# Multiscale Experimental and Computational Methods to Characterize Hormonal and Mechanical Contributions to Pregnancy-Induced Remodeling of Skeletal Muscle

> **NIH NIH K99** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $117,959

## Abstract

Project Summary
Pelvic floor disorders (PFDs) are common conditions that affect ~25% of U.S. women. PFDs are morbid, with
more than 50% of afflicted patients rating them as “worse than death”. As vaginal delivery is the greatest
epidemiologic risk factor for PFDs—likely due to the pelvic floor muscle (PFM) dysfunction it often incites—
greater understanding of vaginal delivery biomechanics and birth injury is needed. But in order to accurately
study the mechanical behavior of the PFMs during childbirth, their structure, function, and integrity just before
childbirth must be known. Acquiring this knowledge is not trivial given the degree of pregnancy-induced
remodeling that soft tissues undergo during gestation. Thus, this study aims to implement multiscale
experimental and computational methods to characterize and simulate pregnancy-induced remodeling of PFMs.
Phase 1 (K99) is the experimental arm of this proposal. In Aim 1 cell culture of primary 1) skeletal muscle stem
cells and 2) fibro-adipogenic progenitors isolated from female rat PFMs will be used to determine the impact of
sex hormones (e.g., estrogen) and mechanical stretch on resulting 1) myotube growth and 2) collagen secretion
by fibroblasts, respectively. After the cultured cells have differentiated, myotube size, fusion index, and the
amount of collagen secreted (quantified as a percentage of the sampled area) will be quantified via bright field
(myotubes) and fluorescence (fibroblasts) microscopy. These will serve as proxies for muscle fiber growth and
collagen deposition in vivo, allowing for the determination of the effect of sex hormones and mechanical stretch
on the contractile and extracellular matrix (ECM) components of the PFMs. Meanwhile, Aim 2 will define changes
in whole PFM active and passive mechanics across the nonpregnant—postpartum continuum. Whole PFMs will
be harvested from rats at various stages throughout the pregnancy and postpartum, and then ex vivo active and
passive mechanical testing will be performed. This will establish changes in force generating capacity (active
properties) and load bearing capacity (passive properties) across the continuum, revealing how the function of
both the contractile (active) and ECM (passive) components of PFMs are altered by pregnancy and childbirth.
Phase 2 (R00) is the computational arm of this proposal. Aim 3 will generate intracellular signaling network (cell
level) and finite element (whole muscle level) models, calibrate and validate those models using literature and
Phase 1 data, and then couple those models; resulting in a multiscale computational model of pregnancy-
induced PFM remodeling. This coupled model will consider sex hormone levels, the degree of mechanical stretch
acting on myofibers and the ECM, myofiber growth, and collagen deposition collectively while simulating their
impact on whole PFM active and passive function. Together, these aims will characterize the multiscale
(intracellular and whole muscle) mec...

## Key facts

- **NIH application ID:** 10866292
- **Project number:** 1K99HD115224-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Megan Routzong
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $117,959
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10866292

## Citation

> US National Institutes of Health, RePORTER application 10866292, Multiscale Experimental and Computational Methods to Characterize Hormonal and Mechanical Contributions to Pregnancy-Induced Remodeling of Skeletal Muscle (1K99HD115224-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10866292. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*
