# Molecular genetics of mammalian larynx and vocal fold development

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2020 · $365,593

## Abstract

Abstract:
Vocal communication underlies almost every aspect of human social interactions, yet we know almost
nothing about the developmental biology of the organs of vocalization, the larynx and vocal folds. This is
a significant issue because problems with voice are a common but poorly understood aspect of many
human structural birth defects, and the resulting difficulty in communication has a profound effect on
patients' quality of life. Here, we propose to develop the mouse as a model for normal and pathological
development of the larynx and vocal folds. We will systematically characterize embryonic lineages and
gene expression patterns in the diverse tissues of the mouse larynx, and we will characterize
pathological laryngeal development in mouse models of human birth defect syndromes. We will also
take a systems biology approach to defining gene regulatory circuitry underlying mammalian laryngeal
development. By combining genetic fate mapping, molecular genetics, and systems biology, the
experiments proposed here will provide a modern foundation from which we can assemble a detailed
mechanistic understanding of laryngeal development and gain molecular insights into the etiology of
human laryngeal birth defects.

## Key facts

- **NIH application ID:** 9925656
- **Project number:** 5R01HD090163-04
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Steven Alexander Vokes
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $365,593
- **Award type:** 5
- **Project period:** 2017-07-10 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9925656

## Citation

> US National Institutes of Health, RePORTER application 9925656, Molecular genetics of mammalian larynx and vocal fold development (5R01HD090163-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9925656. Licensed CC0.

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