Members of the epidermal growth factor (EGF) family are the most important growth factors involved in epithelialization during cutaneous wound healing. Heparin-binding EGF-like growth factor (HB-EGF), a member of the EGF family, is thought to play an important role in skin wound healing. To investigate the in vivo function of HB-EGF in skin wound healing, we generated keratinocyte-specific HB-EGF-deficient mice using Cre/loxP technology in combination with the keratin 5 promoter. Studies of wound healing revealed that wound closure was markedly impaired in keratinocyte-specific HB-EGF-deficient mice. HB-EGF mRNA was upregulated at the migrating epidermal edge, although cell growth was not altered. Of the members of the EGF family, HB-EGF mRNA expression was induced the most rapidly and dramatically as a result of scraping in vitro. Combined, these findings clearly demonstrate, for the first time, that HB-EGF is the predominant growth factor involved in epithelialization in skin wound healing in vivo and that it functions by accelerating keratinocyte migration, rather than proliferation.
Age prediction with epigenetic information is now edging closer to practical use in forensic community. Many age-related CpG (AR-CpG) sites have proven useful in predicting age in pyrosequencing or DNA chip analyses. In this study, a wide range methylation status in the ELOVL2 and FHL2 promoter regions were detected with methylation-sensitive high resolution melting (MS-HRM) in a labor-, time-, and cost-effective manner. Non-linear-distributions of methylation status and chronological age were newly fitted to the logistic curve. Notably, these distributions were revealed to be similar in 22 living blood samples and 52 dead blood samples. Therefore, the difference of methylation status between living and dead samples suggested to be ignorable by MS-HRM. Additionally, the information from ELOVL2 and FHL2 were integrated into a logistic curve fitting model to develop a final predictive model through the multivariate linear regression of logit-linked methylation rates and chronological age with adjusted R(2)=0.83. Mean absolute deviation (MAD) was 7.44 for 74 training set and 7.71 for 30 additional independent test set, indicating that the final predicting model is accurate. This suggests that our MS-HRM-based method has great potential in predicting actual forensic age.
There is high demand for forensic age prediction in actual crime investigations. In this study, a novel age prediction model for saliva samples using methylation-sensitive high resolution melting (MS-HRM) was developed. The methylation profiles of ELOVL2 and EDARADD showed high correlations with age and were used to predict age with support vector regression. ELOVL2 was first reported as an age predictive marker for saliva samples. The prediction model showed high accuracy with a mean absolute deviation (MAD) from chronological age of 5.96 years among 197 training samples. The model was further validated with an additional 50 test samples (MAD = 6.25). In addition, the age prediction model was applied to saliva extracted from seven cigarette butts, as in an actual crime scene. The MAD (7.65 years) for these samples was slightly higher than that of intact saliva samples. A smoking habit or the ingredients of cigarettes themselves did not significantly affect the prediction model and could be ignored. MS-HRM provides a quick (2 hours) and cost-effective (95% decreased compared to that of DNA chips) method of analysis. Thus, this study may provide a novel strategy for predicting the age of a person of interest in actual crime scene investigations.
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