The BioGRID Interaction Database: 2011 update

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein interaction data from model organisms and humans (http://www.thebiogrid.org). BioGRID currently holds 347 966 interactions (170 162 genetic, 177 804 protein) curated from both high-throughput data sets and individual focused studies, as derived from over 23 000 publications in the primary literature. Complete coverage of the entire literature is maintained for budding yeast (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe) and thale cress (Arabidopsis thaliana), and efforts to expand curation across multiple metazoan species are underway. The BioGRID houses 48 831 human protein interactions that have been curated from 10 247 publications. Current curation drives are focused on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health. The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. An automated Interaction Management System (IMS) is used to prioritize, coordinate and track curation across international sites and projects. BioGRID provides interaction data to several model organism databases, resources such as Entrez-Gene and other interaction meta-databases. The entire BioGRID 3.0 data collection may be downloaded in multiple file formats, including PSI MI XML. Source code for BioGRID 3.0 is freely available without any restrictions

Membrane-type 1 matrix metalloproteinase-mediated progelatinase A activation in non-tumorigenic and tumorigenic humaneratinocytes

Elevated expression of type IV collagenases (MMP-2 and MMP-9) has been strongly correlated with tumour progression and metastasis in various tumours. Here, we analysed expression and activation of these MMPs in non-tumourigenic HaCaT cells and the malignant HaCaT variant II-4 rt. In monolayer cultures, both cell types secreted latent MMP-2 (proMMP-2) in comparable amounts, while MMP-9 production was clearly higher in II-4 rt cells. Upon contact with fibrillar collagen type I the malignant II-4 rt cells, but not the HaCaT cells, gained the capability to activate proMMP-2. This process is shown to be membrane-associated and mediated by MT1-MMP. Surprisingly, all membrane preparations from either HaCaT cells or II-4 rt cells grown as monolayers, as well as within collagen gels, contained considerable amounts of active MT1-MMP. However, within collagen gels HaCaT cells showed significantly higher TIMP-2 levels compared to II-4 rt cells. This indicates that TIMP-2 might play a central role for MT1-MMP-mediated gelatinolytic activity. Indeed, collagen type I-induced MT1-MMP-mediated proMMP-2 activation by II-4 rt membranes could be completely abolished by an excess of TIMP-2. In conclusion, our data suggest that MT1-MMP-mediated proMMP-2 activation might be associated with malignant progression of epidermal tumour cells. © 2000 Cancer Research Campaig

Genetic study of Ascochyta blight resistance in chickpea and lentil

Non-Peer ReviewedAscochyta blight is responsible for severe crop losses in most chickpea and lentil production areas around the world. The research was conducted to study the genetic basis for Ascochyta blight resistance in chickpea and lentil by means of QTL analysis, and PCR-based approaches to identify resistance gene analogues (RGA) sequences in the lentil genome. An AFLP and three SSR markers were linked to the gene(s) for Ascochyta resistance in a chickpea population derived from a cross between CDC Chico and CDC Marengo. Two QTL that explained 36 % and 29 % of the disease reaction variability were identified in a lentil RI population derived from a cross between ILL5588 and L692-16-1. These markers were converted into SCAR markers to simplify their use for marker-assisted selection

Revisiting strategies for breeding anthracnose resistance in lentil: the case with wild species

Non-Peer ReviewedBreeders at the Crop Development Centre (CDC) have up to now only used germplasm resources available in the cultivated lentil to develop new varieties with resistance to diseases. Based on recent studies, the available cultivated germplasm does not offer sufficient genetic variation for resistance to anthracnose and ascochyta diseases. Lentil crop is attacked by two major diseases (anthracnose and ascochyta) that can cause 100% loss in the worst scenarios. Since anthracnose is only a major lentil disease in North America, no work has been done to improve resistance to this disease elsewhere. Wild species of many crops are known to carry many disease resistance genes lacking in the cultivated crop. We began the search for anthracnose resistance in the six wild species of lentil (world collection), of which two can be easily crossed with the cultivated type. Two strains of anthracnose (race 1 and race 2) with varying degrees of virulence were reported. The 2002 field data suggested that some of the Lens ervoides and Lens lamottei accessions exhibited no lesions at all when exposed to the combination of the two anthracnose strains. The cultivated types that show resistance to the less virulent strain were severely affected by anthracnose. In the greenhouse study the wild species were inoculated with the two strains separately and results indicate that no accession is immune to the more virulent type. However, some of the L. ervoides and L. lamottei accessions had good resistance compared to their cultivated counterparts. As a long term strategy, the lentil breeding program at CDC, University of Saskatchewan has a goal of fully utilizing the available resistance sources. However, these two species cannot be easily crossed with the cultivated types using the conventional/manual crossing techniques. A tissue culture procedure involving embryo rescue is used to facilitate crossing. We have been able to successfully rescue some embryos from crosses with Lens ervoides. The hybrid plants produce some fertile seeds which will be evaluated for resistance to both anthracnose and ascochyta. The selected resistant lines will then be backcrossed to the adopted backgrounds in order to deploy resistance genes

