protection against take-all disease . Although the soyasaponin pathway is common among Fabaceae, only Medicago spp. acquired the hemolytic pathway. Fig. S6. Expression of LjCYP93E1 and LjbHLH032. Data retrieved from Lotus Base. Lj1g3v3555800: LjCYP93E1; Lj0g3v0292969: LjbHLH032.


The progression and asymmetry of age-related hearing loss has not been well characterized in those 80 years of age and older because public datasets mask upper extremes of age to protect anonymity. We aimed to model the progression and asymmetry of hearing loss in the older old using a representative, national database. This was a cross-sectional, multicentered US epidemiologic analysis using the National Health and Nutrition Examination Study (NHANES) 2005-2006, 2009-2010, and 2011-2012 cycles. Subjects included non-institutionalized, civilian adults 80 years and older (n=621). Federal security clearance was granted to access publicly-restricted age data. Outcome measures included pure-tone average air conduction thresholds and the 4-frequency pure tone average (PTA). 621 subjects were 80 years old or older , representing 10,600,197 Americans. Hearing loss exhibited constant acceleration across the adult lifespan at a rate of 0.0052 dB/year2 . Compounded over a lifetime, the velocity of hearing loss would increase five-fold, from 0.2 dB loss/year at age 20 to 1 dB loss/year at age 100. This model predicted mean PTA within 2 dB of accuracy for most ages between 20 and 100 years. There was no change in the asymmetry of hearing loss with increasing age over 80 years . In conclusion, hearing loss steadily and predictably accelerates across the adult lifespan to at least age 100, becoming near-universal. These population-level statistics will guide treatment and policy recommendations for hearing health in the older old.


Drosophila melanogaster as a model to investigate the sensory comparisons that govern a motor sequence. Flies perform grooming movements spontaneously, but when covered with dust, they clean their bodies following an anterior-to-posterior sequence. After investigating different sensory modalities that could detect dust, we focus on mechanosensory bristle neurons, whose optogenetic activation induces a similar sequence. Computational modeling predicts that higher sensory input strength to the head will cause anterior grooming to occur first. We test this prediction using an optogenetic competition assay whereby two targeted light beams independently activate mechanosensory bristle neurons on different body parts. We find that the initial choice of grooming movement is determined by the ratio of sensory inputs to different body parts. In dust-covered flies, sensory inputs change as a result of successful cleaning movements. Simulations from our model suggest that this change results in sequence progression. One possibility is that flies perform frequent comparisons between anterior and posterior sensory inputs, and the changing ratios drive different behavior choices. Alternatively, flies may track the temporal change in sensory input to a given body part to measure cleaning effectiveness. The first hypothesis is supported by our optogenetic competition experiments: iterative spatial comparisons of sensory inputs between body parts is essential for organizing grooming movements in sequence.Animals integrate information from different sensory modalities, body parts, and time points to inform behavioral choice, but the relevant sensory comparisons and the underlying neural circuits are still largely unknown. We use the grooming behavior of  Drosophila covered with dust compare sensory inputs from mechanosensory bristles on different body parts during grooming. The ratio of anterior:posterior sensory input and its dynamics, rather than the rate of dust removal from the anterior, drives the anterior-to-posterior grooming sequence.Zhang et al. find that  Drosophila melanogaster  insertion , a(t), and dr. d(t) represents the amount of dust on each body part. a(t) represents the sensory input induced by dust. a(t) follows a normal distribution whose mean is equal to current d(t). dr indicates the dust removal rate, or the percentage of dust that is transferred from a body part to the legs in each grooming bout. It is defined as a percentage of the current d(t) rather than a constant amount to capture diminishing returns–less dust is removed when there is less on a body part. The initial value of d(0) and the constant value for dr are specified by the user, and first iteration selects the body part with the highest a(0) to be groomed. d(t) is re-calculated in each iteration, using dr applied to the currently selected body part to reduce d(t) and determine new values of a(t) for each body part. The winner-take-all layer then compares the updated sensory input level a(t) to select the next grooming action : when the A:P sensory input ratio was high, anterior grooming always dominated, and when the A:P sensory input ratio was low, posterior behaviors dominated over the whole time course.Flies remove dust particles from specific body parts during grooming. In our model, we simulated this by including the term gression . The proBristle-spGAL4–1, constant optogenetic stimulus for 14 min (in undusted flies). The A:P grooming ratio stayed similar over time  Faster sensory input change reduces the A:P sensory input ratio faster. Flies may frequently compare the levels of sensory input to anterior versus posterior body and switch when they become close to equal. Animals respond to the absolute level of sensory inputs, but they also monitor how these sensory inputs change over time , 6, 49. R74C07-GAL4 labels more eye bristle neurons compared with abdominal bristle neurons, which may explain why lower anterior light intensity compared with posterior light is required to reach equilibrium. The different half-times of 90 s versus 125 s  in competition with either low (dark blue) or high (light blue) posterior stimulation levels . In bothus 125 s  also indNext, we used this equilibrium point (30μW:52μW)  and 6C tThe anterior-to-posterior grooming transition occurs when the sensory input ratio reaches a certain threshold–but does the history matter at all? We tested whether reversing the ramp of anterior stimulation would alter the transition point, starting with low anterior illumination and increasing it to approach the equilibrium point  from below at different time points, using various slopes . We founDrosophila grooming. We found that multiple types of mechanosensory organs are involved in grooming, but mechanosensory bristles are most essential for grooming sequence: their activation induces the anterior-to-poster grooming progression and cyclic switching between body cleaning and leg rubbing. Electrophysiology recordings have shown that a mechanosensory bristle can respond to displacements as small as 100nm [Drosophila remove debris, increase sensory acuity, and restore proper position of body parts, so it is an appropriate response to mechanosensory stimulation. Future work will be required to determine the exact mechanism of dust sensing by different mechanosensory organs.The neural mechanisms for processing sensory signals and the way this information is used to select among behaviors remain open questions. Evaluating which kinds of sensory inputs can initiate a behavior is the first step in understanding this process. In this work, we systematically investigated the role of different types of sensory organs in as 100nm . Therefoas 100nm , 25. ThuBehavioral analyses suggest challenges the nervous system solves. For grooming, the presence of a somatotopic map can be inferred because of the precision with which the legs move to sweep stimulated bristles . Some abThe CNS integrates information from different sensory modalities and body parts. Our experiments show that during grooming, flies frequently compare sensory input strengths from anterior and posterior body parts to choose grooming actions. Mechanosensory bristle and proprioceptive neurons in the leg extend axons into distinct areas of the leg neuropils of the ventral nerve cord. Bristle neurons from the body also project to the ventral nerve cord and abdominal bristle neurons arborize in the abdominal ganglia , whereasModeling guided experimental tests using our optogenetic competition assay. We determined which spatial and temporal comparisons matter for behavior choice. For grooming, we now know that comparisons between mechanosensory bristle neurons on anterior and posterior body parts are critical. Using a combination of anatomically guided selection of genetic reagents and behavioral screening, we previously mapped much of the neural circuitry controlling antennal grooming . Our futjhsimpson@ucsb.edu). This study did not generate new unique reagents.Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Julie H. Simpson  and converted into single image through Extended Depth of Field plugin (http://bigwww.epfl.ch/demo/edf/).Eye photos of male flies were taken through an SZX 12 Olympus stereomicroscope at different Z positions. Z series for each fly were registered through BUnwarpJ (Three chambers were used in fly dusting assay: dusting chamber (24 well corning tissue culture plate #3524), transfer chamber and recording chamber. Dust-induced grooming assays were performed in 21–23°C. 4–7 days male flies were anesthetized on ice and transferred to the middle four wells of transfer chamber. 