Wound healing mechanism in Mongolian gerbil skin
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The skin wound healing ability of animals differs depending on the environment. The gerbil wound model showed a different wound healing mechanism than was known thus far. Many other wound healing mechanisms have been found to involve transforming growth factor-beta 1 (TGF-β1). However, in the wound healing of gerbil skin, the expression of TGF-β1 seems to be not enough compared to mouse. In this study, we compared the wound healing process of gerbil and mouse back skin. At 3 days after wounding, the TGF-β1 level was downregulated in gerbil skin wound healing compared mouse. In addition, gerbils have fewer integrin signals related to the regulation of TGF-β activation and signaling. Despite lacking these factors, the wound healing results in the gerbil are similar to those for skin wound healing in mice. In contrast, in gerbil skin wound healing, the basal skin layer showed hyperplasia in re-epithelialization, more production of hair follicles, and low probability of collagen infiltration at the late stages of wound healing. These data suggest that different wound healing mechanisms are present in the mammals.
KeywordsGerbil Mouse Skin wound TGF-β1 Integrin
This research was financially supported by grants from the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (NRF-2017M3A9B3061833).
- Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300Google Scholar
- Li L, Tang Q, Nakamura T, Suh JG, Ohshima H, Jung HS (2016b) Fine tuning of Rac1 and RhoA alters cuspal shapes by remolding the cellular geometry. Sci Rep 28:1–12Google Scholar
- Reynolds LE, Conti FJ, Silva R, Robinson SD, Iyer V, Rudling R et al (2008) α3β1 integrin-controlled Smad7 regulates reepithelialization during wound healing in mice. J Clin Invest 118:965–974Google Scholar
- Sun J, Nishiyama T, Shimizu K, Kadota K (2013) TCC: an R package for comparing tag count data with robust normalization strategies. Bioinformatics 14:214–219Google Scholar