Retraction: Fatty acid synthase is a novel therapeutic target in multiple myeloma

This study investigated the biological significance of the inhibition of fatty acid synthase (FAS) in multiple myeloma (MM) using the small molecule inhibitor Cerulenin. Cerulenin triggered growth inhibition in both MM cell lines and MM patient cells, and overcame the survival and growth advantages conferred by interleukin-6, insulin-like growth factor-1, and bone marrow stromal cells. It induced apoptosis in MM cell lines with only modest activation of caspase -8, -9, -3 and PARP; moreover, the pan-caspase inhibitor Z-VAD-FMK did not inhibit Cerulenin-induced apoptosis and cell death. In addition, treatment of MM cells with Cerulenin primarily up-regulated apoptosis-inducing factor/endonuclease G, mediators of caspase-independent apoptosis. Importantly, Cerulenin induced endoplasmic reticulum stress response via up-regulation of the Grp78/IRE1α/JNK pathway. Although the C-Jun-NH2-terminal kinase (JNK) inhibitor SP600215 blocked Cerulenin-induced cytotoxicity, it did not inhibit apoptosis and caspase cleavage. Furthermore, Cerulenin showed synergistic cytotoxic effects with various agents including Bortezomib, Melphalan and Doxorubicin. Our results therefore indicate that inhibition of FAS by Cerulenin primarily triggered caspase-independent apoptosis and JNK-dependent cytotoxicity in MM cells. This report demonstrated that inhibition of FAS has anti-tumour activity against MM cells, suggesting that it represents a novel therapeutic target in MM

MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe

Playing hide and seek with poorly tasting paediatric medicines: do not forget the excipients

The development of paediatric medicines can be challenging since this is a diverse patient population with specific needs. For example, the toxicity of excipients may differ in children compared to adults and children have different taste preferences. Acceptable palatability of oral paediatric medicinal products is of great importance to facilitate patient adherence. This has been recognised by regulatory authorities and so is becoming a key aspect of paediatric pharmaceutical development studies. Many active pharmaceutical ingredients (APIs) have aversive taste characteristics and so it is necessary to utilise taste masking techniques to improve the palatability of paediatric oral formulations. The aim of this review is to provide an overview of different approaches to taste masking APIs in paediatric oral dosage forms, with a focus on the tolerability of excipients used. In addition, where possible, the provision of examples of some marketed products is made

High-throughput, quantitative analyses of genetic interactions in E. coli.

Large-scale genetic interaction studies provide the basis for defining gene function and pathway architecture. Recent advances in the ability to generate double mutants en masse in Saccharomyces cerevisiae have dramatically accelerated the acquisition of genetic interaction information and the biological inferences that follow. Here we describe a method based on F factor-driven conjugation, which allows for high-throughput generation of double mutants in Escherichia coli. This method, termed genetic interaction analysis technology for E. coli (GIANT-coli), permits us to systematically generate and array double-mutant cells on solid media in high-density arrays. We show that colony size provides a robust and quantitative output of cellular fitness and that GIANT-coli can recapitulate known synthetic interactions and identify previously unidentified negative (synthetic sickness or lethality) and positive (suppressive or epistatic) relationships. Finally, we describe a complementary strategy for genome-wide suppressor-mutant identification. Together, these methods permit rapid, large-scale genetic interaction studies in E. coli

An ontology-based search engine for protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database.</p> <p>Results</p> <p>We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions.</p> <p>Conclusion</p> <p>Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology.</p