10-day old males were used in Kir inhibition experiments to increase the expression level of Kir. Flies were left in transfer chamber for 15 min to recover. Around 5 mg Reactive Yellow 86 dust was added into each of the 4 middle wells of dusting chamber. Before use, dust was baked in a 160°C oven overnight to remove extra moisture. For fly dusting, transfer chamber was aligned with dusting chamber. Flies were tapped into dusting chamber and shaken for 10 times. After dusting, flies and dust were transferred back into transfer chamber. Transfer chamber was banged against an empty pipette tip box to remove extra dust. Dusted flies were then immediately tapped into recording chamber for video recording. The whole dusting process was performed in a WS-6 downflow hood. As undusted control, flies with the same genotype were shaken in chambers without dust. At least 10 individuals were recorded for each genotype.30 Hz videos were recorded for 50,000 frames 27.78 min) with a DALSA Falcon2 color 4M camera. A white LED ring right was used for illumination. Infrared backlight was used for grooming experiments in the dark. Videos were processed through ABRS to generate ethograms. Grooming modules were described previously [.78 min wControl-spGAL4 (8.4 mW/cm2), Bristle-spGAL4–1 (0.84 mW/cm2), Bristle-spGAL4–2 (0.84 mW/cm2), Wing+haltere CS-spGAL4 (5.6 mW/cm2), Control-GAL4 (8.4 mW/cm2), CO-GAL4 (1.4 mW/cm2), SR-GAL4 (8.4 mW/cm2), HP-GAL4 (5.6 mW/cm2), MD-GAL4 (5.6 mW/cm2), Antennal CO-spGAL4 (5.6 mW/cm2), R21D12-GAL4 (8.4 mW/cm2), R73D10-GAL4 (5.6 mW/cm2), R86D09-GAL4 (5.6 mW/cm2), VT028607-GAL4 (8.4 mW/cm2), R14F05-GAL4 (8.4 mW/cm2), Gr33a-GAL4 (5.6 mW/cm2), Gr64f-GAL4 (5.6 mW/cm2), Ppk28-GAL4 (5.6 mW/cm2), Orco-GAL4 (5.6 mW/cm2), Or56a-GAL4 (5.6 mW/cm2), Control-LexA (5.6 mW/cm2), R42G12-LexA (5.6 mW/cm2). 30Hz videos were recorded by IDS UI-3370CP-C-HQ camera and manually annotated in VCode or automatically annotated by ABRS (https://github.com/AutomaticBehaviorRecognitionSystem/ABRS).After cold anesthesia, flies were left to recover in recording chamber for at least 20 min. Custom-made LED panels  were used for light activation from below. 20 Hz 20% light duty cycle was used in all experiments. LED power was adjusted according to the expression level and behavioral response of different lines. Light intensity was measured by Thorlabs S130VC power sensor coupled with PM100D console. The light intensity used in the experiments are: R52A06 > ChrimsonR flies, 20 Hz 20% light duty cycle was used for anterior light stimulation, 20 Hz 50% light duty cycle was used for posterior light stimulation. 20 Hz 20% light duty cycle was used for both anterior and posterior light stimulations in R74C07 > ChrimsonR flies. Each fly was tested in two different light conditions. The order of light conditions was random. 20 min recovery time was given between different conditions. 30Hz videos were recorded with a Point Grey BFS-U3–13Y3M-C camera and manually annotated in VCode.Experimental rig was set up as protocol described previously , 44 withd(t) stands for dust amount on different body parts. For simulation of Canton S flies, d(t) was set up according to mechanosensory bristle numbers on different body parts. Initial dust on front legs and back legs was set to be 200. a(t) represents neural activities induced by dust. It follows a normal distribution whose mean is d(t), the relationship between d(t) and s(t) is estimated according to the bristle electrophysiology recordings [cordings :a(t)~N(abody part(t)) as the winner. The winner body part will be groomed in this grooming iteration. If the winner is leg, some percent of dust (10dr) will be discarded. Otherwise, some percent of dust (dr) will be transformed from winner body part to the corresponding legs:Winner-take-all layer determines the body part which has the highest neural activity  function. Each dot is one fly. The mean is shown as a blue line, 95% confidence intervals for the mean are showed as dark shades. The median is shown as a dotted red line. 1 standard deviation is shown as light color shade.Data was plotted with notBoxPlot (https://github.com/raacampbell/shadedErrorBar) function was used for grooming progression figures. For dusting experiments or model simulations. Behavior probabilities were calculated every 16 s in a sliding 32 s time window. For optogenetic experiments with 5 s and 1min light activation, behavior probabilities were calculated every 2.5 s in a sliding 5 s time window. For optogenetic experiments with 5min and 14min light activation, behavior probabilities were calculated every 5 s in a sliding 10 s time window. Each data point is the average among all individuals. The shade stands for the standard error of mean.shadedErrorBar  fly performed anterior or posterior grooming for that interval. If the fly did not perform any grooming behavior during that period. That time point for the fly was discarded from further analysis. Otherwise, the log ratio of anterior grooming to posterior grooming was calculated as following:https://data.mendeley.com/datasets/fxz8dgywcd/1.Ethogram data, modeling code and an example video for fly-on-a-ball experiment are available at 2


Previous studies identifying hearing loss as a promising modifiable risk factor for cognitive decline mostly adjusted for baseline age solely. As such a faster cognitive decline at a higher age, which is expected considering the non-linear relationship between cognition and age, may have been overlooked. Therefore it remains uncertain whether effects of hearing loss on cognitive decline extend beyond age-related declines of cognitive function.3,590 non-demented participants were eligible for analysis at baseline, and a maximum of 837 participants were eligible for the longitudinal analysis. Hearing loss was defined at baseline. Cognitive function was measured at baseline and at follow-up (4.4 years [SD: 0.2]). Multivariable linear regression analysis was used for the cross-sectional analysis. Linear mixed models were used to assess the longitudinal association between hearing loss and cognitive decline over time while adjusting for confounders and the interaction of age and follow-up time.Hearing loss was associated with lower cognitive function at baseline. Moreover, hearing loss was associated with accelerated cognitive decline over time on a memory test. After additionally adjusting for the interaction between age and follow-up time, we found that hearing loss did not accelerate cognitive decline anymore.Hearing loss was associated with lower cognitive function at baseline and accelerated cognitive decline on a memory test. The association between hearing loss and accelerated cognitive decline was non-significant after additional adjustment for non-linear age effects. More evidence is needed to ensure the role of hearing loss as a modifiable risk factor for cognitive decline.The online version of this article (10.1007/s00415-020-10208-8) contains supplementary material, which is available to authorized users. Recently, hearing loss has been put forward as a promising modifiable risk factor for cognitive decline and dementia , 32, 45.Several longitudinal studies reported associations between hearing loss and poorer cognitive function , and witAgainst this background, we aimed to elucidate whether hearing loss accelerates cognitive decline over time in dementia-free participants whom are at risk of cognitive decline and cognitive impairment from a large population-based study. We measured hearing loss, including speech understanding, and repeatedly assessed cognitive functioning with comprehensive cognitive testing. We examined whether trajectories of cognitive decline differed across degrees of hearing impairment while adjusting for potentially strong effects of age.This study is embedded in the Rotterdam Study, a prospective, population-based cohort study. The Rotterdam Study was initiated in 1989 and investigates determinants and consequences of aging. Details of the study have been described previously . The entN = 83), participants with a history of dementia or those who were insufficiently screened for dementia at baseline (N = 51), and participants who developed dementia during follow-up (N = 15), leaving 3590 participants with baseline hearing assessment. From those 3590 participants, data were available on different cognitive tests, namely the MMSE (N = 3584), the Stroop test (N = 3500), the Word Fluency test (WFT) (N = 3536), the Letter Digit Substitution test (LDST) (N = 3507), the Word Learning test (WLT) (N = 3239), and the Purdue Pegboard test (PPT) (N = 3264). There were 3498 participants with both data on hearing thresholds and speech understanding in noise.In total, 3739 participants underwent baseline hearing assessment (2011–2014). We excluded participants with probable conductive hearing loss  for the longitudinal analysis. At follow-up, 837 