ABSTRACT: Covering denuded dermal surface after injury requires migration, proliferation and differentiation of skin keratinocytes. To clarify the major traits controlling these intermingled biological events, we surveyed the genomic modifications occurring during the course of a scratch closure of cultured human keratinocytes. Using a DNA microarray approach, we report the identification of 161 new markers of epidermal repair. Expression data, combined with functional analysis performed with specific inhibitors of ERK, p38[MAPK] and PI3 kinases, demonstrate that kinase pathways exert very selective functions by precisely controlling the expression of specific genes. Inhibition of the ERK pathway totally blocks the wound closure and inactivates many early transcription factors and EGF-type growth factors. P38[MAPK] inhibition only delays âhealingâ, probably in line with the control of genes involved in the propagation of injury-initiated signalling. In contrast, PI3 kinase inhibition accelerates the scratch closure and potentiates the scratch-dependent stimulation of three genes related to epithelial cell transformation, namely HAS3, HBEGF and Ets1. Our results define in vitro human keratinocyte wound closure as a reparation process resulting from a fine balance between positive signals controlled by ERK and p38[MAPK], and negative ones triggered off by PI3 kinase. The perturbation of any of these pathways might lead to dysfunction in the healing process, as those observed in pathological wounding phenotypes, such as hypertrophic scars or keloids. Keywords: Transcriptome of healing keratinocytes The DNA microarray analysis concerning the identification of the genes altered in response to cell scratching was performed on 3 independent experiments, derived from 3 distinct cell cultures. Results were generated after 2 independent microarray analyses, meaning that each time point was the result of 6 independent analyses. The data relative to the effect of U0126, SB203580 and LY294002 on the transcriptomal healing response were obtained from 2 independent experiments performed using 2 distinct cell cultures. Results were generated after 2 independent microarray analyses. The cDNA microarray contained 4.200 distinct cDNA probes. It has been previously described in Moreilhon et al (11). Gene selection was based on relevance to inflammation, infection, differentiation, ion transport, cell signaling, cell migration, proliferation etc⦠A large fraction of the probes also corresponded to transcripts encoding membrane proteins. The list of the 4.200 probes is available at http://www.microarray.fr/IPMC/cDNA_microarray5k.html, and the microarray is archived in GEO under reference GSE1853. The cDNA probes were PCR amplified from cDNA derived from Universal Human Reference RNA (Stratagene) by reverse transcription. Probes had the following properties: 1) they have a normalized length of 250 +/- 19 bp; 2) they have a normalized GC content of 52 +/- 8%; 3) they were specific for a unique human gene; and 4) they were controlled by DNA sequencing. PCR products were purified by using QIAquick 96 PCR Purification Kit (Qiagen), resuspended in 3SSC with an average spotting concentration of 200 ng/μl. Microarrays were printed with a SDDC-2 (Bio-Rad) on homemade aldehyde-coated glass microscope slides. Data presented into the present manuscript only refer to sequence-verified probes. Each PCR product was spotted four times on each slide (2 independent clusters of 2 spots spatially separated), to reduce positional bias of the fluorescence readout. The CyDye-labelled first-strand cDNAs were generated with the CyScribe First-Strand cDNA Labelling Kit (RPN 2600, Amersham Pharmacia Biotech), as described in Moreilhon et al, using ten micrograms of total RNA as template. Unincorporated CyDye-nucleotides were removed using the Nucleotide Removal Kit (Qiagen). Data Collection and Analysis : Data Collection : Microarrays were scanned either on GenePix4000B or on ScanArray5000 (Perkin Elmer). Both machines provided similar results (not shown). 16-bits TIF images were quantified with the corresponding softwares (GenePix Pro 5.0 program (Axon Instruments) for the GenePix, and Quantarray for the ScanArray). Intensities (either total or background values) were defined as average intensities for each spot. Negative controls (ânegâ) were spotted on each slide. They corresponded to non mammalian mRNA sequences with no significant identity with any human sequences. Such spots were used as controls. Data was normalized using a dye-swap method as described by Moreilhon et al.. We found that this method, while requiring duplication of experiments, improves the reproducibility of the quantification. Scratched (S) and non-scratched (NS) RNA samples were reverse transcribed in the presence of known amounts of the corresponding non mammalian âcontrolâ RNAs namely DmdNaC and DGNaC added at a respective S/NS ratio of 5, 0.2. Only experiments showing a good correlation between the experimental and true ratios for these controls were kept for analysis. Results are expressed as average ratios of intensities in âscratched cellsâ over intensities in ânon-scratched cellsâ. Data was then analyzed and/or visualized with MEV , or with the stand-alone program GeneANOVA, and the SAMâs Excel⢠plugin . Lists of genes significantly down and up-regulated under the different experimental conditions were selected by analysis of variance or using SAM. For the analysis of variance, signal was modelled as proposed by Kerr and Churchill (17) according to: yijkg = μ + Ai + Dj + Tk + Gg + (TG)kg + (AG)ig + (DG)jg + εijkg (1), where yijkg is the fluorescent intensity of array i and dye j representing treatment k and gene g. μ, Ai, Dj, Tk, and Gg are factors representing average signals among the whole experiment, one array, one dye, one treatment, or one gene, respectively. (TG)kg, (AG)ig, and (DG)jg are two-factor interactions between treatment and gene, array and gene (spot effects), dye and gene, respectively. εijkg represents a residual noise component, modelled as a Gaussian distribution with mean 0 and variance ϲ. (TG)kg denotes differences in expression for particular treatment and gene combinations that are not explained by the average effects on these treatments and genes. They represent the effects of interest, which were modelled using GeneANOVA. SAM analysis was performed as described in Tusher et al. (16). Ontologies attached to each gene were then used to classify altered genes according to main biological themes. Annotation of our probes was also accessible through MEDIANTE, our own microarray database which stores annotations derived from public databases. Additional statistical analyses, including principal component analysis (PCA) were performed with GeneANOVA.
