Multi-Scale Modeling of Tissue Expansion: Genome Expression Patterns in the Acute Stretch Scenario
Sergey Y. Turin, MD1, Joanna K. Ledwon, PhD1, Hanah Bae, MSEd2, Taeksang Lee, MS3, Adrian Buganza Tepole, PhD4, Jolanta M. Topczewska, PhD1, Arun K. Gosain, MD1.
1Northwestern University Feinberg School of Medicine, Stanley Manne Children’s Research Institute, Chicago, IL, USA, 2Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA, 3School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA, 4Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
Despite decades of experience, tissue expansion (TE) often has high complication rates. Creating a reliable model of skin growth would allow for data-driven optimization of expansion protocols and decrease complication rates if used to plan the expansion. The changes in signaling pathways observed on the transcriptional level in skin under stretch are not well understood. Therefore, we combined mathematical models of skin under stretch with the biological response measured by gene expression levels and with histological assessment of skin structure with the goal of creating a comprehensive multi-scale model of tissue expansion.
METHODS: Five animal models (Yucatan minipigs) underwent 10 expansion protocols. Each animal was tattooed with 4 grids, 2 of which served as controls. Expanders were placed subcutaneously. The expansion protocols varied regarding volume of fill (60 or 30 cc), timing (1 hour, 24 hours, 3 days, or 7 days prior to expansion), or single versus 2 fills. 3D photography was captured for isogeometric analysis to measure skin growth and stretch. Total RNA from individual biopsies was isolated, gene expression was estimated using RNA-Seq (64 samples), then differences in gene expression were calculated and verified by qRT-PCR.
RESULTS: Statistically significant changes in gene expression levels correlated to the amount of stretch were obtained for each model. Figure 1 illustrates the amount of stretch and growth attained prior to sacrifice, as measured by isogeometric analysis for model #3. The apex of the expander (orange) represents the highest stretch and was correlated with the largest changes in gene expression. The genes most dramatically activated by stretch include MMP1, SAA3, ILB1. PDLIM and RHOF were two of the most consistently downregulated genes. The identified genes include well-known responders to the mechanical force (e.g. MMP1 or TNC), as well as completely new genes with no described role in skin adaption to stretch, presenting a new area for further study. Figure 2 represents the change in selected genes' expression (as fold change) in the same model, showing the level of expression at the apex (i.e. area of maximal stretch), the side, and base of the expander compared to contralateral controls. These data show correlation between the magnitude of stretch and fold change in gene expression. Subsequent isogeometric analysis provides the tools for determination of the proportion of tissue growth attributable to expansion versus elastic stretch or animal growth.
CONCLUSION: We have correlated skin growth with changes in gene expression levels and the mathematically calculated mechanical forces applied to each tissue expansion scenario tested. With the addition of histological analysis, we will attain a multi-scale model of skin expansion. Future translational studies will aim to guide tissue expansion protocols in humans to minimize complications and maximize tissue growth.
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