# LIVE IMAGING OF BONE REGENERATION IN ZEBRAFISH

> **NIH NIH R01** · DUKE UNIVERSITY · 2021 · $31,836

## Abstract

ABSTRACT
Mammalian bone has the capacity throughout life to regenerate in response to fracture injury. However, there is
a ceiling for this regenerative potential, with hurdles to regeneration after a major trauma like limb amputation.
This has a significant socio-economic impact, as it is estimated that at least one in two Americans over age 50
is expected to have or be at risk of bone disease, and every year an estimated 1.5 million individuals suffer a
fracture due to bone disease. We have developed imaging methods to study how osteoblasts drive bone
regeneration in zebrafish, which display robust regeneration after major injury to bony structures like their fins,
scales, and jaws. Our goal is to exploit this regenerative capacity, new imaging platforms we have created, and
the molecular genetic approaches available in zebrafish to improve our ability to understand and manipulate the
regenerative capacity of bone. We have recently identified Erk signaling waves as a mechanism for the control
of osteoblast regeneration. This Supplement application extends our original proposal by testing the hypothesis
that mechanical signals contribute to osteoblast regeneration by integrating inputs from the Erk pathway and
Hippo/Yap-Taz pathway, another signaling pathway playing crucial roles in bone morphogenesis and
regeneration.

## Key facts

- **NIH application ID:** 10414209
- **Project number:** 3R01AR076342-02S1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Stefano Di Talia
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $31,836
- **Award type:** 3
- **Project period:** 2020-02-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10414209

## Citation

> US National Institutes of Health, RePORTER application 10414209, LIVE IMAGING OF BONE REGENERATION IN ZEBRAFISH (3R01AR076342-02S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10414209. Licensed CC0.

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