Cholesterol esterification in human monocyte-derived macrophages is inhibited by protein kinase C with dual roles for mitogen activated protein kinases

The possible role of protein kinase C (PKC) and mitogen activated protein (MAP) kinases in the stimulation of cholesterol esterification by acetylated low density lipoprotein (acLDL) in human monocyte-derived macrophages (HMDM) was studied. Cholesterol esterification, as assessed by the rate of incorporation of [3H]-oleate into cholesteryl ester, was markedly higher in HMDM incubated with acLDL as compared to native LDL (nLDL). In the presence of the phorbol ester, phorbol 12-myristate 13-acetate (PMA, 100 nM), however, the rate of incorporation was reduced by about 50% and 85% in incubations with nLDL and acLDL, respectively. Thus, the difference in the rate of cholesteryl esterification induced by the two types of lipoprotein was abolished by PMA, indicating that PKC activation inhibits the process, and this was confirmed by the finding that the PKC inhibitor calphostin C reversed the PMA-induced inhibition of cholesterol esterification. Incubation of HMDM with PMA was found to cause a considerable increase in the activation of p42/44 extracellular signal-regulated MAP kinases (ERK) and p38 MAP kinases, reaching a maximum at 30 min. In the presence of acLDL, the ERK inhibitor PD98059 decreased cholesterol esterification in HMDM by about 35%. In contrast, the p38 inhibitor SB203580 had no effect. However, when PMA was present in addition to SB203580, esterification was reduced to a level lower than that observed with PMA alone. These findings suggest that activation of ERK, but not p38, MAP kinases is involved in the induction of cholesterol esterification by acLDL in HMDM, while p38 MAP kinases may modulate the inhibitory effect of PKC, and thus provide evidence that MAP kinases play a role in the regulation of foam cell formation in human macrophages.     

Modulation of microRNA editing,expression and processing by ADAR2 deaminase in glioblastoma

BackgroundADAR enzymes convert adenosines to inosines within double-stranded RNAs, including microRNA (miRNA) precursors, with important consequences on miRNA retargeting and expression. ADAR2 activity is impaired in glioblastoma and its rescue has anti-tumoral effects. However, how ADAR2 activity may impact the miRNome and the progression of glioblastoma is not known.ResultsBy integrating deep-sequencing and array approaches with bioinformatics analyses and molecular studies, we show that ADAR2 is essential to edit a small number of mature miRNAs and to significantly modulate the expression of about 90 miRNAs in glioblastoma cells. Specifically, the rescue of ADAR2 activity in cancer cells recovers the edited miRNA population lost in glioblastoma cell lines and tissues, and rebalances expression of onco-miRNAs and tumor suppressor miRNAs to the levels observed in normal human brain. We report that the major effect of ADAR2 is to reduce the expression of a large number of miRNAs, most of which act as onco-miRNAs. ADAR2 can edit miR-222/221 and miR-21 precursors and decrease the expression of the corresponding mature onco-miRNAs in vivo and in vitro, with important effects on cell proliferation and migration.ConclusionsOur findings disclose an additional layer of complexity in miRNome regulation and provide information to better understand the impact of ADAR2 editing enzyme in glioblastoma. We propose that ADAR2 is a key factor for maintaining edited-miRNA population and balancing the expression of several essential miRNAs involved in cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0575-z) contains supplementary material, which is available to authorized users.     

Using dendrochronology to trace the impact of the hemiparasite Tristerix chodatianus on Andean Polylepis trees

Plant Ecology - The high Andean forests of the genus Polylepis (Rosaceae) are threatened by extinction due to anthropogenic effects such as timber extraction, burning, and overgrazing. Some species...     

Site specific replacements of a single loop nucleoside with a dibenzyl linker may switch the activity of TBA from anticoagulant to antiproliferative

Many antiproliferative G-quadruplexes (G4s) arise from the folding of GT-rich strands. Among these, the Thrombin Binding Aptamer (TBA), as a rare example, adopts a monomolecular well-defined G4 structure. Nevertheless, the potential anticancer properties of TBA are severely hampered by its anticoagulant action and, consequently, no related studies have appeared so far in the literature. We wish to report here that suitable chemical modifications in the TBA sequence can preserve its antiproliferative over anticoagulant activity. Particularly, we replaced one residue of the TT or TGT loops with a dibenzyl linker to develop seven new quadruplex-forming TBA based sequences (TBA-bs), which were studied for their structural (CD, CD melting, 1D NMR) and biological (fibrinogen, PT and MTT assays) properties. The three-dimensional structures of the TBA-bs modified at T13 (TBA-bs13) or T12 (TBA-bs12), the former endowed with selective antiproliferative activity, and the latter acting as potently as TBA in both coagulation and MTT assays, were further studied by 2D NMR restrained molecular mechanics. The comparative structural analyses indicated that neither the stability, nor the topology of the G4s, but the different localization of the two benzene rings of the linker was responsible for the loss of the antithrombin activity for TBA-bs13.     

Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble

Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand, there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models.     

Simultaneous determination of quinolones for veterinary use by high-performance liquid chromatography with electrochemical detection

A selective method based on high-performance liquid chromatography with electrochemical detection (HPLC-ECD) has been developed to enable simultaneous determination of three fluoroquinolones (FQs), namely danofloxacin (DANO), difloxacin (DIFLO) and sarafloxacin (SARA). The fluoroquinolones are separated on a Novapack C-18 column and detected in a high sensitivity amperometric cell at a potential of +0.8 V. Solid-phase extraction was used for the extraction of the analytes in real samples. The range of concentration examined varied from 10 to 150 ng g^?1 for danofloxacin, from 25 to 100 ng g^?1 for sarafloxacin and from 50 to 315 ng g^?1 for difloxacin, respectively. The method presents detection limits under 10 ng g^?1 and recoveries around 90% for the three analytes have been obtained in the experiments with fortified samples. This HPLC-ECD approach can be useful in the routine analysis of antibacterial residues being less expensive and less complicated than other more powerful tools as hyphenated techniques.