Two NAD-linked redox shuttles maintain the peroxisomal redox balance in Saccharomyces cerevisiae

In Saccharomyces cerevisiae, peroxisomes are the sole site of fatty acid β-oxidation. During this process, NAD(+) is reduced to NADH. When cells are grown on oleate medium, peroxisomal NADH is reoxidised to NAD(+) by malate dehydrogenase (Mdh3p) and reduction equivalents are transferred to the cytosol by the malate/oxaloacetate shuttle. The ultimate step in lysine biosynthesis, the NAD(+)-dependent dehydrogenation of saccharopine to lysine, is another NAD(+)-dependent reaction performed inside peroxisomes. We have found that in glucose grown cells, both the malate/oxaloacetate shuttle and a glycerol-3-phosphate dehydrogenase 1(Gpd1p)-dependent shuttle are able to maintain the intraperoxisomal redox balance. Single mutants in MDH3 or GPD1 grow on lysine-deficient medium, but an mdh3/gpd1Δ double mutant accumulates saccharopine and displays lysine bradytrophy. Lysine biosynthesis is restored when saccharopine dehydrogenase is mislocalised to the cytosol in mdh3/gpd1Δ cells. We conclude that the availability of intraperoxisomal NAD(+) required for saccharopine dehydrogenase activity can be sustained by both shuttles. The extent to which each of these shuttles contributes to the intraperoxisomal redox balance may depend on the growth medium. We propose that the presence of multiple peroxisomal redox shuttles allows eukaryotic cells to maintain the peroxisomal redox status under different metabolic conditions

Ptc6 is required for proper rapamycin-induced down-regulation of the genes coding for ribosomal and rRNA processing proteins in S. cerevisiae

Ptc6 is one of the seven components (Ptc1-Ptc7) of the protein phosphatase 2C family in the yeast Saccharomyces cerevisiae. In contrast to other type 2C phosphatases, the cellular role of this isoform is poorly understood. We present here a comprehensive characterization of this gene product. Cells lacking Ptc6 are sensitive to zinc ions, and somewhat tolerant to cell-wall damaging agents and to Li+. Ptc6 mutants are sensitive to rapamycin, albeit to lesser extent than ptc1 cells. This phenotype is not rescued by overexpression of PTC1 and mutation of ptc6 does not reproduce the characteristic geneti

Oxidation behavior of graphene-coated copper at intrinsic graphene defects of different origins

The development of ultrathin barrier films is vital to the advanced semiconductor industry. Graphene appears to hold promise as a protective coating; however, the polycrystalline and defective nature of engineered graphene hinders its practical applications. Here, we investigate the oxidation behavior of graphene-coated Cu foils at intrinsic graphene defects of different origins. Macro-scale information regarding the spatial distribution and oxidation resistance of various graphene defects is readily obtained using optical and electron microscopies after the hot-plate annealing. The controlled oxidation experiments reveal that the degree of structural deficiency is strongly dependent on the origins of the structural defects, the crystallographic orientations of the underlying Cu grains, the growth conditions of graphene, and the kinetics of the graphene growth. The obtained experimental and theoretical results show that oxygen radicals, decomposed from water molecules in ambient air, are effectively inverted at Stone-Wales defects into the graphene/Cu interface with the assistance of facilitators

The Yeast Resource Center Public Image Repository: A large database of fluorescence microscopy images

<p>Abstract</p> <p>Background</p> <p>There is increasing interest in the development of computational methods to analyze fluorescent microscopy images and enable automated large-scale analysis of the subcellular localization of proteins. Determining the subcellular localization is an integral part of identifying a protein's function, and the application of bioinformatics to this problem provides a valuable tool for the annotation of proteomes. Training and validating algorithms used in image analysis research typically rely on large sets of image data, and would benefit from a large, well-annotated and highly-available database of images and associated metadata.</p> <p>Description</p> <p>The Yeast Resource Center Public Image Repository (YRC PIR) is a large database of images depicting the subcellular localization and colocalization of proteins. Designed especially for computational biologists who need large numbers of images, the YRC PIR contains 532,182 TIFF images from nearly 85,000 separate experiments and their associated experimental data. All images and associated data are searchable, and the results browsable, through an intuitive web interface. Search results, experiments, individual images or the entire dataset may be downloaded as standards-compliant OME-TIFF data.</p> <p>Conclusions</p> <p>The YRC PIR is a powerful resource for researchers to find, view, and download many images and associated metadata depicting the subcellular localization and colocalization of proteins, or classes of proteins, in a standards-compliant format. The YRC PIR is freely available at <url>http://images.yeastrc.org/</url>.</p

Proteome-wide remodeling of protein location and function by stress.

