Mouse polyomavirus large T antigen inhibits cell growth and alters cell and colony morphology in Saccharomyces cerevisiae

AbstractThe gene for mouse polyomavirus large tumor (LT) antigen, a potent oncoprotein, was expressed in Saccharomyces cerevisiae from the inducible GAL1 promoter. Substantial cell growth inhibition as well as colony and cell morphology changes dependent on cyclic adenosine monophosphate (cAMP) were observed. In contrast to cell and colony morphology alterations, the growth inhibition appeared to be transient, thus indicating the existence of an active adaptation of yeast cells to the LT antigen presence

Quantitative differential proteomics of yeast extracellular matrix: there is more to it than meets the eye

Background: Saccharomyces cerevisiae multicellular communities are sustained by a scaffolding extracellular matrix, which provides spatial organization, and nutrient and water availability, and ensures group survival. According to this tissue-like biology, the yeast extracellular matrix (yECM) is analogous to the higher Eukaryotes counterpart for its polysaccharide and proteinaceous nature. Few works focused on yeast biofilms, identifying the flocculin Flo11 and several members of the HSP70 in the extracellular space. Molecular composition of the yECM, is therefore mostly unknown. The homologue of yeast Gup1 protein in high Eukaryotes (HHATL) acts as a regulator of Hedgehog signal secretion, therefore interfering in morphogenesis and cell-cell communication through the ECM, which mediates but is also regulated by this signalling pathway. In yeast, the deletion of GUP1 was associated with a vast number of diverse phenotypes including the cellular differentiation that accompanies biofilm formation. Methods: S. cerevisiae W303-1A wt strain and gup1Δ mutant were used as previously described to generate biofilmlike mats in YPDa from which the yECM proteome was extracted. The proteome from extracellular medium from batch liquid growing cultures was used as control for yECM-only secreted proteins. Proteins were separated by SDS-PAGE and 2DE. Identification was performed by HPLC, LC-MS/MS and MALDI-TOF/TOF. The protein expression comparison between the two strains was done by DIGE, and analysed by DeCyder Extended Data Analysis that included Principal Component Analysis and Hierarchical Cluster Analysis. Results: The proteome of S. cerevisiae yECM from biofilm-like mats was purified and analysed by Nano LC-MS/MS, 2D Difference Gel Electrophoresis (DIGE), and MALDI-TOF/TOF. Two strains were compared, wild type and the mutant defective in GUP1. As controls for the identification of the yECM-only proteins, the proteome from liquid batch cultures was also identified. Proteins were grouped into distinct functional classes, mostly Metabolism, Protein Fate/Remodelling and Cell Rescue and Defence mechanisms, standing out the presence of heat shock chaperones, metalloproteinases, broad signalling cross-talkers and other putative signalling proteins. The data has been deposited to the ProteomeXchange with identifier PXD001133.Conclusions: yECM, as the mammalian counterpart, emerges as highly proteinaceous. As in higher Eukaryotes ECM, numerous proteins that could allow dynamic remodelling, and signalling events to occur in/and via yECM were identified. Importantly, large sets of enzymes encompassing full antagonistic metabolic pathways, suggest that mats develop into two metabolically distinct populations, suggesting that either extensive moonlighting or actual metabolism occurs extracellularly. The gup1Δ showed abnormally loose ECM texture. Accordingly, the correspondent differences in proteome unveiled acetic and citric acid producing enzymes as putative players in structural integrity maintenance.This work was funded by the Marie Curie Initial Training Network GLYCOPHARM (PITN-GA-2012-317297), and by national funds from FCT I.P. through the strategic funding UID/BIA/04050/2013. Fábio Faria-Oliveira was supported by a PhD scholarship (SFRH/BD/45368/2008) from FCT (Fundação para a Ciência e a Tecnologia). We thank David Caceres and Montserrat MartinezGomariz from the Unidad de Proteómica, Universidad Complutense de Madrid – Parque Científico de Madrid, Spain for excellent technical assistance in the successful implementation of all proteomics procedures including peptide identification, and Joana Tulha from the CBMA, Universidade do Minho, Portugal, for helping with the SDS-PAGE experiments, and the tedious and laborious ECM extraction procedures. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium, via the PRIDE partner repository, with the dataset identifier PXD001133. We would like to thank the PRIDE team for all the help and support during the submission process.info:eu-repo/semantics/publishedVersio

Storage and transport of chemical pesticides

W artykule omówione zostały magazynowanie i transport chemicznych środków ochrony roślin.The paper was discussed storage and transport of chemical pesticides

Landmark Finding Algorithms for Indoor Autonomous Mobile Robot Localization

This contribution is oriented to ways of computer vision algorithms for mobile robot localization in internal and external agricultural environment. The main aim of this work was to design, create, verify and evaluate speed and functionality of computer vision localization algorithm. An input colour camera data and depth data were captured by MS® Kinect sensor that was mounted on 6-wheel-drive mobile robot chassis. The design of the localization algorithm was focused to the most significant blobs and points (landmarks) on the colour picture. Actual coordinates of autonomous mobile robot were calculated out from measured distances (depth sensor) and calculated angles (RGB camera) with respect to landmark points. Time measurement script was used to compare the speed of landmark finding algorithm for localization in case of one and more landmarks on picture. The main source code was written in MS Visual studio C# programming language with Microsoft.Kinect.1.7.dll on Windows based PC. Algorithms described in this article were created for a future development of an autonomous agronomical mobile robot localization and control