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• W2000877345 abstract "Covering denuded dermal surfaces 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 wound 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, p38MAPK and phosphatidylinositol 3-kinase (PI3K), 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. p38MAPK inhibition only delays “healing,” probably in line with the control of genes involved in the propagation of injury-initiated signaling. In contrast, PI3K 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 repair process resulting from a fine balance between positive signals controlled by ERK and p38MAPK and negative ones triggered by PI3K. The perturbation of any of these pathways might lead to dysfunction in the healing process, similar to those observed in pathological wounding phenotypes, such as hypertrophic scars or keloids. Covering denuded dermal surfaces 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 wound 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, p38MAPK and phosphatidylinositol 3-kinase (PI3K), 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. p38MAPK inhibition only delays “healing,” probably in line with the control of genes involved in the propagation of injury-initiated signaling. In contrast, PI3K 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 repair process resulting from a fine balance between positive signals controlled by ERK and p38MAPK and negative ones triggered by PI3K. The perturbation of any of these pathways might lead to dysfunction in the healing process, similar to those observed in pathological wounding phenotypes, such as hypertrophic scars or keloids. Epidermal wound healing is a complex physiological process involving multiple cell features, such as proliferation, migration, and differentiation of the keratinocytes situated at the edge of the wound. All of these steps involve a well orchestrated regulation of multiple signaling pathways that control the expression of many genes endowed with diverse crucial functions (growth factors, cytokines, proteases, integrins, cell cycle genes, and extracellular matrix components). Disrupted expression of these molecules or of their signaling pathways can block or accelerate the healing process, thereby leading to chronic wounds or hypertrophic scars or keloids. Transgenic and knock out mice models have largely helped in defining some crucial genes involved in the wound healing response (1Grose R. Werner S. Methods Mol. Med. 2003; 78: 191-216PubMed Google Scholar), but the great majority of the genes involved in skin wound healing remain unidentified. Recently, several global expression studies using DNA microarrays have approached this question in such models and have brought interesting insights in terms of a genomic characterization of wound healing (2Feezor R.J. Paddock H.N. Baker H.V. Varela J.C. Barreda J. Moldawer L.L. Schultz G.S. Mozingo D.W. Physiol. Genomics. 2004; 16: 341-348Crossref PubMed Scopus (40) Google Scholar, 3Pedersen T.X. Leethanakul C. Patel V. Mitola D. Lund L.R. Dano K. Johnsen M. Gutkind J.S. Bugge T.H. Oncogene. 2003; 22: 3964-3976Crossref PubMed Scopus (66) Google Scholar, 4Theilgaard-Monch K. Knudsen S. Follin P. Borregaard N. J. Immunol. 2004; 172: 7684-7693Crossref PubMed Scopus (162) Google Scholar). Obvious structural differences between murine and human skin limit notably the generalization of such data to the wound healing features occurring in human tissues. Such a biological limitation has justified the development of new experimental approaches using human models. At present, only one transcriptomal study relative to human skin wound healing has been published (5Cole J. Tsou R. Wallace K. Gibran N. Isik F. Wound Repair Regen. 2001; 9: 360-370Crossref PubMed Scopus (72) Google Scholar). Although in vivo approaches are relevant to embrace globally what happens during skin wound healing, the complexity provided by the coexistence of many cell types makes difficult the identification of mechanisms specific to keratinocytes. This in vivo approach allowed the analysis of events occurring immediately after injury but was unable to address specific questions about later events affecting cell migration, proliferation, or differentiation. Numerous in vitro studies have addressed these questions, quantifying various parameters associated with reepithelialization. For instance, in vitro “wound healing” has been modeled by treating skin cells with soluble factors known to be secreted in vivo after injury, such as TNFα, 4The abbreviations used are: TNF, tumor necrosis factor; NHK, normal human keratinocyte(s); ECM, extracellular matrix; TGF, transforming growth factor; MAPK, mitogen-activated protein kinase; PI3K, phosphatidylinositol 3-kinase; ERK, extracellular signal-regulated kinase. 