Abstract
BackgroundPathological scarring in wounds is a prevalent clinical outcome with limited prognostic options. The objective of this study was to investigate whether cellular signaling proteins could be used as prognostic biomarkers of pathological scarring in traumatic skin wounds.MethodsWe used our previously developed and validated computational model of injury-initiated wound healing to simulate the time courses for platelets, 6 cell types, and 21 proteins involved in the inflammatory and proliferative phases of wound healing. Next, we analysed thousands of simulated wound-healing scenarios to identify those that resulted in pathological (i.e., excessive) scarring. Then, we identified candidate proteins that were elevated (or decreased) at the early stages of wound healing in those simulations and could therefore serve as predictive biomarkers of pathological scarring outcomes. Finally, we performed logistic regression analysis and calculated the area under the receiver operating characteristic curve to quantitatively assess the predictive accuracy of the model-identified putative biomarkers.ResultsWe identified three proteins (interleukin-10, tissue inhibitor of matrix metalloproteinase-1, and fibronectin) whose levels were elevated in pathological scars as early as 2 weeks post-wounding and could predict a pathological scarring outcome occurring 40 days after wounding with 80% accuracy.ConclusionOur method for predicting putative prognostic wound-outcome biomarkers may serve as an effective means to guide the identification of proteins predictive of pathological scarring.
Highlights
Pathological scarring in wounds is a prevalent clinical outcome with limited prognostic options
Our analysis suggested that IL-10, tissue inhibitor of matrix metalloproteinase (TIMP)-1, and fibronectin levels on days 14 and 21 post-wounding could predict, with accuracies of 80 and 86%, respectively, a pathological scarring outcome occurring 40 days after wounding
We found that six proteins—IL-10, tissue inhibitor of matrix metalloproteinase-1 (TIMP-1), IL-6, transforming growth factor-β (TGF-β), chemokine CXCL8, and fibronectin—were characterized by relatively little overlap between their concentration distributions for normal-healing simulations and the corresponding distributions for mild pathological-scarring simulations and severe pathological-scarring simulations on day 40 (Fig. 3a–f )
Summary
Pathological scarring in wounds is a prevalent clinical outcome with limited prognostic options. HTSs are characterized by excess collagen and increased angiogenesis, as well as by the increased presence of proliferative cells and the proteins they release, such as fibronectin, transforming growth factor-β (TGF-β), and matrix metalloproteinase-9 (MMP9) [4,5,6, 8, 9] These characteristics are useful in understanding the pathogenesis of HTSs, informative prognostic biomarkers that can reliably predict the risk of Nagaraja et al J Transl Med (2018) 16:32 pathological scar development are few [4]. Existing predictive indicators of HTSs include both empirical factors (e.g., appearance, size, and closure rate of a wound) and serum biomarkers [e.g., serum concentrations of TGF-β, decorin, interleukin (IL)-6, IL-10, and IL-β] [4, 10, 11] Such predictors are rarely used clinically due to the lack of readily available laboratory tests to measure those proteins. One complementary approach that allows systematic identification of promising biomarkers of pathological wound scarring is computational modeling
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