participants had data available for the MMSE, 764 participants for the Stroop test, 519 participants for the WFT, 780 participants for the LDST, 755 participants for the WLT, and 714 participants for the PPT. The mean time interval between cognitive baseline assessment and re-examination was 4.4 years (SD: 0.2). See supplementary methods for an explanation regarding the attrition rate.To determine hearing loss expressed by hearing thresholds in decibel (dB), pure-tone audiometry (PTA) was performed in a soundproof booth . A compuTo measure speech understanding in noise, we derived a signal-to-noise ratio  from the digits-in-noise (DIN) test, a 3-min test of speech understanding in noise . Both spCognitive function was assessed in detail with an extensive neuropsychological test battery comprising the MMSE, the Stroop test , the WFT , the LDST (number of correct digits within 60 s), the 15-WLT , and the PPT . The MMSE was administered during a home visit, the other tests were administered at the research center. All tests instructions were presented verbally. The MMSE is a validated screening tool for cognitive decline and cognitive impairment . The StrDuring home interviews, educational level was assessed and categorized as primary education, lower education, intermediate vocational education and higher education. Smoking habits were assessed during the same interview and subsequently classified into never, former and current smoking . AlcoholT tests, χ2 tests, and Mann–Whitney U Tests when appropriate. Subsequently, we present three sequential analyses to examine the association between hearing loss and cognition.We investigated whether baseline characteristics differed between participants with just a baseline assessment and participants with both a baseline and a follow-up assessment using 2, sex, education, alcohol consumption, smoking behavior, systolic- and diastolic blood pressure, and use of blood pressure lowering medication. All SNR analyses were additionally adjusted by PTA hearing levels for all frequencies.First, we assessed the cross-sectional association between hearing loss  and cognitive functioning at baseline using multivariable linear regression models. We adjusted for age, ageSecond, we used linear mixed models with random intercepts and slopes to elucidate the longitudinal association between degrees of hearing loss  and cognitive trajectories per test. In each model, we entered follow-up time in years after baseline measurement to use as time variable. For adjustment, we used the same models as described above. In a second model, a two-way interaction between age and follow-up time was added to account for possible slope differences for cognition over time, depending on the baseline age. All SNR analyses were additionally adjusted by PTA hearing thresholds. Next to the linear effects of hearing loss on cognition, an interaction of hearing loss and follow-up time was incorporated in all models, to allow slope differences in the relationship between cognitive functioning and time explained by degree of hearing loss. The linear hearing loss term (intercept difference) and the interaction term between hearing loss and follow-up time (slope difference) are the main terms of interest in this longitudinal analysis. For SNR analysis, random slopes were not included as the models failed to converge.Third, we performed similar linear mixed models to study the longitudinal association between hearing levels  and cognitive trajectories per test.g factor, by z transforming and averaging performance across each of the tests (except for the MMSE). Results were non-significant and effect estimates were smaller than those for the MMSE, indicating that the g factor in this sample cannot be considered as a more sensitive marker of global cognition than the MMSE. To facilitate interpretability and comparability (previous studies often used the MMSE), we chose to show the results for the MMSE and omit results regarding the g-factor from the final manuscript.In sensitivity analyses, we explored whether longitudinal associations between hearing levels and cognitive trajectories differed between men and women and between mid-life (51–70 years) compared to late life (70–99 years). Originally, the MMSE was designed as a cognitive screening tool and is therefore limited in its capability to truly measure global cognitive functioning . In an alme” function of the R-package “nlme” [p value < 0.05 was considered statistically significant.IBM SPSS Statistics version 25  and RStudio; integrated development environment for R, version 3.5.1.  were used for statistical analyses. Analyses with linear mixed models were done using the “e “nlme” . A p valTable Table In the first model, mild hearing loss showed statistically significant intercept differences, compared to normal hearing thresholds on the WFT, LDST, and the PPT Table . In lineMoreover, assessing hearing levels continuously showed that the additional change in cognitive functioning attributable to hearing loss were small and non-significant for both hearing thresholds and speech understanding in noise (Supplementary Table 2). Results did not differ between males and females or between midlife and late-life (Supplementary Tables 3 and 4).In this large population-based study in non-demented older adults at risk for cognitive decline and cognitive impairment, we found that hearing loss was associated with poorer global cognitive functioning, executive functioning, verbal fluency, attention, memory, and manual dexterity. After adjustment for the possible non-linear effects of age on cognitive change during follow-up, we did not find that hearing loss for either hearing thresholds or speech understanding in noise accelerates cognitive decline over time.N = 15), precluding the possibility to analyze whether hearing loss is associated with an increased risk of dementia in this sample.Strengths of this study are its prospective and longitudinal population-based design, the large sample size and the standardized assessment of hearing thresholds with pure-tone audiometry and a speech-in-noise test as well as cognitive functioning with comprehensive cognitive testing. However, the following limitations of this study must be considered. First, although we extensively adjusted for age and other important confounders, residual confounding might still be present. For example, frailty and psychosocial well-being may confound our results as those are known to be highly related to age-related hearing loss , 51. SecOur cross-sectional results were comparable with other studies, reflected in lower scores on cognitive tests with higher levels of hearing loss , 28, 37.In our longitudinal analysis we found an accelerated decline in memory function (as measured with the 15-WLT) with moderate hearing loss, which is comparable to the results and effect estimates of other population-based studies , 30. UneBesides elevated hearing thresholds, speech understanding in noise could contribute towards accelerated cognitive decline. The ability to understand speech in noise is a complex process in which elements of peripheral processing interact with more centrally located elements of auditory processing . As suchIt is also worthwhile considering whether found associations in our and previous studies might be driven by confounding and/or bias. The absence of an effect of hearing loss on cognitive decline in the current study is not explained by selection bias, as the sample with both a baseline- and a follow-up measurement were significantly older than the participants with just a baseline measurement. Moreover, it has been proposed that upstream common causes, i.e., inflammation, vascular pathology, and other systemic neurodegenerative processes, may lead to both hearing loss and cognitive decline through central nervous system-wide functional decline, rather than that those two are directly related to one another . As suchWe should also acknowledge that our follow-up time (mean = 4.4 years) may have been too short to capture a possible small, but significant effect of hearing loss on cognition. Epidemiological evidence has shown that elevated blood pressure in mid-life, an established modifiable risk factor of dementia, increases the risk of cognitive impairment 20–30 years later , 26, 49.In conclusion, hearing loss was significantly associated with compromised cognitive function and with accelerated decline on the 15-WLT measuring memory function. Notably, the latter association seemed to be driven by non-linear effects of age. Future, population-based studies are needed to further confirm the role of hearing loss as a potential modifiable risk factor for cognitive decline, whilst paying attention to effects of age on cognition. Even though more research is needed to strengthen evidence between hearing loss and accelerated neurodegeneration, our results do underline the great importance to acknowledge the effects of hearing loss (whether it being direct or indirect) on compromised cognitive function and associated general frailty within the elderly.Supplementary file1 (PDF 185 kb)Below is the link to the electronic supplementary material.


