Miconazole induces changes in actin cytoskeleton prior to reactive oxygen species induction in yeast

The antifungal compound miconazole inhibits ergosterol biosynthesis and induces reactive oxygen species (ROS) in susceptible yeast species. To further uncover the mechanism of miconazole antifungal action and tolerance mechanisms, we screened the complete set of haploid Saccharomyces cerevisiae gene deletion mutants for mutants with an altered miconazole sensitivity phenotype. We identified 29 S. cerevisiae genes, which when deleted conferred at least 4-fold hypersensitivity to miconazole. Major functional groups encode proteins involved in tryptophan biosynthesis, membrane trafficking including endocytosis, regulation of actin cytoskeleton, and gene expression. With respect to the antifungal activity of miconazole, we demonstrate an antagonism with tryptophan and a synergy with a yeast endocytosis inhibitor. Because actin dynamics and induction of ROS are linked in yeast, we further focused on miconazole-mediated changes in actin cytoskeleton organization. In this respect, we demonstrate that miconazole induces changes in the actin cytoskeleton, indicative of increased filament stability, prior to ROS induction. These data provide novel mechanistic insights in the mode of action of a ROS-inducing azole.     

Novel fungicidal benzylsulfanyl-phenylguanidines

A series of substituted benzylsulfanyl-phenylamines was synthesized, of which four substituted benzylsulfanyl-phenylguanidines (665, 666, 667 and 684) showed potent fungicidal activity (minimal fungicidal concentration, MFC ? 10 μM for Candida albicans and Candida glabrata). A benzylsulfanyl-phenyl scaffold with an unsubstituted guanidine resulted in less active compounds (MFC = 50-100 μM), whereas substitution with an unsubstituted amine group resulted in compounds without fungicidal activity. Compounds 665, 666, 667 and 684 also showed activity against single C. albicans biofilms and biofilms consisting of C. albicans and Staphylococcus epidermidis (minimal concentration resulting in 50% eradication of the biofilm, BEC50 ? 121 μM for both biofilm setups). Compounds 665 and 666 combined potent fungicidal (MFC = 5 μM) and bactericidal activity (minimal bactericidal concentration, MBC for S. epidermidis ? 4 μM). In an in vivo Caenorhabditis elegans model, compounds 665 and 667 exhibited less toxicity than 666 and 684. Moreover, addition of those compounds to Candida-infected C. elegans cultures resulted in increased survival of Candida-infected worms, demonstrating their in vivo efficacy in a mini-host model.     

Introgression of rol genes from rhizogenic Agrobacterium strains into Escallonia spp.

Plant Cell, Tissue and Organ Culture (PCTOC) - The introgression of rol-genes of rhizogenic Agrobacterium into the plant genome induces changes in plant phenotype and physiology. However, only...     

Integrating functional genomics data using maximum likelihood based simultaneous component analysis

Background  

In contemporary biology, complex biological processes are increasingly studied by collecting and analyzing measurements of the same entities that are collected with different analytical platforms. Such data comprise a number of data blocks that are coupled via a common mode. The goal of collecting this type of data is to discover biological mechanisms that underlie the behavior of the variables in the different data blocks. The simultaneous component analysis (SCA) family of data analysis methods is suited for this task. However, a SCA may be hampered by the data blocks being subjected to different amounts of measurement error, or noise. To unveil the true mechanisms underlying the data, it could be fruitful to take noise heterogeneity into consideration in the data analysis. Maximum likelihood based SCA (MxLSCA-P) was developed for this purpose. In a previous simulation study it outperformed normal SCA-P. This previous study, however, did not mimic in many respects typical functional genomics data sets, such as, data blocks coupled via the experimental mode, more variables than experimental units, and medium to high correlations between variables. Here, we present a new simulation study in which the usefulness of MxLSCA-P compared to ordinary SCA-P is evaluated within a typical functional genomics setting. Subsequently, the performance of the two methods is evaluated by analysis of a real life Escherichia coli metabolomics data set.     

