Temporal expression of wound healing–related genes inform wound age estimation in rats after a skeletal muscle contusion: a multivariate statistical model analysis
Although many time-dependent parameters involved in wound healing have been exhaustively investigated, establishing an objective and reliable means for estimating wound age remains a challenge. In this study, 78 Sprague–Dawley rats were divided randomly into a control group and contusion groups at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h post-injury (n = 6 per group). The expression of 35 wound healing–related genes was explored in contused skeletal muscle by real-time polymerase chain reaction. Differences between the groups were assessed by partial least squares discriminant analysis (PLS-DA). The results show that the samples were classified into three groups by wound age (4–12, 16–24, and 28–48 h). A Fisher discriminant analysis model of 14 selected genes was constructed, and 94.9% cross-validated grouped cases were correctly classified. A PLS regression analysis using 14 genes showed reasonable internal predictive validity, with a root mean squared error of cross-validation of approximately 8 h. To examine whether the prediction models were capable of analyzing new (ungrouped) cases, an external validation was carried out using the expression data from an additional 30 rats. Approximately 76.7% of ungrouped cases were correctly classified, which was a lower proportion than that for cross-validation. Similarly, the prediction results of the PLS model showed lower relatively external predictive validity (root mean squared error of prediction = 11 h) than internal predictive validity. Although the prediction results were less accurate than expected, the gene expression modeling and multivariate analyses showed great potential for estimating injury time. These multivariate methods may be valuable when devising future wound time estimation strategies.
KeywordsFDA Multivariate statistical model analysis PLSR Real-time PCR Skeletal muscle contusion Wound age estimation
This project is financially supported by grants from the National Natural Science Foundation of China (grant numbers 81601646, 81571852).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants performed by any of the authors. The principles of the Guide for the Care and Use of Laboratory Animals protocol, published by the Ministry of the People’s Republic of China, were followed.
- 1.Wang L-L, Zhao R, Liu C-S, Liu M, Li S-S, Li J-Y, Jiang S-K, Zhang M, Tian Z-L, Wang M, Zhang M-Z, Guan D-W (2016) A fundamental study on the dynamics of multiple biomarkers in mouse excisional wounds for wound age estimation. J Forensic Legal Med 39:138–146. https://doi.org/10.1016/j.jflm.2016.01.027 CrossRefGoogle Scholar
- 3.Yagi Y, Murase T, Kagawa S, Tsuruya S, Nakahara A, Yamamoto T, Umehara T, Ikematsu K (2016) Immunohistochemical detection of CD14 and combined assessment with CD32B and CD68 for wound age estimation. Forensic Sci Int 262:113–120. https://doi.org/10.1016/j.forsciint.2016.02.031 CrossRefGoogle Scholar
- 4.Sun JH, Zhu XY, Dong TN, Zhang XH, Liu QQ, Li SQ, Du QX (2017) An “up, no change, or down” system: time-dependent expression of mRNAs in contused skeletal muscle of rats used for wound age estimation. Forensic Sci Int 272:104–110. https://doi.org/10.1016/j.forsciint.2017.01.012 CrossRefGoogle Scholar
- 6.Liu R, Sun Q, Hu T, Li L, Nie L, Wang J, Zhou W, Zang H (2018) Multi-parameters monitoring during traditional Chinese medicine concentration process with near infrared spectroscopy and chemometrics. Spectrochim Acta A Mol Biomol Spectrosc 192:75–81. https://doi.org/10.1016/j.saa.2017.10.068 CrossRefGoogle Scholar
- 8.Zhu X-y, Du Q-x, Li S-q, Sun J-h (2016) Comparison of the homogeneity of mRNAs encoding SFRP5, FZD4, and Fosl1 in post-injury intervals: subcellular localization of markers may influence wound age estimation. J Forensic Legal Med 43:90–96. https://doi.org/10.1016/j.jflm.2016.07.013 CrossRefGoogle Scholar
- 10.Hassan Gaballah M, Fukuta M, Maeno Y, Seko-Nakamura Y, Monma-Ohtaki J, Shibata Y, Kato H, Aoki Y, Takamiya M (2016) Simultaneous time course analysis of multiple markers based on DNA microarray in incised wound in skeletal muscle for wound aging. Forensic Sci Int 266:357–368. https://doi.org/10.1016/j.forsciint.2016.06.027 CrossRefGoogle Scholar
- 12.van de Goot FR, Korkmaz HI, Fronczek J, Witte BI, Visser R, Ulrich MM, Begieneman MP, Rozendaal L, Krijnen PA, Niessen HW (2014) A new method to determine wound age in early vital skin injuries: a probability scoring system using expression levels of fibronectin, CD62p and factor VIII in wound hemorrhage. Forensic Sci Int 244:128–135. https://doi.org/10.1016/j.forsciint.2014.08.015 CrossRefGoogle Scholar
- 18.Tian ZL, Jiang SK, Zhang M, Wang M, Li JY, Zhao R, Wang LL, Li SS, Liu M, Zhang MZ, Guan DW (2016) Detection of satellite cells during skeletal muscle wound healing in rats: time-dependent expressions of Pax7 and MyoD in relation to wound age. Int J Legal Med 130(1):163–172. https://doi.org/10.1007/s00414-015-1251-x CrossRefGoogle Scholar
- 19.Fronczek J, Lulf R, Korkmaz HI, Witte BI, van de Goot FR, Begieneman MP, Schalkwijk CG, Krijnen PA, Rozendaal L, Niessen HW, Reijnders UJ (2015) Analysis of inflammatory cells and mediators in skin wound biopsies to determine wound age in living subjects in forensic medicine. Forensic Sci Int 247:7–13. https://doi.org/10.1016/j.forsciint.2014.11.014 CrossRefGoogle Scholar
- 21.Li N, Du Q, Bai R, Sun J (2018) Vitality and wound-age estimation in forensic pathology: review and future prospects. FSR 1–10. https://doi.org/10.1080/20961790.2018.1445441
- 25.Sun J-h, Wang Y-y, Zhang L, Gao C-r, Zhang L-z, Guo Z (2009) Time-dependent expression of skeletal muscle troponin I mRNA in the contused skeletal muscle of rats: a possible marker for wound age estimation. Int J Legal Med 124(1):27–33. https://doi.org/10.1007/s00414-009-0323-1 CrossRefGoogle Scholar
- 26.Fisher BD, Baracos VE, Shnitka TK, Mendryk SW, Reid DC (1990) Ultrastructural events following acute muscle trauma. Med Sci Sports Exerc 22(2):185–193Google Scholar
- 29.Toziou PM, Barmpalexis P, Boukouvala P, Verghese S, Nikolakakis I (2018) Quantification of live lactobacillus acidophilus in mixed populations of live and killed by application of attenuated reflection Fourier transform infrared spectroscopy combined with chemometrics. J Pharm Biomed Anal 154:16–22. https://doi.org/10.1016/j.jpba.2018.03.009 CrossRefGoogle Scholar