Horizontal Gene Transfer (HGT) is the process of transferring genetic materials between species. Through sharing genetic materials, microorganisms in the human microbiota form a network. The network can provide insights into understanding the microbiota. Here, we constructed the HGT networks from the gut microbiota sequencing data and performed network analysis to characterize the HGT networks of gut microbiota.Proteobacteria  and Actinobacteria ; children host highly abundant communities of Proteobacteria , and the mothers host highly abundant communities of Firmicutes  metagenome. The results indicated that (1) the HGT networks are scale-free. (2) The networks expand their complexities, sizes, and edge numbers, accompanying the early stage of lives; and microbiota established in children shared high similarity as their mother , supporting the transmission of microbiota from mother to child. (3) Groups harbor group-specific network edges, and network communities, which can potentially serve as biomarkers. For instances, IBD patient group harbors highly abundant communities of Hence, we proposed the HGT network constructions from the gut microbiota sequencing data. The HGT networks capture the host state and the response of microbiota to the environmental and host changes, and they are essential to understand the human microbiota. Human gut microbiota is a complex ecosystem consisting of a total of 10entclass1pt{minimabacteria , environbacteria , can leaResearchers have developed tools to detect HGTs. GIST  identifiNetwork science is a powerful tool to analyze and understand such complex systems. Various mathematical models are available to analyze and quantify such systems –22. In oWe apply the HGT networks to study two typical human gut microbiota datasets.The first is a 283-sample dataset of Mother-to-Child , nutrienBacteroidetes and decreasing of Firmicutes are observed in IBD patients [The second is a 148-sample dataset of longitudinal Inflammatory bowel disease (IBD) metagenome . The compatients . Since gpatients . The linpatients . HoweverProteobacteria  and Actinobacteria . Compared with mother, the phylum composition of child-specific HGT communities have significant decreasing of Firmicutes  [Mycobacterium, Sutterella, and Pseudomonas, compared to non-IBD individuals. Child-specific HGT edges are mainly from Bifidobacterium and Escherichia. These differences imply the alteration of gut microbiota caused by the change of selection pressure.Our results consist of three aspects. First, we investigate the general characteristics of the HGT network. HGT network is a complex network, and we used power-law distribution and three heavy tail distributions to fit the network. The result demonstrates that the HGT networks are scale-free, which implies the HGT network holds important properties such as ultra-small world property and robust to random disruption. Second, we studied the dynamic change of HGT networks, especially for the Mother-to-Child data set. The increasing complexity (Von Newman Entropy), network size, and HGT event rate in child HGT networks accompany the growth of child gut microbiota during the first three months after birth. Furthermore, the high structural similarity  between the child and maternal HGT networks supports the transmission of microbiota from mother to child. Third, we analyze phenotype-specific HGT communities and HGT edges. As for HGT communities, compared with control individuals, the phylum composition of IBD-specific HGT communities have significant increasing of ent}e-7)  and incr 0.0015) , 41. As As we can see, both in Mother-to-Child and longitudinal IBD data sets, by analyzing temporal HGT networks, we captured the significant changes of HGT networks. These changes reflect the alteration of gut microbiota under the change of host-associated selection pressure. Therefore, the HGT network is an effective model to describe the relationship between the gut microbiota and the host state. It provides a new perspective to observe the change of gut microbiota in the everchanging environment.We applied HGT networks analysis to two human gut longitudinal metagenomic sequencing datasets: Mother-to-Child data set  and longFollowing the method in, we constructed one HGT network per sample. Then we obtained 283 HGT networks from the Mother-to-Child data set and 148 HGT networks from the longitudinal IBD data set. Figure powerlaw package [Our study implies that the HGT network is scale-free. It is supported by the result that the degree distribution of the HGT network is better fitted by power-law than the other three heavy tail distribution . We filt package  to estimThe evaluation of the goodness of fit for power-law distribution is described in “alpha which is the parameter of power-law distribution. Figure alpha fitted to maternal and child HGT networks respectively. The distribution of fitted exponents corresponding to Mother is significantly different to the one corresponding to child  and has larger mean value alpha fitted to HGT networks of non-IBD, UC, and CD respectively. Three exponent distributions are significantly different to each other  and have IBD).HGT networks in different groups harbor different distributions of fitted exponents alpha in all groups are in region . So HGT networks hold the ultra-small world property, which implies that HGT networks tend to form dense sub-graphs. Such a network structure implies that the HGT network is robust and could maintain a stable status [As shown in Fig. e status .Fig. 4Thd of HGT networks has a linear relationship with lnlnN, where N is the number of nodes in the network, ln is the natural logarithm. Figure d and lnlnN of HGT networks in Mother-to-Child and Longitudinal IBD data sets. The ultra-small diameter d increases as lnlnN, which is significantly slower growth than the lnN derived for random networks. Therefore, the average distance in an HGT network is smaller than that in a random network, which imply the ultra-small world property of HGT networks.Since the degree exponent ll world . The ultWe studied the evolvement of HGT networks across time from two aspects: network complexity and network similarity. Network complexity is measured by Von Newman entropy, network size, and HGT event rate. Network similarity is measured by the Jaccard index and degree correlation. We observed, first, child HGT networks have increasing network complexity in the first three months after birth. Second, maternal and child HGT networks share a family-specific significant similarity. Last, the individual-specific similarity of the HGT network between different time points in IBD patients is significantly larger than that in non-IBD individuals. The evolvement of the infant HGT network describes the growth of gut microbiota. Figure We analyzed the evolvement of maternal HGT networks at three-time points: gestational week 27 (M_Gest), birth (M_Birth), and three months post-delivery (M_3_months), and child HGT networks during the first three months. The evolvement is measured from three aspects: Von Newman entropy, network size, and HGT event rate. The Von Newman Entropy measures the complexity of the network see “” sectionWe measured the similarity of HGT networks to explore the evolvement of dynamic gut microbiota see “” sectionThird, to explore the transmission of the HGT network from mother to child, we compared maternal and child HGT networks within- and across-families. As illustrated in Fig. We first focus on edges in the HGT network and identify multiple conserved edges corresponding to host states. In HGT network, the edge is constructed according to HGT events see “” sectionBifidobacterium is the genus that significantly different in child and mother  see “” sectionFirmicutes: 35.3%, Actinobacteria: 29.8%, Proteobacteria: 19.4%, Bacteroidetes: 15.1%, Others: 0.4%. The average phylum composition of maternal HCC is Firmicutes: 78.2%, Actinobacteria: 10.1%, Bacteroidetes: 7.9%, Proteobacteria: 3.2%, Others: 0.6%. Compared with child HCCs, the increasing of Firmicutes  are significant. Genomes belonging to Firmicutes which plays an important role in maintaining the gut health [Proteobacteria and Actinobacteria. As shown in the genus composition of child HECs, these genus include Bifidobacterium, Escherichia, etc.We got 24 child and 148 maternal HCCs in the Mother-to-Child data set, and get the phylum composition of these HCCs as shown in Fig. t health  are the We also explored the HGT network similarity in different disease states using the longitudinal IBD data set. We utilize four similarity metrics, including Jaccard index, degree correlation, Pagerank correlation, and clustering coefficient, to measure network similarity. For each individual, we calculate the four metrics among his HGT networks at different time points , Sutterella  [Pseudomonas . These genera could be treated as potential biomarkers. Pseudomonas plays an important role in IBD [We get 38 IBD HECs and 12 Non-IBD HECs. Figure  0.3894) , Pseudome in IBD . These fFirmicutes: 70.7%, Bacteroidetes: 14.4%, Proteobacteria: 6.8%, Actinobacteria: 5.9%, Verrucomicrobia: 1.9%, Others: 3%. The average phylum composition of IBD HCC is Firmicutes: 53.6%, Proteobacteria: 19.6%, Actinobacteria: 14.5%, Bacteroidetes: 9.9%, Others: 2.4%. Compared with Non-IBD HGT communities, IBD HCCs have less genome nodes from Firmicutes and Bacteroidetes which are the two most dominant phyla in the large intestine of healthy adults [Proteobacteria  and Actinobacteria . Many species belonging to Proteobacteria and Actinobacteria have strong association with the pathogenesis of IBD [We found 40 IBD and 54 Non-IBD HCCs in the longitudinal IBD datasets. Figure y adults . While ts of IBD .Figure As shown in Fig. Mycobacterium sp. VKM Ac-1816D, Mycobacterium sp. 1100029.7, and Mycobacterium sp. 1554424.7) from genus Mycobacterium, which includes pathogens known to cause IBD [Pseudomonas sp. 2588-5, Pseudomonas sp. 2995-1, and Pseudomonas sp. T) belong to genus Pseudomonas, which is also associated with IBD [Moreover, the species in the community of IBD HCC are found to be associated with IBD. Figure ause IBD , 54, 55.with IBD .Fig. 12Srecombinase family protein (51/2186) [plasmid mobilization relaxosome protein Mob (39/2186) [IS110 family transposase (23/2186) [site-specific integras (23/2186) [conjugal transfer protein Tra (20/2186) [transposase (2/2186) [multidrug SMR transporter [multidrug transporter AcrB [multidrug transporter MatE [multidrug efflux RND transporter permease subunit [HGT event can cause the combination of parts of two genes belonging to two different genome sequences to fusion a gene see “” section51/2186) , plasmid39/2186) , IS110 f23/2186) , site-sp23/2186) , conjuga20/2186)  and tran(2/2186) . Specifi(2/2186) . Productnsporter , multidrter AcrB , multidrter MatE , and mul subunit .To construct HGT networks, we downloaded all bacterial genomes from the National Center for Biotechnology Information (NCBI). For each taxonomy, we choose the genome with the minimal scaffold number and highest completeness, whose contamination is less than 10% in Genome Taxonomy Database (GTDB) taxonomy evaluation results . FinallyFigure k is degree and D, we must have The scale-free network has its degree distribution ested in , in ordetio test  to compaH denote the HGT network, V denotes the vertex set, which represents genomes linked by HGT events, E is an edge set, which indicates whether there exist HGT events between two genome nodes. Then the von Neumann entropy u. |V| is the number of nodes,  is an edge in E.Structural complexity is an important characteristic of complex networks. It greatly determines the function and status of complex networks. The growth of temporal networks often leads to structural change, which also means a change of complexity. Ye et al.  proposedHGT event rate. Let H denotes the number of detected HGT events in sample S, We take next-generation sequencing (NGS) short reads as input of LEMON to detect HGT events on reference genomes. The amount of raw data and the length of reference genomes can affect the number of detected HGT events in one sample We normalized the number of HGT events and define the normalized number as After obtaining the HGT networks, we calculated the similarity between networks according to the following measurements. First, we measured the similarity between networks by Jaccard similarity of species that present in the network. If two HGT networks share more species, then they obtain higher Jaccard similarity. Besides species present and absent, we also measured network similarity based on the topology property for species in networks, e.g., degree, PageRank, clustering coefficient. For two networks, we find species shared by both networks, record their degree in two networks, and calculate the Spearman correlation on the two-degree lists. The generated correlation implies the consistency of the importance rank for every species in two networks at the aspect of different topology properties. Finally, we assigned these comparison pair into multiple groups and studied the similarity distribution. To test whether the similarity between networks in the same family is significantly higher than it between different families, we applied a T-test to the similarity values between these two groups in different sample times.We detected community in each HGT network in each sample and to study the community evolution by comparing those detected HGT communities across time/individual. First, for community detection, we applied the Leiden algorithm , which iFor each HGT event, we recorded the number of samples it present. HGT events that show in less than the minimum sample threshold are filtered out. The minimum samples threshold is set as 5 and 4 in Mother-to-Child and Longitudinal IBD experiments, respectively. Then we measured the relationship between HGT events by their Jaccard similarity considering the samples they show up. After convert similarity to distance, we applied hierarchical clusters and determined classes by dynamic tree cut. We set distance to 0.6 and minimum class sizes as 10 and 20 for Mother-to-Child and Longitudinal IBD data sets, respectively. The outcome of the hierarchical result is HGT event clusters (HECs). Each HEC refers to the group of HGT events which occur together.ith HCC, IBD respectively in IBD. Otherwise num(Non-IBD) is 38 and the number of IBD samples num(IBD) is 109, the larger value of num(IBD) make IBD communities that are contained in Since each cluster of HGT communities/events (HCC/HEC) consists of HGT communities/events from different groups, it may contain multiple labels with an unequal number of communities/events. The label of the cluster should be determined by the predominant label in the cluster. Therefore we determined the label of cluster as following. Without loss of generality, we take the determination of labels for HCCs in longitudinal IBD data set as an example. From longitudinal IBD data set, we got 94 HCCs Let We further extended the HGT network study to gene function.a and b are the two HGT breakpoints. Since the two HGT breakpoints are in two gene regions, the combination of parts of two genes caused by HGT forms a fusion gene. We detected HGT breakpoints by LEMON and identified the gene fusion. So we can link the HGT event to gene function. The gene annotations  are collected from NCBI.Figure HGT is the process of sharing genetic material among different microbial species. It links different species by transferring genetic information. By analyzing HGT networks constructed from two longitudinal metagenomic sequencing data sets: Mother-to-Child and longitudinal IBD data sets, we found the HGT network is scale-free, whose degree distribution follows a power law. Most nodes have a small degree and are connected by hub nodes. Moreover, statistics support the ultra-small world property of the HGT network. The distance between two randomly nodes in the HGT network could maintain small despite the growth of the network. Such a stable internal structure demonstrates the robustness of the HGT network in the everchanging environment. The HGT network also provides us an efficient way to model human gut microbiota. The development of the child gut microbiota during the first three months after birth could be captured by the evolvement of temporal HGT networks. The increasing of HGT network complexity and size is led by the growth of strains harboring HGT events. Furthermore, we have found a significant similarity between the family-specific child and maternal HGT networks. Therefore, HGT network could characterize bacterial transmission patterns from mother to child. It also demonstrates that maternal gut bacteria may be an essential source of child gut bacteria.Proteobacteria and Actinobacteria phylum. So HGT communities contain more bacteria from Proteobacteria and Actinobacteria and fewer bacteria from Firmicutes. Similar change is also observed in newborn children. By clustering HGT communities, we could find similar communities across multiple HGT networks that are from the same group. These similar communities reflect the influence of a specific host state on the structure of the HGT network and could be treated as potential biomarkers. The formation of the HGT community cluster is led by similar HGT events across samples. Through clustering HGT events, we realized that in IBD patients, the composition change of HGT communities is led by the increasing of HGT events mainly contained in the pathogenic genus Mycobacterium, Sutterella, and Pseudomonas. While in newborn children, we observed the increase of HGT events contained in Bifidobacterium and Escherichia. These differences reflect the alteration of gut microbiota in different conditions. The summarization of fusion genes in HGT events helps us better realize genes associated with HGTs. Many fusion genes in HGT events encode proteins, including recombinase family protein, plasmid mobilization relaxosome protein Mob, conjugal transfer protein Tra, and so on, that facilitate the horizontal transfer of genetic material. Furthermore, in IBD patients, more fusion genes caused by HGT events encode multidrug transporter proteins. This reveals that beneficial HGT events contribute to the survival of associated microbial strains in the gut under the specific selection pressure.Analysis of the HGT community and HGT event demonstrates that age and disease change the internal structure of HGT networks. Compared with healthy individuals, the inflammatory conditions of the gastrointestinal tract in IBD patients are suitable for the growth of bacteria from In summary, through the HGT network, we research human gut microbiota from a systematic perspective. The network analysis of Mother-to-Child and longitudinal IBD data sets demonstrate the characteristic of HGT networks differs under different conditions. In the future, we will apply our pipeline to analyze more HGT networks. It helps us get a deeper understanding of the relationship between host states and microbial interactions.Additional file 1. Spearman correlation between degrees of nodes in HGT networks in IBD dataset.Additional file 2. Jaccard similarity of nodes in HGT networks in IBD dataset.Additional file 3. Spearman correlation between PageRank of all nodes in HGT networks in IBD dataset.Additional file 4. Spearman correlation between the clustering coefficient of all nodes in HGT networks in IBD dataset. Additional file 5. The heatmaps of similarity matrix for IBD HGT networks measured using Jaccard similarity , Spearman correlation between degrees , Spearman correlation between PageRank , and Spearman correlation between the clustering coefficient . The heatmap of similarity matrix for Infant HGT networks measured using Jaccard similarity*degree correlation .