Protein location and function can change dynamically depending on many factors, including environmental stress, disease state, age, developmental stage, and cell type. Here, we describe an integrative computational framework, called the conditional function predictor (CoFP; http://nbm.ajou.ac.kr/cofp/), for predicting changes in subcellular location and function on a proteome-wide scale. The essence of the CoFP approach is to cross-reference general knowledge about a protein and its known network of physical interactions, which typically pool measurements from diverse environments, against gene expression profiles that have been measured under specific conditions of interest. Using CoFP, we predict condition-specific subcellular locations, biological processes, and molecular functions of the yeast proteome under 18 specified conditions. In addition to highly accurate retrieval of previously known gold standard protein locations and functions, CoFP predicts previously unidentified condition-dependent locations and functions for nearly all yeast proteins. Many of these predictions can be confirmed using high-resolution cellular imaging. We show that, under DNA-damaging conditions, Tsr1, Caf120, Dip5, Skg6, Lte1, and Nnf2 change subcellular location and RNA polymerase I subunit A43, Ino2, and Ids2 show changes in DNA binding. Beyond specific predictions, this work reveals a global landscape of changing protein location and function, highlighting a surprising number of proteins that translocate from the mitochondria to the nucleus or from endoplasmic reticulum to Golgi apparatus under stress. KEYWORDS: DTT and MMS; bioinformatics; dynamic function prediction; protein translocation; systems biolog

Biosynthesis of Vitamin C by Yeast Leads to Increased Stress Resistance

during respiration, or indirectly-caused by other stressing factors. Vitamin C or L-ascorbic acid acts as a scavenger of ROS, thereby potentially protecting cells from harmful oxidative products. While most eukaryotes synthesize ascorbic acid, yeast cells produce erythro-ascorbic acid instead. The actual importance of this antioxidant substance for the yeast is still a subject of scientific debate. is increased, but also the tolerance to low pH and weak organic acids at low pH is increased. cells endogenously producing vitamin C as a cellular model to study the genesis/protection of ROS as well as genotoxicity

Statistical and visual differentiation of subcellular imaging

<p>Abstract</p> <p>Background</p> <p>Automated microscopy technologies have led to a rapid growth in imaging data on a scale comparable to that of the genomic revolution. High throughput screens are now being performed to determine the localisation of all of proteins in a proteome. Closer to the bench, large image sets of proteins in treated and untreated cells are being captured on a daily basis to determine function and interactions. Hence there is a need for new methodologies and protocols to test for difference in subcellular imaging both to remove bias and enable throughput. Here we introduce a novel method of statistical testing, and supporting software, to give a rigorous test for difference in imaging. We also outline the key questions and steps in establishing an analysis pipeline.</p> <p>Results</p> <p>The methodology is tested on a high throughput set of images of 10 subcellular localisations, and it is shown that the localisations may be distinguished to a statistically significant degree with as few as 12 images of each. Further, subtle changes in a protein's distribution between nocodazole treated and control experiments are shown to be detectable. The effect of outlier images is also examined and it is shown that while the significance of the test may be reduced by outliers this may be compensated for by utilising more images. Finally, the test is compared to previous work and shown to be more sensitive in detecting difference. The methodology has been implemented within the iCluster system for visualising and clustering bio-image sets.</p> <p>Conclusion</p> <p>The aim here is to establish a methodology and protocol for testing for difference in subcellular imaging, and to provide tools to do so. While iCluster is applicable to moderate (<1000) size image sets, the statistical test is simple to implement and will readily be adapted to high throughput pipelines to provide more sensitive discrimination of difference.</p

LRRK2 G2019S mutation attenuates microglial motility by inhibiting focal adhesion kinase.

In response to brain injury, microglia rapidly extend processes that isolate lesion sites and protect the brain from further injury. Here we report that microglia carrying a pathogenic mutation in the Parkinson's disease (PD)-associated gene, G2019S-LRRK2 (GS-Tg microglia), show retarded ADP-induced motility and delayed isolation of injury, compared with non-Tg microglia. Conversely, LRRK2 knockdown microglia are highly motile compared with control cells. In our functional assays, LRRK2 binds to focal adhesion kinase (FAK) and phosphorylates its Thr-X-Arg/Lys (TXR/K) motif(s), eventually attenuating FAK activity marked by decreased pY397 phosphorylation (pY397). GS-LRRK2 decreases the levels of pY397 in the brain, microglia and HEK cells. In addition, treatment with an inhibitor of LRRK2 kinase restores pY397 levels, decreased pTXR levels and rescued motility of GS-Tg microglia. These results collectively suggest that G2019S mutation of LRRK2 may contribute to the development of PD by inhibiting microglial response to brain injury

A Bayesian Network Driven Approach to Model the Transcriptional Response to Nitric Oxide in Saccharomyces cerevisiae

The transcriptional response to exogenously supplied nitric oxide in Saccharomyces cerevisiae was modeled using an integrated framework of Bayesian network learning and experimental feedback. A Bayesian network learning algorithm was used to generate network models of transcriptional output, followed by model verification and revision through experimentation. Using this framework, we generated a network model of the yeast transcriptional response to nitric oxide and a panel of other environmental signals. We discovered two environmental triggers, the diauxic shift and glucose repression, that affected the observed transcriptional profile. The computational method predicted the transcriptional control of yeast flavohemoglobin YHB1 by glucose repression, which was subsequently experimentally verified. A freely available software application, ExpressionNet, was developed to derive Bayesian network models from a combination of gene expression profile clusters, genetic information and experimental conditions

A new pairwise kernel for biological network inference with support vector machines

International audienceBACKGROUND: Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.). RESULTS: Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel. This new kernel for pairs can easily be used by most SVM implementations to solve problems of supervised classification and inference of pairwise relationships from heterogeneous data. We demonstrate, using several real biological networks and genomic datasets, that this approach often improves upon the state-of-the-art SVM for indirect inference with another pairwise kernel, and that the combination of both kernels always improves upon each individual kernel. CONCLUSION: The metric learning pairwise kernel is a new formulation to infer pairwise relationships with SVM, which provides state-of-the-art results for the inference of several biological networks from heterogeneous genomic data