In vitro biological activities of Douglas fir essential oil in a human skin disease model

Figures & data

Figure 1. Bioactivity profile of Douglas fir essential oil (DEO, 0.0037% v/v) in human dermal fibroblast system HDF3CGF.

Notes: The X-axis indicates protein biomarker readouts. The Y-axis denotes log₁₀ transformed relative expression levels of biomarkers following the exposure of cells to DEO compared to the levels observed in vehicle-treated control cells. Vehicle control values are shaded in gray, denoting 95% confidence level. Asterisks indicate biomarker “key activity,” as DEO-induced changes in their expression levels were significantly different (p < 0.05) from those in vehicle-treated controls with an effect size of at least 10% (more than 0.05 log ratio units). MCP-1, monocyte chemoattractant protein; VCAM-1, vascular cell adhesion molecule 1; ICAM-1, intracellular cell adhesion molecule 1; IP-10, interferon gamma-induced protein 10; I-TAC, interferon-inducible T-cell alpha chemoattractant; IL-8, interleukin-8; MIG, monokine induced by gamma interferon; EGFR, epidermal growth factor receptor; M-CSF, macrophage colony-stimulating factor; MMP-1, matrix metalloproteinase 1; PAI-1, plasminogen activator inhibitor 1; TIMP, tissue inhibitor of metalloproteinase.

Figure 2. Top 20 canonical pathways that match with the profile of biological effects of Douglas fir essential oil (DEO, 0.0037%, v/v) on gene expression in the HDF3CGF system.

Notes: Calculations were made using QIAGEN Ingenuity Pathway Analysis. Each p value, describing the likelihood that the observed association between a specific pathway and the data-set is due to random chance, was obtained by using the right-tailed Fisher’s exact test. Pathways with smaller actual P value (or conversely, bigger −ln [p-value], indicated by black bars) match more significantly with DEO biological activity profile. The ratios, indicated by gray bars, were calculated by considering the number of genes that participate in a given canonical pathway from the DEO data-set, and dividing it by the total number of genes in that pathway.