# Mechanosensor Development, Function and Dysfunction

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2021 · $604,747

## Abstract

SUMMARY
The long-term goal of my laboratory is to elucidate the mechanisms that control mechanotransduction in hair cells,
and the defects in this process that cause deafness. We propose here to study the mechanisms that regulate
the assembly and function of the hair cell's mechanotransduction complex. We hypothesize that several proteins
including TMIE, LHFPL5, TMC1/2, and CIB2 assemble into a mechanotransduction complex in hair cell
stereocilia. We predict that some of these proteins contribute to the pore of the transduction channel while other
regulate pore properties or link the channel to the tip link and the cytoskeleton. We also predict that a specialized
molecular machinery regulates the transport of the components of the mechanotransduction machinery from the
cell body into stereocilia. To test our hypothesis, we will use genetically modified combined with biochemical,
cell biological and electrophysiological methods to study protein function in protein transport and
mechanotransduction. Our preliminary data show the feasibility of our approach. We have new evidence
regarding the mechanisms by which components of the mechanotransduction machinery function within the
transduction complex and we have identified new proteins that regulate protein transport from the cell body into
stereocilia.

## Key facts

- **NIH application ID:** 10091423
- **Project number:** 5R01DC005965-19
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Ulrich Mueller
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $604,747
- **Award type:** 5
- **Project period:** 2003-04-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10091423

## Citation

> US National Institutes of Health, RePORTER application 10091423, Mechanosensor Development, Function and Dysfunction (5R01DC005965-19). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10091423. Licensed CC0.

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