INTRACELLULAR LOCALIZATION OF AN ENDOGENOUS CELLULOSE SYNTHASE OF MICRASTERIAS DENTICULATA (DESMIDIALES,CHLOROPHYTA) BY MEANS OF TRANSIENT GENETIC TRANSFORMATION1

The desmid Micrasterias denticulata Bréb. is useful for the study of streptophyte cell wall biology and morphology. However, no tools to analyze cell biological processes in vivo in this species are available. In the present study, transient gene expression under the control of the chl a/b–binding protein gene of the Closterium peracerosum–strigosum–littorale complex (CpCAB1) promotor was achieved for M. denticulata and illustrated by the intracellular localization of an endogenous cellulose synthase (MdCesA1). A transformation efficiency of 1/5,000 cells was achieved following microparticle bombardment. The free green fluorescent protein (GFP) signal was detected both in the nucleus and in the cytoplasm. The MdCesA1‐GFP fusion protein, on the other hand, occurred at the plasma membrane in particles concentrated at the lobe indentations, the lobe tips, and, to a lesser extent, along the lobe sides. Hence, the multipolar growth mechanism of the cell is reflected. In addition, the margins of cytoplasmic compartments, most likely dictyosomes, were labeled, in accordance with the known secretory pathway of cellulose synthase complexes. Besides intracellular localization studies, the utility of the system for overexpression phenotyping is discussed.     

Design and baseline characteristics of the ParkFit study,a randomized controlled trial evaluating the effectiveness of a multifaceted behavioral program to increase physical activity in Parkinson patients

Background  

Many patients with Parkinson's disease (PD) lead a sedentary lifestyle. Promotion of physical activities may beneficially affect the clinical presentation of PD, and perhaps even modify the course of PD. However, because of physical and cognitive impairments, patients with PD require specific support to increase their level of physical activity.     

Protein markers for insulin-producing beta cells with higher glucose sensitivity

Background and Methodology

Pancreatic beta cells show intercellular differences in their metabolic glucose sensitivity and associated activation of insulin production. To identify protein markers for these variations in functional glucose sensitivity, rat beta cell subpopulations were flow-sorted for their level of glucose-induced NAD(P)H and their proteomes were quantified by label-free data independent alternate scanning LC-MS. Beta cell-selective proteins were also identified through comparison with rat brain and liver tissue and with purified islet alpha cells, after geometrical normalization using 6 stably expressed reference proteins.

Principal Findings

All tissues combined, 943 proteins were reliably quantified. In beta cells, 93 out of 467 quantifiable proteins were uniquely detected in this cell type; several other proteins presented a high molar abundance in beta cells. The proteome of the beta cell subpopulation with high metabolic and biosynthetic responsiveness to 7.5 mM glucose was characterized by (i) an on average 50% higher expression of protein biosynthesis regulators such as 40S and 60S ribosomal constituents, NADPH-dependent protein folding factors and translation elongation factors; (ii) 50% higher levels of enzymes involved in glycolysis and in the cytosolic arm of the malate/aspartate-NADH-shuttle. No differences were noticed in mitochondrial enzymes of the Krebs cycle, beta-oxidation or respiratory chain.

Conclusions

Quantification of subtle variations in the proteome using alternate scanning LC-MS shows that beta cell metabolic glucose responsiveness is mostly associated with higher levels of glycolytic but not of mitochondrial enzymes.     

Ecological Forecasting and the Urbanization of Stream Ecosystems: Challenges for Economists,Hydrologists, Geomorphologists,and Ecologists

The quantity and quality of freshwater resources are now being seriously threatened, partly as a result of extensive worldwide changes in land use, and scientists are often called upon by policy makers and managers to predict the ecological consequences that these alterations will have for stream ecosystems. The effects of the urbanization of stream ecosystems in the United States over the next 20 years are of particular concern. To address this issue, we present a multidisciplinary research agenda designed to improve our forecasting of the effects of land-use change on stream ecosystems. Currently, there are gaps in both our knowledge and the data that make it difficult to link the disparate models used by economists, hydrologists, geomorphologists, and ecologists. We identify a number of points that practitioners in each discipline were not comfortable compromising on—for example, by assuming an average condition for a given variable. We provide five instructive examples of the limitations to our ability to forecast the fate of stream and riverine ecosystems one drawn from each modeling step: (a) Accurate economic methods to forecast land-use changes over long periods (such as 20 years) are not available, especially not at spatially explicit scales; (b) geographic data are not always available at the appropriate resolution and are not always organized in categories that are hydrologically, ecologically, or economically meaningful; (c) the relationship between low flows and land use is sometimes hard to establish in anthropogenically affected catchments; (d) bed mobility, suspended sediment load, and channel form—all of which are important for ecological communities in streams—are difficult to predict; and (e) species distributions in rivers are not well documented, and the data that do exist are not always publicly available or have not been sampled at accurate scales, making it difficult to model ecological responses to specified levels of environmental change. Meeting these challenges will require both interdisciplinary cooperation and a reviewed commitment to intradisciplinary research in the fields of economics, geography, quantitative spatial analysis, hydrology, geomorphology, and ecology. Present address for L.C.T.: Wildlife, Fish, and Conservation Biology Department, University of California–Davis, Davis, California 95616, USA.