Identification of microRNA-mRNA functional interactions in UVB-induced senescence of human diploid fibroblasts

Abstract

Background: Cellular senescence can be induced by a variety of extrinsic stimuli, and sustained exposure to sunlight is a key factor in photoaging of the skin. Accordingly, irradiation of skin fibroblasts by UVB light triggers cellular senescence, which is thought to contribute to extrinsic skin aging, although molecular mechanisms are incompletely understood. Here, we addressed molecular mechanisms underlying UVB induced senescence of human diploid fibroblasts.

Results: We observed a parallel activation of the p53/p21(WAF1) and p16(INK4a)/pRb pathways. Using genome-wide transcriptome analysis, we identified a transcriptional signature of UVB-induced senescence that was conserved in three independent strains of human diploid fibroblasts (HDF) from skin. In parallel, a comprehensive screen for microRNAs regulated during UVB-induced senescence was performed which identified five microRNAs that are significantly regulated during the process. Bioinformatic analysis of miRNA-mRNA networks was performed to identify new functional mRNA targets with high confidence for miR-15a, miR-20a, miR-20b, miR-93, and miR-101. Already known targets of these miRNAs were identified in each case, validating the approach. Several new targets were identified for all of these miRNAs, with the potential to provide new insight in the process of UVB-induced senescence at a genome-wide level. Subsequent analysis was focused on miR-101 and its putative target gene Ezh2. We confirmed that Ezh2 is regulated by miR-101 in human fibroblasts, and found that both overexpression of miR-101 and downregulation of Ezh2 independently induce senescence in the absence of UVB irradiation. However, the downregulation of miR-101 was not sufficient to block the phenotype of UVB-induced senescence, suggesting that other UVB-induced processes induce the senescence response in a pathway redundant with upregulation of miR-101.

Conclusion: We performed a comprehensive screen for UVB-regulated microRNAs in human diploid fibroblasts, and identified a network of miRNA-mRNA interactions mediating UVB-induced senescence. In addition, miR-101 and Ezh2 were identified as key players in UVB-induced senescence of HDF.

Figure 1

Transcriptome analysis of UVB-induced senescence. A. Ingenuity pathway analysis. Activated pathways were determined by Ingenuity System Pathway Analysis software (http://www.ingenuity.com/) by Core Analysis. Shown are eight significantly activated pathways (threshold p-value ≤ 0.05). B. In silico analysis of mRNA expression change in response to UVB irradiation. (left) The differential gene expression profile from total RNA was examined by Affymetrix GeneChip analysis (cutoff of > 1.5 fold regulation) 1, 7, and 9 days between control and UVB irradiated of HDF samples. The expression values were sorted by level of Ezh2 expression (rows). (right) Heatmap of selected 67 genes were determined by RT-PCR and sorted by level of Ezh2 expression (rows), red representing overexpression and green representing underexpression of the transcript.

Figure 2

Validated miRNA expression levels. miRNA was isolated from UVB treated and control cells. miRNA expression levels for miR-20a, miR-20b, miR-15a, and miR-93 were determined by Locked nucleic acid (LNA)-miRNA microarray. Experiments were performed in triplicates. *p < 0.01; **p < 0.001.

Figure 3

microRNA expression level. The expression levels of 806 miRNAs were determined at day 1, 4, 7 and 9 by LNA microarray. Displayed are five selected miRNAs with the corresponding mRNA targets, identified by DIANA LAB (http://diana.cslab.ece.ntua.gr/tarbase/) and miRecords (http://mirecords.biolead.org/). In red are miRNAs and target mRNAs that are upregulated, downregulated genes are colored in blue and not regulated genes are in black.

Figure 4

Correlation network of miR-20a/b and their high confidence target genes. We used 10 prediction tools to obtain, based on public data, candidate miRNA-mRNA target interactions and we identified high confidence targets by mRNA and miRNA expression. In positive cases, miRNA expression should show a negative correlation with the respective target gene mRNA level. We calculated Pearson correlation coefficients between miRNAs and their targets. Results of the analysis are presented here for miR-20a (A), and miR-20b (B). The color and shape of nodes are based on different node attributes available for the analyzed dataset. The red triangles, purple circles and orange diamonds in the network are indicating miRNAs, target genes, and transcription factors, respectively. Edges represent correlation between miRNAs and mRNAs, the color of the edges designate the type of interaction. Red is for positive and green is for negative correlation. Protein was isolated from UVB irradiated and control cells at the indicated time points. Protein levels were analyzed by standard Western blot for Cyclin D1 (C). Experiments were performed in triplicates, one representative experiment is shown.

