Joshua N. Levinson

Bio: Joshua N. Levinson is an academic researcher from McGill University. The author has contributed to research in topics: Endoplasmic reticulum & Saccharomyces cerevisiae. The author has an hindex of 3, co-authored 3 publications receiving 2009 citations.

Global Mapping of the Yeast Genetic Interaction Network

Abstract: A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

Saccharomyces cerevisiae Big1p, a putative endoplasmic reticulum membrane protein required for normal levels of cell wall beta-1,6-glucan.

Abstract: Deletion of Saccharomyces cerevisiae BIG1 causes an approximately 95% reduction in cell wall beta-1,6-glucan, an essential polymer involved in the cell wall attachment of many surface mannoproteins. The big1 deletion mutant grows very slowly, but growth can be enhanced if cells are given osmotic support. We have begun a cell biological and genetic analysis of its product. We demonstrate, using a Big1p-GFP fusion construct, that Big1p is an N-glycosylated integral membrane protein with a Type I topology that is located in the endoplasmic reticulum (ER). Some phenotypes of a big1Delta mutant resemble those of strains disrupted for KRE5, which encodes another ER protein affecting beta-l,6-glucan levels to a similar extent. In a big1Deltakre5Delta double mutant, both the growth and alkali-soluble beta-l,6-glucan levels were reduced as compared to either single mutant. Thus, while Big1p and Kre5p may have similar effects on beta-l,6-glucan synthesis, these effects are at least partially distinct. Residual beta-l,6-glucan levels in the big1Deltakre5Delta double mutant indicate that these gene products are unlikely to be beta-l,6-glucan synthase subunits, but rather may play some ancillary roles in beta-l,6-glucan synthase assembly or function, or in modifying proteins for attachment of beta-l,6-glucan.

Functional, comparative and cell biological analysis of Saccharomyces cerevisiae Kre5p.

Abstract: Saccharomyces cerevisiae kre5Δ mutants lack β-1,6-glucan, a polymer required for proper cell wall assembly and architecture. A functional and cell biological analysis of Kre5p was conducted to further elucidate the role of this diverged protein glucosyltransferase-like protein in β-1,6-glucan synthesis. Kre5p was found to be a primarily soluble N-glycoprotein of ∼200 kDa, that localizes to the endoplasmic reticulum. The terminal phenotype of Kre5p-deficient cells was observed, and revealed a severe cell wall morphological defect. KRE6, encoding a glucanase-like protein, was identified as a multicopy suppressor of a temperature-sensitive kre5 allele, suggesting that these proteins may participate in a common β-1,6-biosynthetic pathway. An analysis of truncated versions of Kre5p indicated that all major regions of the protein are required for viability. Finally, Candida albicans KRE5 was shown to partially restore growth to S. cerevisiae kre5Δ cells, suggesting that these proteins are functionally related. Copyright © 2002 John Wiley & Sons, Ltd.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

BioGRID: a general repository for interaction datasets

Abstract: Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at http://www.thebiogrid.org. BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. Over 30 000 interactions have recently been added from 5778 sources through exhaustive curation of the Saccharomyces cerevisiae primary literature. An internally hyper-linked web interface allows for rapid search and retrieval of interaction data. Full or user-defined datasets are freely downloadable as tab-delimited text files and PSI-MI XML. Pre-computed graphical layouts of interactions are available in a variety of file formats. User-customized graphs with embedded protein, gene and interaction attributes can be constructed with a visualization system called Osprey that is dynamically linked to the BioGRID.

Integration of biological networks and gene expression data using Cytoscape

Abstract: Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.

The genetic landscape of a cell.

Abstract: A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.