MicroRNAs in melanoma development and resistance to target therapy

microRNAs constitute a complex class of pleiotropic post-transcriptional regulators of gene expression involved in the control of several physiologic and pathologic processes. Their mechanism of action is primarily based on the imperfect matching of a seed region located at the 5' end of a 21-23 nt sequence with a partially complementary sequence located in the 3' untranslated region of target mRNAs. This leads to inhibition of mRNA translation and eventually to its degradation. Individual miRNAs are capable of binding to several mRNAs and several miRNAs are capable of influencing the function of the same mRNAs. In recent years networks of miRNAs are emerging as capable of controlling key signaling pathways responsible for the growth and propagation of cancer cells. Furthermore several examples have been provided which highlight the involvement of miRNAs in the development of resistance to targeted drug therapies. In this review we provide an updated overview of the role of miRNAs in the development of melanoma and the identification of the main downstream pathways controlled by these miRNAs. Furthermore we discuss a group of miRNAs capable to influence through their respective up- or down-modulation the development of resistance to BRAF and MEK inhibitors

Mathematical and computational modelling of post-transcriptional gene relation by micro-RNA

Mathematical models and computational simulations have proved valuable in many areas of cell biology, including gene regulatory networks. When properly calibrated against experimental data, kinetic models can be used to describe how the concentrations of key species evolve over time. A reliable model allows ‘what if’ scenarios to be investigated quantitatively in silico, and also provides a means to compare competing hypotheses about the underlying biological mechanisms at work. Moreover, models at different scales of resolution can be merged into a bigger picture ‘systems’ level description. In the case where gene regulation is post-transcriptionally affected by microRNAs, biological understanding and experimental techniques have only recently matured to the extent that we can postulate and test kinetic models. In this chapter, we summarize some recent work that takes the first steps towards realistic modelling, focusing on the contributions of the authors. Using a deterministic ordinary differential equation framework, we derive models from first principles and test them for consistency with recent experimental data, including microarray and mass spectrometry measurements. We first consider typical mis-expression experiments, where the microRNA level is instantaneously boosted or depleted and thereafter remains at a fixed level. We then move on to a more general setting where the microRNA is simply treated as another species in the reaction network, with microRNA-mRNA binding forming the basis for the post-transcriptional repression. We include some speculative comments about the potential for kinetic modelling to contribute to the more widespread sequence and network based approaches in the qualitative investigation of microRNA based gene regulation. We also consider what new combinations of experimental data will be needed in order to make sense of the increased systems-level complexity introduced by microRNAs

Induction of microRNAs, mir-155, mir-222, mir-424 and mir-503, promotes monocytic differentiation through combinatorial regulation

Acute myeloid leukemia (AML) involves a block in terminal differentiation of the myeloid lineage and uncontrolled proliferation of a progenitor state. Using phorbol myristate acetate (PMA), it is possible to overcome this block in THP-1 cells (an M5-AML containing the MLL-MLLT3 fusion), resulting in differentiation to an adherent monocytic phenotype. As part of FANTOM4, we used microarrays to identify 23 microRNAs that are regulated by PMA. We identify four PMA-induced micro- RNAs (mir-155, mir-222, mir-424 and mir-503) that when overexpressed cause cell-cycle arrest and partial differentiation and when used in combination induce additional changes not seen by any individual microRNA. We further characterize these prodifferentiative microRNAs and show that mir-155 and mir-222 induce G2 arrest and apoptosis, respectively. We find mir-424 and mir-503 are derived from a polycistronic precursor mir-424-503 that is under repression by the MLL-MLLT3 leukemogenic fusion. Both of these microRNAs directly target cell-cycle regulators and induce G1 cell-cycle arrest when overexpressed in THP-1. We also find that the pro-differentiative mir-424 and mir-503 downregulate the anti-differentiative mir-9 by targeting a site in its primary transcript. Our study highlights the combinatorial effects of multiple microRNAs within cellular systems.Comment: 45 pages 5 figure

MicroRNA and transcription factor co-regulatory networks and subtype classification of seminoma and non-seminoma in testicular germ cell tumors

Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer. FFLs may include two regulators such as transcription factors (TFs) and microRNAs (miRNAs). In this study, we extensively investigated TF and miRNA regulation pairs, their FFLs, and TF-miRNA mediated regulatory networks in two major types of testicular germ cell tumors (TGCT): seminoma (SE) and non-seminoma (NSE). Specifically, we identified differentially expressed mRNA genes and miRNAs in 103 tumors using the transcriptomic data from The Cancer Genome Atlas. Next, we determined significantly correlated TF-gene/miRNA and miRNA-gene/TF pairs with regulation direction. Subsequently, we determined 288 and 664 dysregulated TF-miRNA-gene FFLs in SE and NSE, respectively. By constructing dysregulated FFL networks, we found that many hub nodes (12 out of 30 for SE and 8 out of 32 for NSE) in the top ranked FFLs could predict subtype-classification (Random Forest classifier, average accuracy ≥90%). These hub molecules were validated by an independent dataset. Our network analysis pinpointed several SE-specific dysregulated miRNAs (miR-200c-3p, miR-25-3p, and miR-302a-3p) and genes (EPHA2, JUN, KLF4, PLXDC2, RND3, SPI1, and TIMP3) and NSE-specific dysregulated miRNAs (miR-367-3p, miR-519d-3p, and miR-96-5p) and genes (NR2F1 and NR2F2). This study is the first systematic investigation of TF and miRNA regulation and their co-regulation in two major TGCT subtypes

