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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Physiol. | doi: 10.3389/fphys.2019.00636

Predictive approach identifies molecular targets and interventions to restore angiogenesis in wounds with delayed healing

  • 1Biotechnology HPC Software Applications Institute (BHSAI), United States
  • 2University of Illinois at Chicago, United States
  • 3United States Army Medical Research and Materiel Command, United States

Impaired angiogenesis is a hallmark of wounds with delayed healing, and currently used therapies to restore angiogenesis have limited efficacy. Here, we employ a computational simulation-based approach to identify influential molecular and cellular processes, as well as protein targets, whose modulation may stimulate angiogenesis in wounds. We developed a mathematical model that captures the time courses for platelets, 9 cell types, 29 proteins, and oxygen, which are involved in inflammation, proliferation, and angiogenesis during wound healing. We validated our model using previously published experimental data. By performing global sensitivity analysis on thousands of simulated wound-healing scenarios, we identified six processes (among the 133 modeled in total) whose modulation may improve angiogenesis in wounds. By simulating knockouts of 25 modeled proteins and by simulating different wound-oxygenation levels, we identified four proteins [namely, transforming growth factor (TGF)-β, vascular endothelial growth factor (VEGF), fibroblast growth factor-2 (FGF-2), and angiopoietin- 2 (ANG-2)], as well as oxygen, as therapeutic targets for stimulating angiogenesis in wounds. Our modeling results indicated that simultaneous inhibition of TGF-β and supplementation of either FGF-2 or ANG-2 could be more effective in stimulating wound angiogenesis than the modulation of either protein alone. Our findings suggest experimentally testable intervention strategies to restore angiogenesis in wounds with delayed healing.

Keywords: Wound Healing, Angiogenesis, computational analysis, Endothelial Cells, VEGF

Received: 16 Oct 2018; Accepted: 06 May 2019.

Edited by:

Shankar Subramaniam, University of California, San Diego, United States

Reviewed by:

Hermann Frieboes, University of Louisville, United States
Stacey D. Finley, University of Southern California, United States  

Copyright: © 2019 Nagaraja, Chen, DiPietro, Reifman and Mitrophanov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jaques Reifman, Biotechnology HPC Software Applications Institute (BHSAI), Frederick, MD 21702, Maryland, United States, jaques.reifman.civ@mail.mil