Characterization of the acute temporal changes in excisional murine cutaneous wound inflammation by screening of the wound-edge transcriptome

Abstract

This work represents a maiden effort to systematically screen the transcriptome of the healing wound-edge tissue temporally using high-density GeneChips. Changes during the acute inflammatory phase of murine excisional wounds were characterized histologically. Sets of genes that significantly changed in expression during healing could be segregated into the following five sets: up-early (6-24 h; cytokine-cytokine receptor interaction pathway), up-intermediary (12-96 h; leukocyte-endothelial interaction pathway), up-late (48-96 h; cell-cycle pathway), down-early (6-12 h; purine metabolism) and down-intermediary (12-96 h; oxidative phosphorylation pathway). Results from microarray and real-time PCR analyses were consistent. Results listing all genes that were significantly changed at any specific time point were further mined for cell-type (neutrophils, macrophages, endothelial, fibroblasts, and pluripotent stem cells) specificity. Candidate genes were also clustered on the basis of their functional annotation, linking them to inflammation, angiogenesis, reactive oxygen species (ROS), or extracellular matrix (ECM) categories. Rapid induction of genes encoding NADPH oxidase subunits and downregulation of catalase in response to wounding is consistent with the fact that low levels of endogenous H2O2 is required for wound healing. Angiogenic genes, previously not connected to cutaneous wound healing, that were induced in the healing wound-edge included adiponectin, epiregulin, angiomotin, Nogo, and VEGF-B. This study provides a digested database that may serve as a valuable reference tool to develop novel hypotheses aiming to elucidate the biology of cutaneous wound healing comprehensively.

Figures

Fig. 1.

Secondary-intention excisional dermal wound closure in mice. A: two 16 × 8 mm full-thickness rectangular (inset) excisional wounds were placed on the dorsal skin, equidistant from the midline and adjacent to the 4 limbs. These wounds were left to heal by secondary intention. Wound closure is shown as percentage of area of initial wound determined on the indicated day after wounding. The shaded area indicates time period selected for the study of gene expression profile in wound edge tissue postinjury. Data are means ± SD, n = 4. *P < 0.05; **P < 0.001 compared with 0 h time point postwound. B: definition of wound edge tissue used for microarray analysis. The dotted area represents the wound edge tissue. Tattooing was performed on 4 corners (filled circles) of the wound site to keep track of the edges. To minimize the possible interfering effects of tattoo-related trauma, the procedure was performed 1 wk ahead of the wounding. The tissue containing tattoo dots was excluded from analyses.

Fig. 2.

Histological characterization of healing wound tissue used for gene expression profiling studies. Wound (Fig. 1) tissues from excisional wounds were collected at indicated time postinjury. Formalin-fixed paraffin sections or OCT-embedded frozen sections were stained using Masson Trichrome procedure (A). This procedure results in blue-black nuclei, blue collagen and cytoplasm. Epidermal cells are stained in red. i: Low magnification (×1.25) images showing cross-section of entire wounds at 6 and 96 h postwounding. Boxed area shows the wound edge tissue shown in ii and iii. Wound-edge tissue imaged with ×5 (ii) or ×20 (iii) objectives. Scale bar = 200 μm (for ii) and 50 μm (for iii). D, dermis; EGT, early granulation tissue; Es, Eschar tissue; FP, fibrin plug; HE, hyperproliferative epithelium. The newly forming epithelial tip is shown with an arrow. Alternatively, sections were immunostained as shown in B. B, i: Antineutrophil antibody (brown, shown with black arrow) that recognizes a polymorphic 40 kDa antigen expressed by polymorphonuclear cells. Counterstaining was performed using hematoxylin (blue); ii: F4/80 antibody (brown, shown with black arrow) that recognizes the murine F4/80 antigen, a 160 kDa cell surface glycoprotein expressed on a wide range of mature tissue macrophages. Counterstaining was performed using hematoxylin (blue); iii: Cd31 antibody (red fluorescence, shown with white arrow) that recognizes the mouse CD31, a 140 kDa cell surface glycoprotein that is expressed at high levels on endothelial cells. Counterstaining was performed using DAPI (blue, fluorescence). Scale bar, 50 μm. iv: Relative quantification (arbitrary units) of neutrophils, macrophages, and CD31+ endothelial cells from tissue sections obtained 6–96 h postwound was performed using a image processing tool kit. Data are means ± SD (n = 3). *P < 0.05 compared with 6 h time point postwounding.

Fig. 3.

GeneChip data analysis scheme used to identify kinetics of differentially expressed genes in dermal wounds. Image acquisition and processing were performed using GCOS (GeneChip operating software, Affymetrix). GC-RMA was applied for data normalization and background correction. ArrayAssist v5.1 software was used to identify significant (P < 0.05; false discovery rate corrected) differentially expressed genes in wound tissue compared with skin (0 h) from the site where wound was created. Details of software and other resources for data analysis have been provided in methods.

Fig. 4.

Number of differentially expressed genes in course of healing. Total number of genes differentially expressed (significant, P < 0.05) at a specific time postwounding. Gray shaded area in each bar shows number of downregulated vs. the solid area that shows number of upregulated genes.

Fig. 5.

Cluster of genes showing specific pattern of expression in wound tissue during healing. All genes that were significantly changed at any single time point were subjected to hierarchical clustering. Five major clusters of genes that change during the course of healing were identified. Major functional categories in each of these clusters were identified and are presented in Table 1.

Fig. 6.

Real-time PCR validation of GeneChip microarray expression analysis. Expression levels of selected genes identified (Tables S2–S6) using GeneChip analysis were independently determined using real-time PCR. The correlation coefficient (r) of data obtained from microarray analysis vs. real-time PCR is shown for each of the gene. The regression is derived from 4 pairs of data at each time point for a total number of 24. PCR data represent means ± SD, n = 5. The animals used for real-time PCR were a set that was independent of the animals used for microarray analysis. Irg-1, immunoresponsive gene 1; IL-1β, interleukin 1 beta; JunB, Jun-B oncogene; Rrad, Ras-related associated with diabetes; CXCL5, chemokine (C-X-C motif) ligand 5; Upp1, uridine phosphorylase 1; Cyba, cytochrome b-245, alpha polypeptide (p22 phox); Saa3, serum amyloid A 3; Krtb6, keratin 6B; MMP11, matrix metallopeptidase 11.

Fig. 7.

Visualization of the expression pattern of candidate genes representing wound-specific cell types in a healing wound. All genes that were significantly changed at any single time point were subjected to specific filtration for wound cell type specific genes. The following 5 major wound cell types were targeted: neutrophils (A), macrophages (B), endothelial cells (C), fibroblasts (D), and pluripotent stem cells (E). Further subsets based on patterns of kinetics within each of the cell type were identified and indicated on margins of each cluster. Annotations of each cell type-specific subclusters (e.g., i and ii) are presented in Tables 2, A–E.

Fig. 8.

Visualization of the expression pattern of candidate genes representing repair-specific functional categories in a healing wound. All genes that were significantly changed at any single time point were subjected to specific filtration for process/factors-specific genes. The following processes/factors that play a major role in wound healing were targeted: inflammation (A), angiogenesis (B), reactive oxygen species (ROS, C), and extracellular matrix (ECM, D). Further subsets based on patterns of kinetics within each of the processes were identified and indicated on margins of each cluster. Annotations of each process-specific subclusters are presented in Tables 3, A–D.