In silico Analysis of Combinatorial microRNA Activity Reveals Target Genes and Pathways Associated with Breast Cancer Metastasis

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Inter- and intra-combinatorial regulation by transcription factors and microRNAs

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are a novel class of non-coding small RNAs. In mammalian cells, miRNAs repress the translation of messenger RNAs (mRNAs) or degrade mRNAs. miRNAs play important roles in development and differentiation, and they are also implicated in aging, and oncogenesis. Predictions of targets of miRNAs suggest that they may regulate more than one-third of all genes. The overall functions of mammalian miRNAs remain unclear. Combinatorial regulation by transcription factors alone or miRNAs alone offers a wide range of regulatory programs. However, joining transcriptional and post-transcriptional regulatory mechanisms enables higher complexity regulatory programs that in turn could give cells evolutionary advantages. Investigating coordinated regulation of genes by miRNAs and transcription factors (TFs) from a statistical standpoint is a first step that may elucidate some of their roles in various biological processes.</p> <p>Results</p> <p>Here, we studied the nature and scope of coordination among regulators from the transcriptional and miRNA regulatory layers in the human genome. Our findings are based on genome wide statistical assessment of regulatory associations ("interactions") among the sets of predicted targets of miRNAs and sets of putative targets of transcription factors. We found that combinatorial regulation by transcription factor pairs and miRNA pairs is much more abundant than the combinatorial regulation by TF-miRNA pairs. In addition, many of the strongly interacting TF-miRNA pairs involve a subset of master TF regulators that co-regulate genes in coordination with almost any miRNA. Application of standard measures for evaluating the degree of interaction between pairs of regulators show that strongly interacting TF-miRNA, TF-TF or miRNA-miRNA pairs tend to include TFs or miRNAs that regulate very large numbers of genes. To correct for this potential bias we introduced an additional Bayesian measure that incorporates not only how significant an interaction is but also how strong it is. Putative pairs of regulators selected by this procedure are more likely to have biological coordination. Importantly, we found that the probability of a TF-miRNA pair forming feed forward loops with its common target genes (where the miRNA simultaneously suppresses the TF and many of its targets) is increased for strongly interacting TF-miRNA pairs.</p> <p>Conclusion</p> <p>Genes are more likely to be co-regulated by pairs of TFs or pairs of miRNAs than by pairs of TF-miRNA, perhaps due to higher probability of evolutionary duplication events of shorter DNA sequences. Nevertheless, many gene sets are reciprocally regulated by strongly interacting pairs of TF-miRNA, which suggests an effective mechanism to suppress functionally related proteins. Moreover, the particular type of feed forward loop (with two opposing modes where the TF activates its target genes or the miRNA simultaneously suppresses this TF and the TF-miRNA joint target genes) is more prevalent among strongly interacting TF-miRNA pairs. This may be attributed to a process that prevents waste of cellular resources or a mechanism to accelerate mRNA degradation.</p

miR-200c sensitizes breast cancer cells to doxorubicin treatment by decreasing TrkB and Bmi1 expression.

Acquired resistance to classical chemotherapeutics is a major obstacle in cancer treatment. Doxorubicin is frequently used in breast cancer therapy either as single-agent or in combination with other drugs like docetaxel and cyclophosphamide. All these chemotherapies have in common that they are administered sequentially and often result in chemoresistance. Here, we mimicked this pulse therapy of breast cancer patients in an in vitro cell culture model, where the epithelial breast cancer cell line BT474 was sequentially treated with doxorubicin for several treatment cycles. In consequence, we obtained chemoresistant cells displaying a mesenchymal-like phenotype with decreased levels of miR-200c. To investigate the involvement of miR-200c in resistance formation, we inhibited and overexpressed miR-200c in different cell lines. Thereby, the cells were rendered more resistant or susceptible to doxorubicin treatment. Moreover, the receptor tyrosine kinase TrkB and the transcriptional repressor Bmi1 were identified as miR-200c targets mediating the drug resistance. Hence, we provide a mechanism of acquired resistance to doxorubicin that is caused by the loss of miR-200c. Along with this, our study demonstrates the complex network of microRNA mediated chemoresistance highlighting the challenges in cancer therapy and the importance of novel microRNA-modulating anticancer agents