4The abbreviations used are: TNF, tumor necrosis factor; NHK, normal human keratinocyte(s); ECM, extracellular matrix; TGF, transforming growth factor; MAPK, mitogen-activated protein kinase; PI3K, phosphatidylinositol 3-kinase; ERK, extracellular signal-regulated kinase. or by serum addition (6Banno T. Gazel A. Blumenberg M. J. Biol. Chem. 2004; 279: 32633-32642Abstract Full Text Full Text PDF PubMed Scopus (239) Google Scholar, 7Iyer V.R. Eisen M.B. Ross D.T. Schuler G. Moore T. Lee J.C. Trent J.M. Staudt L.M. Hudson Jr., J. Boguski M.S. Lashkari D. Shalon D. Botstein D. Brown P.O. Science. 1999; 283: 83-87Crossref PubMed Scopus (1707) Google Scholar) (i.e. conditions that are supposed to mimic the context of wound healing). Such studies have led to the identification of interesting gene profiles. However, these procedures do not take into account either the putative signaling generated by the disruption of the extracellular matrix or the cell to cell interaction, which both play pivotal roles in the healing process. Recently, we have developed an original “wounding” device that creates continuous long scratches within cell cultures. This device increases the number of cells participating in the in vitro “wound” closure (up to 40-50% of the cell layer) when compared with the classical scratch models and allows the detection of a wide spectrum of molecular events stimulated in response to injury (8Turchi L. Chassot A.A. Rezzonico R. Yeow K. Loubat A. Ferrua B. Lenegrate G. Ortonne J.P. Ponzio G. J. Invest. Dermatol. 2002; 119: 56-63Abstract Full Text Full Text PDF PubMed Scopus (55) Google Scholar, 9Turchi L. Chassot A.A. Bourget I. Baldescchi C. Ortonne J.P. Meneguzzi G. Lemichez E. Ponzio G. J. Invest. Dermatol. 2003; 121: 1291-1300Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar). Interestingly, this system can generate at the same time shear stress signals and soluble derived factors. It thus represents a powerful tool to investigate the global alterations of gene expression occurring throughout the “healing” process of normal human keratinocytes (NHK) in culture. Combining a DNA microarray approach with our “wounding” device, we have 1) performed an accurate analysis of the global gene expression profile in cultured human keratinocytes after scratching, 2) identified molecular signatures of the response to injury, and 3) elucidated the molecular and functional contributions of the ERK, PI3K, and p38MAPK pathways during human keratinocyte wound healing. Cell Culture—Human keratinocytes were isolated from healthy neonatal foreskin as described (10Rheinwald J.G. Green H. Cell. 1975; 6: 331-343Abstract Full Text PDF PubMed Scopus (3836) Google Scholar). Cell culture keratinocytes were seeded on γ-irradiated 3T3-J2 fibroblast feeder layers (2 × 105 cells/cm2) and grown in Green’s medium (2/3 Dulbecco’s minimal Eagle’s medium, 1/3 Ham’s F-12 medium, 20 mm HEPES, 1000 units/ml penicillin, 1000 units/ml streptomycin, 5 μg/ml insulin, 0.4 mg/ml hydrocortisone, 10 μm cholera toxin, 10 μm recombinant human EGF, 2 μm triiodothyronine, and 18 μm adenine. Antibodies and Western Blotting—Antibodies against phosphorylated forms of ERK1/2, ATF, and AKT were from Cell Signaling Technology and were used at a 1:6000 dilution. After migration on PAGE and transfer onto Immobilon-P membrane (Millipore), the specific proteins were revealed using the Millipore ECL detection system. Scratching Protocol—The cell scratching was performed using a “scarificator,” previously described by Turchi et al. (8Turchi L. Chassot A.A. Rezzonico R. Yeow K. Loubat A. Ferrua B. Lenegrate G. Ortonne J.P. Ponzio G. J. Invest. Dermatol. 