PPARδ Activation Acts Cooperatively with 3-Phosphoinositide-Dependent Protein Kinase-1 to Enhance Mammary Tumorigenesis

Peroxisome proliferator-activated receptorδ (PPARδ) is a transcription factor that is associated with metabolic gene regulation and inflammation. It has been implicated in tumor promotion and in the regulation of 3-phosphoinositide-dependent kinase-1 (PDK1). PDK1 is a key regulator of the AGC protein kinase family, which includes the proto-oncogene AKT/PKB implicated in several malignancies, including breast cancer. To assess the role of PDK1 in mammary tumorigenesis and its interaction with PPARδ, transgenic mice were generated in which PDK1 was expressed in mammary epithelium under the control of the MMTV enhancer/promoter region. Transgene expression increased pT308AKT and pS9GSK3β, but did not alter phosphorylation of mTOR, 4EBP1, ribosomal protein S6 and PKCα. The transgenic mammary gland also expressed higher levels of PPARδ and a gene expression profile resembling wild-type mice maintained on a diet containing the PPARδ agonist, GW501516. Both wild-type and transgenic mice treated with GW501516 exhibited accelerated rates of tumor formation that were more pronounced in transgenic animals. GW501516 treatment was accompanied by a distinct metabolic gene expression and metabolomic signature that was not present in untreated animals. GW501516-treated transgenic mice expressed higher levels of fatty acid and phospholipid metabolites than treated wild-type mice, suggesting the involvement of PDK1 in enhancing PPARδ-driven energy metabolism. These results reveal that PPARδ activation elicits a distinct metabolic and metabolomic profile in tumors that is in part related to PDK1 and AKT signaling

Down-Regulation of miR-101 in Endothelial Cells Promotes Blood Vessel Formation through Reduced Repression of EZH2

Angiogenesis is a balanced process controlled by pro- and anti-angiogenic molecules of which the regulation is not fully understood. Besides classical gene regulation, miRNAs have emerged as post-transcriptional regulators of angiogenesis. Furthermore, epigenetic changes caused by histone-modifying enzymes were shown to modulate angiogenesis as well. However, a possible interplay between miRNAs and histone-modulating enzymes during angiogenesis has not been described. Here we show that VEGF-mediated down-regulation of miR-101 caused pro-angiogenic effects. We found that the pro-angiogenic effects are partly mediated through reduced repression by miR-101 of the histone-methyltransferase EZH2, a member of the Polycomb group family, thereby increasing methylation of histone H3 at lysine 27 and transcriptome alterations. In vitro, the sprouting and migratory properties of primary endothelial cell cultures were reduced by inhibiting EZH2 through up-regulation of miR-101, siRNA-mediated knockdown of EZH2, or treatment with 3-Deazaneplanocin-A (DZNep), a small molecule inhibitor of EZH2 methyltransferase activity. In addition, we found that systemic DZNep administration reduced the number of blood vessels in a subcutaneous glioblastoma mouse model, without showing adverse toxicities. Altogether, by identifying a pro-angiogenic VEGF/miR-101/EZH2 axis in endothelial cells we provide evidence for a functional link between growth factor-mediated signaling, post-transcriptional silencing, and histone-methylation in the angiogenesis process. Inhibition of EZH2 may prove therapeutic in diseases in which aberrant vascularization plays a role

Human migration: the big data perspective

How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants