Identification of genes induced and repressed in response to injury

In this meta-analysis we attempted to assemble the greater part of transcriptomic data on gene expression in response to wounding. We compared the available transcriptomic profiles of injured tissues in the search of the traits shared by different wound models. The analysed gene expression profiles represent diverse tissues including heart, liver, skin, bones, epithelia, brain, spinal cord and nerves from a few species (rat, mouse, human), and different stages of wound response as well as injury types.

The datasets we assembled were divided into five groups corresponding to the sequence of wound response phases: haemostasis & early inflammation, inflammation, early wound repair, late wound repair, and remodelling. We ranked the transcripts in each group according to the percentage of samples displaying a twofold change in expression to sort out top 100 up- and top 100 downregulated genes (Additional file 2).

Common traits of transcriptomic responses to wound in different tissues

Despite the diversity of analysed tissues, we found a number of transcripts displaying similar responses to injury in the major part of samples within the distinguished phases. In order to show the representative examples of transcriptional responses to wounding, distinctive of each tissue, we prepared lists of 20 top-ranked genes induced and repressed in each phase (Figs. 4 and 5). In this synthetic view, the genes that displayed at least a twofold change in expression in a greater number of experiments for a given tissue are indicated as either up- or downregulated. The levels of most transcripts analysed from 4 to 24 h showed neither significant increase nor decrease, as it is shown by the heat map of expression fold changes (Figs. 4 and 5, Additional file 2). For gene expression profiles in the initial phases of healing: haemostasis &early inflammation and inflammation, we revealed disparate transcriptomic responses to wound. In the later phases of repair and remodelling, a number of genes showed similar expression profiles in different tissues (Figs. 4 and 5).

Fig. 4 Twenty top-ranked upregulated genes representative of different wound healing phases. Red fields indicate at least a twofold upregulation in the prevailing part of experiments. Green fields indicate at least a twofold downregulation in the prevailing part of experiments. Black fields indicate no substantial changes in gene expression and blank fields indicate no data Full size image

Fig. 5 Twenty top-ranked downregulated genes representative of different wound healing phases. Red fields indicate at least a twofold upregulation in the prevailing part of experiments. Green fields indicate at least a twofold downregulation in the prevailing part of experiments. Black fields indicate no substantial changes in gene expression and blank fields indicate no data Full size image

Gene ontology analysis

In order to explore the functional associations of genes displaying changes in expression after wounding, we employed gene set enrichment analysis. The results of the analysis are summarized in Fig. 6. We found an enrichment of upregulated genes associated with inflammatory response, chemotaxis of monocyte macrophage and neutrophils, cell proliferation and apoptosis for all phases of response to wounding, while of those related to chemokine activity and eosinophil migration only until early wound repair. In the haemostasis and inflammation phases, we revealed induction of genes involved in response to vitamin D and corticosterone. The later phases of wound healing showed increased expression of genes responsible for collagen fibril synthesis and organisation as well as skin development.

Fig. 6 Ontological categories associated with the genes differentially regulated in different phases of wound response. The gene set enrichment analyses were performed for the top 100 up- and the top 100 downregulated genes for each phase of wound response. The ontological terms were statistically significant as determined by Fisher’s exact test and Benjamini correction for multiple comparisons (p-value adjusted <0.05) Full size image

An analogical analysis conducted for downregulated transcripts exposed an enrichment of downregulated genes involved in carbohydrate and lipid metabolism with the exception for the remodelling phase. Early phases of response to wounding showed an overrepresentation of downregulated genes responsible for cell cycle arrest.

Genes differentially regulated along different wound healing phases

The transcriptomic response to wounding is highly variable dependent on injured tissue, the type of injury and the time following injury. However, we were able to distinguish the genes differentially regulated in the majority of analysed datasets in different phases of wound response in different tissues. We singled out 11 genes upregulated and 1 downregulated through the whole process of wound healing. The expression patterns of those genes are presented in Fig. 7 and their functions are summarized in Table 1. This set of genes is significantly enriched for those associated with extracellular matrix organisation and disassembly, inflammatory response and chemotactic activity (Table 2).

