WEE1 kinase has been described as a major gate keeper at the G2 cell cycle checkpoint and to be involved in tumour progression in different malignant tumours. Here we analysed the expression levels of WEE1 in a series of melanoma patient samples and melanoma cell lines using immunoblotting, quantitative real-time PCR and immunohistochemistry. WEE1 expression was significantly downregulated in patient samples of metastatic origin as compared with primary melanomas and in melanoma cell lines of high aggressiveness as compared with cell lines of low aggressiveness. Moreover, there was an inverse correlation between the expression of WEE1 and WEE1-targeting microRNA miR-195. Further analyses showed that transfection of melanoma cell lines with miR-195 indeed reduced WEE1 mRNA and protein expression in these cells. Reporter gene analysis confirmed direct targeting of the WEE1 3' untranslated region (3'UTR) by miR-195. Overexpression of miR-195 in SK-Mel-28 melanoma cells was accompanied by WEE1 reduction and significantly reduced stress-induced G2-M cell cycle arrest, which could be restored by stable overexpression of WEE1. Moreover, miR-195 overexpression and WEE1 knockdown, respectively, increased melanoma cell proliferation. miR-195 overexpression also enhanced migration and invasiveness of melanoma cells. Taken together, the present study shows that WEE1 expression in malignant melanoma is directly regulated by miR-195. miR-195-mediated downregulation of WEE1 in metastatic lesions may help to overcome cell cycle arrest under stress conditions in the local tissue microenvironment to allow unrestricted growth of tumour cells.

MicroRNA (miRNA) target hubs are genes that can be simultaneously targeted by a comparatively large number of miRNAs, a class of non-coding RNAs that mediate post-transcriptional gene repression. Although the details of target hub regulation remain poorly understood, recent experiments suggest that pairs of miRNAs can cooperate if their binding sites reside in close proximity. To test this and other hypotheses, we established a novel approach to investigate mechanisms of collective miRNA repression. The approach presented here combines miRNA target prediction and transcription factor prediction with data from the literature and databases to generate a regulatory map for a chosen target hub. We then show how a kinetic model can be derived from the regulatory map. To validate our approach, we present a case study for p21, one of the first experimentally proved miRNA target hubs. Our analysis indicates that distinctive expression patterns for miRNAs, some of which interact cooperatively, fine-tune the features of transient and long-term regulation of target genes. With respect to p21, our model successfully predicts its protein levels for nine different cellular functions. In addition, we find that high abundance of miRNAs, in combination with cooperativity, can enhance noise buffering for the transcription of target hubs.

The present study identified miR-638 as one of the most significantly overexpressed miRNAs in metastatic lesions of melanomas compared with primary melanomas. miR-638 enhanced the tumorigenic properties of melanoma cells in vitro and lung colonization in vivo. mRNA expression profiling identified new candidate genes including TP53INP2 as miR-638 targets, the majority of which are involved in p53 signalling. Overexpression of TP53INP2 severely attenuated proliferative and invasive capacity of melanoma cells which was reversed by miR-638. Depletion of miR-638 stimulated expression of p53 and p53 downstream target genes and induced apoptosis and autophagy. miR-638 promoter analysis identified the miR-638 target transcription factor associated protein 2α (TFAP2A/AP-2α) as a direct negative regulator of miR-638, suggestive for a double-negative regulatory feedback loop. Taken together, miR-638 supports melanoma progression and suppresses p53-mediated apoptosis pathways, autophagy and expression of the transcriptional repressor TFAP2A/AP-2α.

MicroRNAs (miRNAs) are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.

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