Horizontal gene transfer (HGT) is one of the most important processes in prokaryote evolution. The sharing of DNA can spread neutral or beneficial genes, as well as genetic parasites across populations and communities, creating a large proportion of the variability acted on by natural selection. Here, we highlight the role of HGT in enhancing the opportunities for conflict and cooperation within and between prokaryote genomes. We discuss how horizontally acquired genes can cooperate or conflict both with each other and with a recipient genome, resulting in signature patterns of gene co-occurrence, avoidance, and dependence. We then describe how interactions involving horizontally transferred genes may influence cooperation and conflict at higher levels . Finally, we consider the benefits and drawbacks of HGT for prokaryotes and its fundamental role in understanding conflict and cooperation from the gene-gene to the microbiome level. This horizontal gene transfer (HGT) is particularly prevalent in prokaryotes, where it is one of the main mechanisms contributing to genetic variation and thus evolution. HGT can occur by transformation  including transposons, integrated prophages, and integrated or self-replicating plasmids . BeneficHere, we consider genes, plasmids, phage, genomes, and organisms as agents that interact on multiple levels . These aFirst, we consider interactions between single horizontally acquired genes (HAGs) and the recipient cell. We expect that a certain cohort of HAGs will have a neutral fitness effect on the recipient cell, with their frequency in the population consequently being determined purely by drift . Other HSome HAGs are undoubtedly beneficial to the recipient cell. We regard this type of interaction as mutual cooperation, with fitness gains for the cell, all its resident genes, and the newly acquired gene, the latter of which can now spread in a new population. These beneficial genes can allow the colonization of novel niches or facilitate survival in changing environments, as shown by the spread of antimicrobial resistance (AMR) genes . CooperaGroups of HAGs can lead to adaptation to novel environments, even resulting in ecological divides between organisms with shared ancestry. Thus, HGT can start the process of diversification between linages that can then co-exist indefinitely as proposed by the ecotype model . A strikgeneA is detrimental to the product of geneB. It may also be harmful to the fitness of the cell to maintain multiple genes with redundant functions. This is possibly at play in the siderophore families of Salinispora spp., where only one of the two possible families  is ever observed in any given strain despite frequent HGT of the encoding gene clusters  can also be considered as interacting agents. Many examples of these types of interactions exist, including cooperation  and conflict . These social and ecological interactions between prokaryotes are widespread and highly prevalent in contexts where multiple cells have the opportunity to interact, such as in biofilms, microbiomes, and symbioses . Here, wEscherichia, for example, most secreted proteins are of recent origin, likely acquired through HGT , while potentially reducing genome size . HGT can species . An even endobia . At leasi genome  and, in ell wall . The insThese interactions raise important questions on the nature of symbioses and microbiomes, namely, at what point does a symbiont become obligate, and how is this driven by HGT? Has ancient HGT from bacteria to host enabled these symbionts to become more of an organelle than otherwise would have been possible? How does HGT between members of the microbiome affect its interaction with the host? It could be argued that these symbiotic interactions go beyond mere cooperation. The preservation by the host of genes lost by its symbionts may be random but might also demonstrate functional integration and unity between host and microbiome. In these examples, HGT has allowed organisms to become inter-dependent, ensuring cooperation in maintaining their relationship. From a wider perspective, many questions remain regarding the degree of influence that HGT exerts on interactions between organisms. The fitness effects of HAGs in different cells and environments should be considered, as should their effects in social contexts.Given the prevalence of genetic parasites and the high probability of acquiring deleterious, or at best neutral genes, it is worth considering why cells engage in HGT despite its risky nature. It is possible that the cell does not have a chance to weigh the risks and instead is constantly battling deleterious incoming DNA against which they cannot always adequately defend . Another possibility is that HGT occurs because even though transferred genes are deleterious overall, their average fitness effects are not high enough for selection to either eliminate them or to lower HGT rates further ] or adaptive [clustered regularly interspaced short palindromic repeats (CRISPR)-Cas], and are present in most prokaryote genomes . These sThe existence of cell-encoded mechanisms that promote HGT indicates that HGT may overall be more beneficial than harmful. As discussed, HAGs can help cells adapt to novel environments, and the benefits of homologous recombination to remove deleterious DNA and reduce clonal interference may help to maintain HGT . The preStreptococcus pneumoniae and Streptococcus mutans, quorum sensing regulates competence, potentially promoting genetic exchange between members of the same species, resulting in both the removal of deleterious mutations or genes and acquisition of beneficial ones  could be maintained collectively by a group of cells that each encode different genes required for that phenotype . ConstraAcquiring new genes will always carry the risk of deleterious or neutral effects, but the benefits of colonizing novel niches may outweigh the risk. Despite progress in understanding many of these hypotheses, we still do not know the main reason why HGT is so prevalent, and a single explanation is unlikely. As more genomes become available, analysis of many genomes from the same and multiple species will help tackle some of these questions.We are only just beginning to understand the multifarious nature of the drivers of genome composition. We have several known unknowns; the rate of gene acquisition by HGT, the distribution of fitness effects that are mediated by incoming genes, the frequency with which innate and acquired prokaryotic defense systems are called into action in natural environments, and the magnitude of intragenomic conflicts, or indeed the strength of the effects of cooperation between recipient cells and HAGs. Furthermore, we do not know to what extent gene sharing and content variation affect conflict and cooperation between the prokaryote genomes and cells in which they reside. Understanding these interactions may also require clarifying at which level selection is acting, that is, clearly defining the agents, a difficult task .The high levels of HGT seen in prokaryotes means that the study of these distinct cooperative and conflicting interactions is central to our understanding of prokaryotic evolution. Thanks to genomics, we can now easily acquire the “parts list” of genomes. The next step will involve our understanding of how they all fit together, and why.RH, FW, JM, YO, and MD-S conceived of, drafted, and approved this manuscript. JM secured necessary funding to support the work of this manuscript. All authors contributed to the article and approved the submitted version.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.