Fig. 7 The genes upregulated along different phases of wound response and healing in different tissues. Red fields indicate at least a twofold upregulation in the prevailing part of experiments. Green fields indicate at least a twofold downregulation in the prevailing part of experiments. Black fields indicate no substantial changes in gene expression and blank fields indicate no data Full size image

Table 1 Functions of genes upregulated along different phases of wound response and healing in different tissues Full size table

Table 2 Functional annotation for 12 genes differentially regulated throughout all phases of wound response Full size table

Genes induced by wounding, such as SERPINA3 could be predicted to improve wound repair. Indeed, topical administration of Serpina3 rescues impaired wound healing in diabetic mice [16]. If a gene is involved in wound response, the deficiency (knockout) of this gene is expected to entail an impaired wound healing. This occurs in the case of a few upregulated genes we distinguished in this study such as CCL2 and LGALS3. The knockout of the Ccl2 gene in mice leads to impaired re-epithelisation and angiogenesis in skin wounds [17]. The Lgals3 knockout was shown to reduce the re-epithelisation rate in murine skin, but the overall wound healing rate remained largely unaffected. Nevertheless, it might be an important factor in chronic wound development [18]. However, there are animal models where positive impacts of single gene deletion have been shown. Improved or accelerated wound healing have been reported in the case of a few other genes upregulated during different wound healing phases as found in this analysis (Fig. 7), Cd44 and Mmp12. The Cd44 deficiency was reported to improve tendon healing [19]. Also the deficiency of Mmp12 was found to have some positive effects on wound repair [20]. We could list a number more of knockout models reported to show either accelerated or improved wound healing including Hif1a [21], Hoxb13 [22], Ifng [23], Il10 [24], Sparc [25], Tgfbr2 [26], Il1r1 [27], and Tnfrsf1a [28]. The mechanisms of healing were developed in the course of evolution for rapid decontamination and closing wounds. The examples of improved or accelerated healing in the knockout models indicate that response to wounding could be modified in a number of ways so as to be directed into regenerative repair.

The group of 12 transcripts which display the changes in expression levels throughout different stages of wound response and repair include mainly the genes of established roles in this process. However, two of them, PTPRC and AQP4 have not been described as important factors associated with wound response or wound healing and these genes are worth considering as candidates for further wound repair and regeneration studies.

Critical remarks

It should be noted that the examination of wound response using microarray techniques is connected with several fundamental problems. The excision of wound area - it is difficult to obtain sufficient amounts of tissues and to excise precisely the injured tissue without collecting the neighbouring normal tissues. Dynamics of the response to injury and wound repair - the available microarray profiling methods allow the examination of several time points, but not continuous tracking of the process. In addition, the transcriptomic profiles reflect not only the alterations in gene expression levels but also the changing content of cell types resulting from cell proliferation, cell death and the accumulation of incoming cells. The limitations of microarray profiling - most expression microarrays do not include a substantial part of known genes, in particular the miRNA genes. The regulation on the translational level – a substantial proportion of genes are regulated on the posttranscriptional level [29].

The interpretation of microarray results adds another complication. Different probe sets for the same gene may produce dramatically different signals. Such different signals may reflect either the presence of two splicing variants or incomplete synthesis of cDNA of long transcripts. In this study, we assumed the algorithm selecting these probe sets for which the highest changes in gene expression were determined.

We would like to stress that the fold changes given here are not supposed to show the accurate expression levels but to visualize trends rather. The exact determination of expression change for a specific gene requires careful validation with alternative reference methods and splice variants should be considered in such examination. This is why we focused the analysis on overall tendencies. It seems that the approach we assumed, was factual, as our analysis indicated a number of genes, which with no doubt are associated with wound response.

The experimental conditions are not standardized, which could be considered as a weakness of this meta-analysis. On the other hand, this diversity of experimental conditions eliminates, in some way, potential batch effects, thus facilitating the search for common traits in wound response.