Repressor element 1-silencing transcription factor deficiency yields profound hearing loss through Kv7.4 channel upsurge in auditory neurons and hair cells
- Hebei Medical University, China
- University of Oxford, United Kingdom
- University of Leeds, United Kingdom
- University of Nevada Reno, United States
Abstract
Repressor element 1-silencing transcription factor (REST) is a transcriptional repressor that recognizes neuron-restrictive silencer elements in the mammalian genomes in a tissue- and cell-specific manner. The identity of REST target genes and molecular details of how REST regulates them are emerging. We performed conditional null deletion of Rest (cKO), mainly restricted to murine hair cells (HCs) and auditory neurons (aka spiral ganglion neurons (SGNs)). Null-inactivation of full-length REST did not affect the development of normal HCs and SGNs but manifested as progressive hearing loss in adult mice. We found that the inactivation of REST resulted in an increased abundance of Kv7.4 channels at the transcript, protein, and functional levels. Specifically, we found that SGNs and HCs from Rest cKO mice displayed increased Kv7.4 expression and augmented Kv7 currents; SGN’s excitability was also significantly reduced. Administration of a compound with Kv7.4 channel activator activity, fasudil, recapitulated progressive hearing loss in mice. In contrast, inhibition of the Kv7 channels by XE991 rescued the auditory phenotype of Rest cKO mice. Previous studies identified some loss-of-function mutations within the Kv7.4-coding gene, Kcnq4, as a causative factor for progressive hearing loss in mice and humans. Thus, the findings reveal that a critical homeostatic Kv7.4 channel level is required for proper auditory functions.
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All data generated or analyzed are included in the source data
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Author details
Funding
National Institute on Deafness and Other Communication Disorders (DC015135,DC016099)
- Ebenezer N Yamoah
National Institute on Aging (AG060504-01,P01 AG051443)
- Ebenezer N Yamoah
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All experimental animal protocols were performed following the Animal Care and Ethical Committee of Hebei Medical University (Shijiazhuang, China). 01644
Copyright
© 2022, Zhang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Repressor element 1-silencing transcription factor deficiency yields profound hearing loss through Kv7.4 channel upsurge in auditory neurons and hair cells
eLife 11:e76754.
https://doi.org/10.7554/eLife.76754
Further reading
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Background:
Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes.
Methods:
This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites.
Results:
During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes.
Conclusions:
Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions.
Funding:
Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).
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