2002; 119: 56-63Abstract Full Text Full Text PDF PubMed Scopus (55) Google Scholar). In classical conditions, 40-50% of the cell culture was scratched, the width between the “wound edges” being about 300-400 μm. The cultures were stopped by washing with phosphate-buffered saline, followed by a quick freezing to -80 °C, at different times after wounding (1, 3, 6, 15, and 24 h). For each time of the kinetic series, nonscratched cells were used as controls. In experiments performed with U0126, SB203580, and LY294002, the inhibitors were preincubated with the cells, 1 h before scratching at a respective concentration of 10, 20, and 15 μm. RNA Extraction—5-10 × 106 cells were quickly homogenized in 10 ml of a 4 m guanidium thiocyanate solution, 25 mm sodium citrate, pH 7.0, 100 mm 2β-mercaptoethanol, 0.5% N-laurylsarcosine, followed by the addition of 1 ml of 2 m sodium acetate, pH 4.0, 8 ml of freshly water-equilibrated phenol, and 2 ml of chloroform. After 15 min on ice, samples were centrifuged for 20 min at 7500 × g. The RNA of the upper aqueous phase was precipitated with one volume of isopropyl alcohol. The sample was incubated 1 h on ice, after which the RNA was pelleted by centrifugation for 20 min at 7500 × g. The pellet was washed twice with 70% ethanol and then resuspended in RNase-free water. cDNA Microarrays—The DNA microarray analysis concerning the identification of the genes altered in response to cell scratching was performed on three independent experiments, derived from three distinct cell cultures. Results were generated after two independent microarray analyses, meaning that each time point was the result of six independent analyses. The data relative to the effect of U0126, SB203580, and LY294002 on the transcriptomal healing response were obtained from two independent experiments performed using two distinct cell cultures. Results were generated after two independent microarray analyses. The cDNA microarray contained 4200 distinct cDNA probes. It has been previously described in Moreilhon et al. (11Moreilhon C. Gras D. Hologne C. Bajolet O. Cottrez F. Magnone V. Merten M. Groux H. Puchelle E. Barbry P. Physiol. Genomics. 2005; 20: 244-255Crossref PubMed Scopus (68) Google Scholar). 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 4200 probes is available on the World Wide Web at 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 a QIAquick 96 PCR purification kit (Qiagen), resuspended in 3SSC with an average spotting concentration of 200 ng/μl. Microarrays were printed with an SDDC-2 microarrayer (Bio-Rad) on homemade aldehyde-coated glass microscope slides. Data presented in the present work only refer to sequence-verified probes. Each PCR product was spotted four times on each slide (two independent clusters of two spots spatially separated) to reduce positional bias of the fluorescence readout. Cy3- and Cy5-labeled cDNA, Postprocessing, and Hybridization—The CyDye-labeled first-strand cDNAs were generated with the CyScribe First-Strand cDNA labeling kit (RPN 2600; Amersham Biosciences), as described by Moreilhon et al. (11Moreilhon C. Gras D. Hologne C. Bajolet O. Cottrez F. Magnone V. Merten M. Groux H. Puchelle E. Barbry P. Physiol. Genomics. 2005; 20: 244-255Crossref PubMed Scopus (68) Google Scholar), using 10 μg of total RNA as template. Unincorporated CyDye nucleotides were removed using the nucleotide removal kit (Qiagen). Data Collection and Analysis—Microarrays were scanned either on GenePix4000B or on a ScanArray5000 microarray scanner (PerkinElmer Life Sciences). Both machines provided similar results (not shown). 16-bit TIF images were quantified with the corresponding software (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 were spotted on each slide. They corresponded to nonmammalian mRNA sequences with no significant identity with any human sequences. Such spots were used as controls. Data were normalized using a dye swap method as described by Moreilhon et al. (11Moreilhon C. Gras D. Hologne C. Bajolet O. Cottrez F. Magnone V. Merten M. Groux H. Puchelle E. Barbry P. Physiol. Genomics. 2005; 20: 244-255Crossref PubMed Scopus (68) Google Scholar). We found that this method, although requiring duplication of experiments, improves the reproducibility of the quantification. Scratched and nonscratched RNA samples were reverse transcribed in the presence of known amounts of the corresponding nonmammalian “control” RNAs, namely DmdNaC and DGNaC (12Darboux I. Lingueglia E. Champigny G. Coscoy S. Barbry P. Lazdunski M. J. Biol. Chem. 1998; 273: 9424-9429Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar, 13Darboux I. Lingueglia E. Pauron D. Barbry P. Lazdunski M. Biochem. Biophys. Res. Commun. 1998; 246: 210-216Crossref PubMed Scopus (47) Google Scholar), added at a respective scratched/nonscratched ratio of 5 and 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 “scratched cells” over intensities in “nonscratched cells.” Data were then analyzed and/or visualized with MEV (14Saeed A.I. Sharov V. White J. Li J. Liang W. Bhagabati N. Braisted J. Klapa M. Currier T. Thiagarajan M. Sturn A. Snuffin M. Rezantsev A. Popov D. Ryltsov A. Kostukovich E. Borisovsky I. Liu Z. Vinsavich A. Trush V. Quackenbush J. BioTechniques. 