Figure 5

Correlation networks of miR-93/miR-15 and their high confidence target genes. We used 10 prediction tools to obtain, based on public data, candidate miRNA-mRNA target interactions and we identified high confidence targets by mRNA and miRNA expression. We calculated Pearson correlation coefficients between miRNAs and their targets. Results of the analysis are presented here for miR-93 (A), and miR-15 (B). The color and shape of nodes are based on different node attributes available for the analyzed dataset. The red triangles, purple circles and orange diamonds in the network are indicating miRNAs, target genes, and transcription factors, respectively. Edges represent correlation between miRNAs and mRNAs, the color of the edges designate the type of interaction. Red is for positive and green is for negative correlation.

Figure 6

Correlation network of miR-101 and its high confidence target genes. (A) We used 10 prediction tools to obtain, based on public data, candidate miRNA-mRNA target interactions and we identified high confidence targets by mRNA and miRNA expression. We calculated Pearson correlation coefficients between miRNAs and their targets. Results of the analysis are presented here for miR-101. The color and shape of nodes are based on different node attributes available for the analyzed dataset. The red triangles, purple circles and orange diamonds in the network are indicating miRNAs, target genes, and transcription factors, respectively. Edges represent correlation between miRNAs and mRNAs, the color of the edges designate the type of interaction. Red is for positive and green is for negative correlation. (B) Protein samples for the indicated time points were collected as described (left panel). Protein levels were determined for Ezh2 by standard Western blot analysis. Experiments were performed in triplicates, shown here is a representative result. miR-101 levels were determined by real-time qPCR as described (right panel). Data represents the mean ± SE for three independent experiments. Co: untreated controls. *p < 0.01; **p < 0.001.

Figure 7

miR-101 overexpression induces premature senescence in HDFs. Cells were reverse transfected as described with miR-101 precursors for overexpression, negative control or with the transfection reagent (siPORT™ NeoFX™) at day 0, 3, 5 and 7. At day 9 RNA and protein were isolated. Expression levels of (A) miR-101 and (B) Ezh2 mRNA were determined by real-time qPCR. Data represents the mean ± SE for three independent experiments. (C) Standard Western blot analysis was performed with monoclonal mouse anti-Ezh2 antibody. Left panel represents densitometric data calculated out of three independent experiments (± SD). (D) Growth curve analysis of miR-101 overexpressing HDFs. cPDLs were calculated. Data represents the mean ± SD for three independent experiments. (E) To determine the senescence status of miR-101 overexpressing and control HDFs, cells were stained for SA-β-gal at day 9. Bars represent the relative amount of SA-β-gal positive cells (± SD). OE: overexpression; cPDL: cumulative population doublings. *p < 0.01; **p < 0.001.

Figure 8

Depletion of miR-101 fails to prevent UVB-induced senescence. Cells were irradiated with UVB twice a day for 4 days. After the last exposure the cells were reverse transfected as described with miR-101 power inhibitors for knockdown, negative control or not treated. Reverse transfection was repeated at day 7. At day 9 RNA and protein were isolated. A. Control cells. Expression levels of miR-101 and Ezh2 mRNA of non-irradiated cells after miR-101 knockdown were determined by real-time qPCR. Bars indicate the mean ± SE of three independent experiments. B. UVB-treated cells miR-101 and Ezh2 expression levels from UVB treated cells after miR-101 knockdown were determined by real-time qPCR. Bars indicate the mean ± SE of three independent experiments. C. Growth curve analysis of miR-101 knockdown cells. cPDLs were calculated as described. Data represent the mean ± SD of three independent experiments. cPDL: cumulative population doublings KD: knockdown. *p < 0.01; **p < 0.001.