EYA4 gene are known to lead to autosomal dominant non-syndromic hereditary hearing loss, DFNA10. To date, 30 variants have been shown to be responsible for hearing loss in a diverse set of nationalities. To better understand the clinical characteristics and prevalence of DFNA10, we performed genetic screening for EYA4 mutations in a large cohort of Japanese hearing loss patients. We selected 1,336 autosomal dominant hearing loss patients among 7,408 unrelated Japanese hearing loss probands and performed targeted genome enrichment and massively parallel sequencing of 68 target genes for all patients. Clinical information of cases with mutations in EYA4 was gathered and analyzed from medical charts. Eleven novel EYA4 variants  and two previously reported variants were found in 12 probands (0.90%) among the 1,336 autosomal dominant hearing loss families. The audiometric configuration of truncating variants tends to deteriorate for all frequencies, whereas that of non-truncating variants tends to show high-frequency hearing loss, suggesting a new correlation between genotype and phenotype in DFNA10. The rate of hearing loss progression caused by EYA4 variants was considered to be 0.63 dB/year, as found in this study and previous reports.Variants in the  Regarding hereditary hearing loss, over 100 genes are known to be causative based on genetic analysis [Hereditary Hearing Loss Homepage: https://hereditaryhearingloss.org/ accessed at August 2019], and this identification has been accelerated by the recent progress in genome sequencing technology. Hereditary hearing loss patterns vary between autosomal dominant, autosomal recessive, X-linked, and mitochondrial. Autosomal dominant non-syndromic hearing loss (ADNSHL) is often seen in postlingual hearing loss patients, and is the cause for approximately 20–25% of all hereditary hearing loss2. Currently, 67 causative genes for ADNSHL have been identified3.Hearing loss is the most common sensory disorder and more than 50% of cases of congenital or early onset hearing loss are caused by genetic factorsEYA4 gene (OMIM* 603550), located on chromosome 6q22.3-q23.2, for DFNA10 (OMIM# 601316) was first identified in American and Belgian hearing loss patients in 20014. EYA4 encodes eye absent 4 protein, a member of the EYA family of proteins. It is a transcriptional activator and is considered to be required for proper eye development as well as for the maturation and maintenance of the organ of Corti5. It has been reported that Eya4 is expressed in early stage otic vesicles, largely confined to the upper cochlear duct, in rodents. These cells later form the stria vascularis, Reissner’s membrane, spiral limbus, and organ of Corti. Eya4 is also expressed in the spiral ganglion neurons and organ of Corti in marmosets; however, the expression pattern in the human cochlea remains unknown6. The EYA4 protein is comprised of 639 amino acids with 2 functional domains. The C-terminal domain, which is composed of 271 residues and is named eyaHR , is highly conserved among EYA family proteins, and a more divergent proline-serine-threonine (PST)-rich transactivation domain is located at the N-terminus (eyaVR).The EYA4 gene and a partial or whole deletion of the EYA4 allele have been reported as a cause of ADNSHL in various ethnic groups, including the American, Belgian, Dutch, Korean, Chinese, Swedish, German, Australian, Hungarian, Philippine, Italian, and Japanese populations31. These previous studies have described the clinical phenotypes of patients with EYA4 variants; however, the detailed characteristics of the hearing loss, such as its progressiveness or severity, remain unclear. In addition, the genotype-phenotype correlation is also yet to be clarified.To date, 30 variants in the EYA4 gene and prevalence of EYA4-associated hearing loss in the Japanese population, and to obtain a more precise description of the clinical features of EYA4-associated hearing loss.In this study, we sought to elucidate the variant spectrum of the A total of 7,408 probands from unrelated Japanese hearing loss families were enrolled from 67 otolaryngology departments across Japan participating in the present study between February 2012 and October 2017. The hereditary patterns of the hearing loss in the probands’ families were autosomal dominant in 1,336, autosomal recessive/sporadic in 4,898, and unknown inheritance pattern in 1,174 cases. Written informed consent was obtained from all patients . This study was approved by the Shinshu University Ethical Committee as well as the respective Ethical Committees of the other participating institutions listed below. Akita University Ethical Committee, Iwate Medical University Ethical Committee, Tohoku Rosai Hospital Ethical Committee, Fukushima Medical University Ethical Committee, Yamagata University Ethical Committee, Dokkyo Medical University Ethical Committee, TAKASAKI Ear Nose & Throat Clinic Ethical Committee, Niigata University Ethical Committee, Tokyo Medical University Ethical Committee, Jikei University Ethical Committee, Toranomon Hospital Ethical Committee, Kitasato University Ethical Committee, International University of Health and Welfare Mita Hospital Ethical Committee, National Rehabilitation Center for Persons with Disabilities Ethical Committee, Keio University Ethical Committee, Hamamatsu University Ethical Committee, Shiga University Ethical Committee, Shiga Medical Center for Children Ethical Committee, Osaka University Ethical Committee, Kobe City Medical Center General Hospital Ethical Committee, Hyogo College of Medicine Ethical Committee, Kyoto Prefectural University Ethical Committee, Okayama University Ethical Committee, Yamaguchi University Ethical Committee, Ehime University Ethical Committee, Kyushu University Ethical Committee, Kanda ENT Clinic Ethical Committee, Nagasaki University Ethical Committee, Miyazaki University Ethical Committee, Kagoshima University Ethical Committee, Ryukyus University Ethical Committee, Sapporo Medical University Ethical Committee, Tohoku University Ethical Committee, Jichi Medical University Ethical Committee, Gunma University Ethical Committee, Jyuntendo University Ethical Committee, Yokohama City University Ethical Committee, Mejiro University Ethical Committee, Saitama Medical University Ethical Committee, Abe ENT clinic Ethical Committee, Tokyo Medical Center Institute of Sensory Organs Ethical Committee, Jichi University Saitama Medical Center Ethical Committee, Aichi Children’s Health Medical Center Ethical Committee, Chubu Rosai Hospital Ethical Committee, Kyoto University Ethical Committee, Mie University Ethical Committee, Kansai Medical University Ethical Committee, Kobe University Ethical Committee, Osaka Medical Center and Research Institute for Maternal and Children Health Ethical Committee, Wakayama Medical University Ethical Committee, Kouchi University Ethical Committee, Hiroshima University Ethical Committee, Hiroshima City Hiroshima Citizen Hospital Ethical Committee, Fukuoka University Ethical Committee, Kurume University Ethical Committee, National Defense Medical College Ethical Committee, Tokai University Ethical Committee, Hokkaido University Ethical Committee, Kanagawa Children’s Medical Center Ethical Committee, Tokyo Medical and Dental University Ethical Committee, Hirosaki University Ethical Committee, Tokyo Metropolitan Children’s Medical Center Ethical Committee, Hakodate central general hospital Ethical Committee, Osaka Red Cross Hospital Ethical Committee, Hiroshima Prefectural Hospital Ethical Committee, Nara Medical University Ethical Committee, and Tsukuba University Ethical Committee. All methods were performed in accordance with the Guidelines for Genetic Tests and Diagnoses in Medical Practice of the Japanese Association of Medical Sciences and the Declaration of Helsinki as required by Shinshu University.EYA4 variants. Pure-tone average (PTA) was calculated from the audiometric thresholds at four frequencies . The severity of hearing loss was divided into mild (PTA: 20–40 dB HL), moderate (41–70 dB HL), severe (71–95 dB HL), and profound (>95 dB HL). Asymmetric hearing loss was defined as a difference in PTA of over 10 dB between the right and left ears. The audiometric configurations were categorized into low-frequency, mid-frequency (U-shaped), high-frequency, flat type, and deaf as reported previously32.The onset age of hearing loss and the degree of progressiveness were analyzed based on the medical charts of the probands and their family members harboring the same 33. MPS was performed with an Ion Torrent Personal Genome Machine (PGM) system using an Ion PGM™ 200 Sequencing Kit and an Ion 318™ Chip (ThermoFisher Scientific). The sequence data were mapped against the human genome sequence (build GRCh37/hg19) with a Torrent Mapping Alignment Program. After sequence mapping, the DNA variant regions were piled up with Torrent Variant Caller plug-in software. After variant detection, their effects were analyzed using ANNOVAR software35. The missense, nonsense, insertion/deletion and splicing variants were selected from among the identified variants. Variants were further selected as less than 1% of: (1) the 1,000 genome database, (2) 6,500 exome variants, (3) the Human Genetic Variation Database , and (4) 333 in-house Japanese normal hearing controls. This filtering process was performed using our original database software described elsewhere36. The pathogenicity of selected variants was evaluated by ACMG  standards and guidelines37. For missense variants in particular, functional prediction software, including Sorting Intolerant from Tolerant (SIFT), Polymorphism Phenotyping (PolyPhen2), LRT, Mutation Taster, Mutation Assessor, Functional Analysis through Hidden Markov Models (FATHMM), RadialSVM, LR, and CADD, were used through the ANNOVAR software program35. Direct sequencing was utilized to confirm the selected variants.Amplicon libraries were prepared using an Ion AmpliSeq™ Custom Panel for 68 genes reported to cause non-syndromic hereditary hearing loss , in accordance with the manufacturer’s instructions. The detailed protocol has been described elsewhereTM sequencing that utilizes multiplex PCR-based targeted genome enrichment38. The depth of coverage information for each amplicon was used for copy number analysis. After normalization, the relative read depths of amplicons were visualized as described previously38.We employed our recently published specialized copy number variation (CNV) detection method for Ion AmpliSeqEYA4 was reported as a genetic cause for autosomal dominant inherited hearing loss, thus, we selected hearing loss patients from apparently autosomal dominant families. Among 1,336 autosomal dominant hearing loss families, we further selected the families with candidate EYA4 variants. The criteria for the selection process were (1) the EYA4 variant was classified into “pathogenic”, “likely pathogenic” or “uncertain significance” and (2) there were no candidate variants in the other 67 genes reported to cause hearing loss. Based on the ACMG guidelines, we regarded “pathogenic” and “likely pathogenic” variants as strong candidates for EYA4-associated hearing loss. In addition, we listed the “variants of uncertain significance” identified during the filtering procedure described above in Table EYA4 pathogenic variants were predicted to have a CADD Phred score of 23.5 or more , so we employed 20 as threshold to allow a safety margin. In addition, we also removed the c.1790delT and c.1886_1899del variants as unlikely causative variants because nonsense-mediated mRNA decay was not presumed to be triggered from the location of the variants. Finally, we selected 12 variants as causative and performed a more detailed hearing loss phenotype analysis.EYA4 pathogenic variant  and four were located in eyaHR (amino acids 370–639). The one previously reported variant was classified as “Pathogenic”. Eight variants were classified as “Likely pathogenic” according to the ACMG guidelines, whereas three remained as “variants of uncertain significance (VUS)”. Further, none of the 12 variants was found in the Japanese 333 in-house controls , and none of the three VUS variants was observed in the ExAC03 database.Among the 1,336 probands with ADNSHL, we identified 12 (0.90%) who carried a possible EYA4 variants, we also identified 6 variants in the EYA4 gene from our cohort . Audiometric configurations in these patients included low-frequency type in 6 ears, mid-frequency type in 2 ears, high-frequency type in 4 ears, and flat type in 12 ears. All individuals had noticed a progression in their hearing loss.Table 27. From our study results, we incorporated the hearing thresholds for 12 cases in this analysis . We compared the hearing thresholds of patients with truncating variants to those with non-truncating variants (missense variants) including the patients in this study and previously reported cases  in the Japanese population. This prevalence is slightly less than those of other ADNSHL genes such as KCNQ4, TECTA, POU4F3, and WFS1. KCNQ4 is one of the most frequently observed responsible genes for ADNSHL in the Japanese population, and its prevalence is 6.6%39. Likewise, the prevalence of ADNSHL caused by TECTA variants is 2.9%, 2.7% for POU4F3 variants, and 2.5% for WFS1 variants42.In this report, we analyzed 1,334 ADNSHL patients and identified 12 candidate variants for KCNQ4 is the most frequent causative gene for ADNSHL in the Japanese population, whereas TECTA is the most frequent causative gene in the American population43. One plausible reason of this difference among populations is the effect of founder or recurrent mutations. Indeed, the variant of KCNQ4; c.211delC, which is commonly observed in Japanese ADNSHL patients, was reported to be caused by a founder effect39. Most of the EYA4 variants found in this report were novel .In previous reports, the audiometric configuration for EYA4-associated hearing loss patients identified in this study and previously reported cases. The rate of progression of hearing loss caused by EYA4 was considered to be 0.63 dB/year (95%CI: 0.41–0.85 dB/year). In previous reports on ADSNHL hearing loss, the progression rate for the POU4F3 gene was 0.5–0.9 dB/year41, that for MYO6 was 2.0 dB/year45, and that for ACTG1 was 2.0–6.0 dB/year46, and the results in this study suggests that the rate of hearing loss progression caused by EYA4 may be relatively mild.We also analyzed the rate of hearing deterioration in EYA4 copy number loss cases. Thus, we speculated that the mechanism of EYA4-associated hearing loss was haploinsufficiency. In the gnomAD database (https://gnomad.broadinstitute.org/gene/ENSG00000112319), a non-negligible number of truncating variants were identified in large control populations. The probability of a loss of function intolerant score (pLI score) was 0.05. This low score may mean the loss of function in this gene is tolerant and without pathogenicity. However, most of the loss of function variants were located in specific exons that only included some splicing variants and were seldom observed in other exons . This result also supports haploinsufficiency as the mechanism underlying EYA4-associated hearing loss. The patient who carried c.1790delT, located in specific exons (truncating variants accumulated in the exon in gnomAD) suffers from an enlarged vestibular aqueduct, and this phenotype was not matched with hearing loss caused by EYA4 mutations. For these reasons, we classified this variant (c.1790delT) as “unlikely causative”.In this study, we identified nine truncating variants including two EYA4-associated hearing loss was 0.90% in Japanese families with autosomal dominant hearing loss. The audiometric configuration of truncating variants tended to exhibit flat-type, whereas that of non-truncating variants tended to be high-frequency hearing loss, suggesting a novel genotype-phenotype correlation in DFNA10.In conclusion, we performed MPS analysis of large cohort of 1,334 ADNSHL patients and successfully identified 12 novel and promising pathogenic variants. Based on this, we estimated the incidence of