2003; 34: 374-378Crossref PubMed Scopus (3944) Google Scholar), or with the stand alone program GeneANOVA (15Didier G. Brezellec P. Remy E. Henaut A. Bioinformatics. 2002; 18: 490-491Crossref PubMed Scopus (87) Google Scholar), and the Significance Analysis of Microarrays (SAM) Excel™ plug-in (16Tusher V.G. Tibshirani R. Chu G. Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 5116-5121Crossref PubMed Scopus (9648) Google Scholar). 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, the signal was modeled as proposed by Kerr and Churchill (17Kerr M.K. Churchill G.A. Biostatistics. 2001; 2: 183-201Crossref PubMed Google Scholar) according to Equation 1, Yijkg=μ+Ai+Dj+Tk+Gg+(TG)kg+(AG)ig+(DG)jg+ɛijkgEq. 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), and dye and gene, respectively. ɛijkg represents a residual noise component, modeled as a Gaussian distribution with mean 0 and variance σ2. (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 modeled using GeneANOVA. SAM analysis was performed as described by Tusher et al. (16Tusher V.G. Tibshirani R. Chu G. Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 5116-5121Crossref PubMed Scopus (9648) Google Scholar). Ontologies attached to each gene were then used to classify altered genes according to main biological themes, as described in Ref. 18Hosack D.A. Dennis Jr., G. Sherman B.T. Lane H.C. Lempicki R.A. Genome. Biol. 2003; 4: R70Crossref PubMed Google Scholar. Annotation of our probes was also accessible through MEDIANTE, our own microarray data base (19Le Brigand K. Russell R. Moreilhon C. Rouillard J. Jost B. Amiot F. Magnone V. Bole-Feysot C. Rostagno P. Virolle V. Defamie V. Dessen P. Williams G. Lyons P. Rios G. Mari B. Gulari E. Kastner P. Gidrol X. Freeman T.C. Barbry P. Nucleic Acids Res. 2006; 34: e87Crossref PubMed Scopus (82) Google Scholar), which stores annotations derived from public data bases. Additional statistical analyses, including principal component analysis, were performed with GeneANOVA. The set of data is accessible in the GEO data base under the reference GSE6820. Quantitative Real Time One-step Reverse Transcription-PCR and Western Blot—RNAs were extracted as described above. mRNA expression levels were quantified by real time one-step reverse transcription-PCR using SYBR-Green PCR Master Mix (Applied Biosystems). The results are the average of three separate experiments. The oligonucleotide sequences corresponding to the different genes used for real time quantitative PCR are available upon request. Identification of the Transcriptomal Profile Associated with Keratinocyte Response to in Vitro Injury—This study was performed in order to identify genes from confluent NHK, the expression of which was altered after a mechanical scratching (i.e. an in vitro system used to recapitulate some aspects of skin wound reepithelialization). For that purpose, we used a device that creates calibrated long scratches, allowing a subsequent detection and quantification of overall molecular events (8Turchi L. Chassot A.A. Rezzonico R. Yeow K. Loubat A. Ferrua B. Lenegrate G. Ortonne J.P. Ponzio G. J. Invest. Dermatol. 2002; 119: 56-63Abstract Full Text Full Text PDF PubMed Scopus (55) Google Scholar). We observed that 5-6 h after scratching, cells began to migrate into the denuded space resulting from the tearing of the keratinocyte sheet. This space was totally filled after 25-30 h (not shown). Microarray Data Mining—To identify the genes accompanying the migration and proliferation of NHK in our scratch assay, we used a ∼4200 sequence-verified cDNA microarray, already described in Ref. 11Moreilhon C. Gras D. Hologne C. Bajolet O. Cottrez F. Magnone V. Merten M. Groux H. Puchelle E. Barbry P. Physiol. Genomics. 2005; 20: 244-255Crossref PubMed Scopus (68) Google Scholar and further detailed under “Experimental Procedures.” NHK were scratched and harvested after 1, 3, 6, 15, and 24 h. Total RNAs were isolated and reverse transcribed into fluorescent cDNAs according to standard protocols (see “Experimental Procedures” for microarray procedures). Fig. 1 shows a typical MA plot (20Dudoit S. Speed T.P. Biostatistics. 2000; 1: 1-26Crossref PubMed Google Scholar) of our experiments. It represents the ratio of scratched (S) cells and nonscratched (NS) signals according to the average intensity of the signal 6 h after scratching. Although the expression of most genes was not modified (M = 0), several probes were decidedly up- or down-regulated in a significant and reproducible way (corresponding to the points located over and below M = 0 in Fig. 1). Variation was corroborated by the use of exogenous nonhuman controls (namely DmDNAC and DgNaC). Their presence at defined concentrations in the RNA samples of nonscratched and scratched cells helped to validate each experiment (see “Experimental Procedures”). Since the results presented here correspond to experiments performed at least three times on three or more distinct cell cultures, selection of genes was performed using an analysis of variance as described by Didier et al. (15Didier G. Brezellec P. Remy E. Henaut A. Bioinformatics. 2002; 18: 490-491Crossref PubMed Scopus (87) Google Scholar). Cut-offs were defined as follows. The p value was chosen below 10-4, and statistical variance was above 40%. Significant transcripts were also identified using the SAM software, according to Ref. 16Tusher V.G. Tibshirani R. Chu G. Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 5116-5121Crossref PubMed Scopus (9648) Google Scholar. The cut-off for discriminating differential expression was set at 2 ± 0.75-fold for the up-regulated and down-regulated genes. These statistical analyses allowed us to select 118 up-regulated and 43 down-regulated genes during the healing time course in wounded cells (Fig. 2). An overview of the major gene pattern is summarized in Fig. 2. This analysis clearly revealed the existence of five clusters of transcripts, depending on their distinct time courses of variation. Cluster 1 included 34 transcripts associated with early genes. They were transiently altered between 0 and 3 h after injury. Cluster 2 (intermediary genes) included 19 transcripts, the expression of which was modified between 3 and 6 h after injury. Cluster 3 (late genes) included 56 transcripts that were altered between 6 and 24 h after injury. Cluster 4 (sustained) included 46 transcripts that were rapidly modified after “injury” but remained up-regulated or down-regulated for at least an additional 15 h. Finally, cluster 5 corresponded to three up-regulated transcripts exhibiting a “biphasic” pattern, with a first burst of stimulation between 1 and 3 h after injury, followed by a second burst after 15 h. To draw a clearer picture of the functional relevance of these genes, we used the Ease program (18Hosack D.A. Dennis Jr., G. Sherman B.T. Lane H.C. Lempicki R.A. Genome. Biol. 2003; 4: R70Crossref PubMed Google Scholar) to further classify the selected transcripts into seven functional families according to the biological properties of the proteins that they encode (Fig. 3). These seven families corresponded to the most significant families identified by the EASE algorithm. The F1 family grouped together the regulators of transcription; the F2 family grouped together signal transducers; the F3 family grouped together growth factors and cytokines; the F4 family grouped together transcripts associated with ECM component/remodeling and cell/cell interaction; the F5 family grouped together transcripts associated with cell proliferation and apoptosis; and the F6 family grouped together transcripts associated with metabolism and transport. The F7 family (“miscellaneous genes”) grouped together transcripts, the function of which remains unclear (Fig. 3). It is interesting to note some striking relationships between the five distinct clusters and the seven families. They are developed below. Transcription Factors and Signal Transducers—45% of the transcripts belonging to the “early genes” cluster also belonged to the transcription factor (F1) family. Most of them, such as ATF3, EGR1, JUN, JUNB, FOS, GATA3, ETS1, KLF10, TNFAIP3, ZFP36, and NR4A2, were early transient genes stimulated between 1 and 3 h after scratching. FOS, JUN, JUNB, EGR1, and ATF3 (Table 1) have previously been reported in wound healing studies (21Amendt C. Mann A. Schirmacher P. Blessing M. J. Cell Sci. 2002; 115: 2189-2198Crossref PubMed Google Scholar, 22Yates S. Rayner T.E. Wound Repair Regen. 2002; 10: 5-15Crossref PubMed Scopus (68) Google Scholar, 23Harper E.G. Alvares S.M. Carter W.G. J. Cell Sci. 2005; 118: 3471-3485Crossref PubMed Scopus (51) Google Scholar). Such observations validate our technical approach and suggest that it is relevant to dissect some aspects of wound healing. In addition, we also found the up-regulation of KLF6, KLF10, PRDM1, MXD1, IRF6, ZFP36, GATA3, NR4A2, and ETS1 transcription factors. The detection of the stress-responsive NR4A2 and ATF3 genes (24Lu D. Chen J. Hai T. Biochem. J. 2006; 401: 559-567Crossref Scopus (129) Google Scholar) probably indicates that stress signals have effectively been delivered following the NHK sheet tearing. The stimulation of KLF10 (TIEG; TGFβ-induced early gene) a TGFβ-specific target, indicates that TGFβ is probably activated early after NHK scratching. This induction did not result from an increase of the TGFβ mRNA but most likely resulted from an activation of the latent TGFβ molecules associated with the ECM (25Annes J.P. Munger J.S. Rifkin D.B. J. Cell Sci. 2003; 116: 217-224Crossref PubMed Scopus (1286) Google Scholar). One classical consequence of mechanical stress is to induce cell death. The stimulation of KLF10 is consistent with this model, since KFL10 is currently associated with the proapoptotic TGFβ signals in epithelial cells (26Chalaux E. Lopez-Rovira T. Rosa J.L. Pons G. Boxer L.M. Bartrons R. Ventura F. FEBS Lett. 1999; 457: 478-482Crossref PubMed Scopus (90) Google Scholar). This observation implies that, in our model, TGFβ participates in the apoptotic program that accompanied NHK scratching. Stimulation of MXD1 (Mad1) was also noticed. This transcription factor is an antagonist of c-Myc and mediates, at least in part, the anti-mitotic and/or differentiation-inducing activities of TGFβ and activin in keratinocytes (27Werner S. Beer H.D. Mauch C. Luscher B. Werner S. Oncogene. 2001; 20: 7494-7504Crossref PubMed Scopus (43) Google Scholar). Its stimulation indicates that, besides their capability to induce apoptosis in response to stress signals, TGFβ family members also probably trigger “pure” antimitotic or differentiation signals intervening in proliferation arrest when cells attain confluence. Parallel to proapoptotic TGFβ signals, stimulation of the NF-κB pathway was noticed to occur rapidly after scratching. Due to well known antiapoptotic properties of NF-κB, it is tempting to speculate that NF-κB activation participates in the stress response by promoting the production of survival signals. These observations also indicate that pro- and antiapoptotic signals can be detected simultaneously immediately after injury. The fact that NHK maintain their ability to migrate and to proliferate a long time after scratching seems to indicate that antiapoptotic signals remain prominent during the whole process of scratch closure.TABLE 1List of the genes already reported in wound healingSymbolLocusLinkMolecular function (Ease)ReferenceV-EGF7422Growth factor activity49Werner S. Grose R. Physiol. Rev. 2003; 83: 835-870Crossref PubMed Scopus (2508) Google ScholarV-EGFC7424Growth factor activity49Werner S. Grose R. Physiol. Rev. 2003; 83: 835-870Crossref PubMed Scopus (2508) Google ScholarTGFA7039Growth factor activity49Werner S. Grose R. Physiol. Rev. 2003; 83: 835-870Crossref PubMed Scopus (2508) Google ScholarAREG374Growth factor activity49Werner S. Grose R. Physiol. Rev. 2003; 83: 835-870Crossref PubMed Scopus (2508) Google ScholarEREG2069Growth factor activity49Werner S. Grose R. Physiol. Rev. 2003; 83: 835-870Crossref PubMed Scopus (2508) Google ScholarHBEGF1839Growth factor activity49Werner S. Grose R. Physiol. Rev. 2003; 83: 835-870Crossref PubMed Scopus (2508) Google ScholarATF3467Transcription factor23Harper E.G. Alvares S.M. Carter W.G. J. Cell Sci. 2005; 118: 3471-3485Crossref PubMed Scopus (51) Google ScholarIL83576Chemokine activity49Werner S. Grose R. Physiol. Rev. 2003; 83: 835-870Crossref PubMed Scopus (2508) Google ScholarIL183606Chemokine activity79Kampfer H. Muhl H. Manderscheid M. Kalina U. Kauschat D. Pfeilschifter J. Frank S. Eur. Cytokine Netw. 2000; 11: 626-633PubMed Google ScholarIFNG3458Cytokine activity80Ishida Y. Kondo T. Takayasu T. Iwakura Y. Mukaida N. J. Immunol. 2004; 172: 1848-1855Crossref PubMed Scopus (205) Google ScholarPDGFB5155Growth factor activity49Werner S. Grose R. Physiol. Rev. 2003; 83: 835-870Crossref PubMed Scopus (2508) Google ScholarTGFB17040Regulator of proliferation49Werner S. Grose R. Physiol. Rev. 2003; 83: 835-870Crossref PubMed Scopus (2508) Google ScholarFST10468Inhibitor of TGFβ superfamily81Sulyok S. Wankell M. Alzheimer C. Werner S. Mol. Cell. Endocrino" @default.
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• W2000877345 title "Transcriptional Signature of Epidermal Keratinocytes Subjected to in Vitro Scratch Wounding Reveals Selective Roles for ERK1/2, p38, and Phosphatidylinositol 3-Kinase Signaling Pathways" @default.
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