Uncovering Ubiquitin and Ubiquitin-like Signaling Networks

Microscopic imaging and technolog

Heterologous Expression of Membrane Proteins: Choosing the Appropriate Host

International audienceBACKGROUND: Membrane proteins are the targets of 50% of drugs, although they only represent 1% of total cellular proteins. The first major bottleneck on the route to their functional and structural characterisation is their overexpression; and simply choosing the right system can involve many months of trial and error. This work is intended as a guide to where to start when faced with heterologous expression of a membrane protein. METHODOLOGY/PRINCIPAL FINDINGS: The expression of 20 membrane proteins, both peripheral and integral, in three prokaryotic (E. coli, L. lactis, R. sphaeroides) and three eukaryotic (A. thaliana, N. benthamiana, Sf9 insect cells) hosts was tested. The proteins tested were of various origins (bacteria, plants and mammals), functions (transporters, receptors, enzymes) and topologies (between 0 and 13 transmembrane segments). The Gateway system was used to clone all 20 genes into appropriate vectors for the hosts to be tested. Culture conditions were optimised for each host, and specific strategies were tested, such as the use of Mistic fusions in E. coli. 17 of the 20 proteins were produced at adequate yields for functional and, in some cases, structural studies. We have formulated general recommendations to assist with choosing an appropriate system based on our observations of protein behaviour in the different hosts. CONCLUSIONS/SIGNIFICANCE: Most of the methods presented here can be quite easily implemented in other laboratories. The results highlight certain factors that should be considered when selecting an expression host. The decision aide provided should help both newcomers and old-hands to select the best system for their favourite membrane protein

A nonconserved Ala401 in the yeast Rsp5 ubiquitin ligase is involved in degradation of Gap1 permease and stress-induced abnormal proteins

A toxic l-proline analogue, l-azetidine-2-carboxylic acid (AZC), causes misfolding of the proteins into which it is incorporated competitively with l-proline, thereby inhibiting the growth of the cells. AZC enters budding yeast Saccharomyces cerevisiae cells primarily through the general amino acid permease Gap1, not through the proline-specific permease Put4. We isolated an AZC-hypersensitive mutant that cannot grow even at low concentrations of AZC because of the accumulation of intracellular AZC. By screening through a yeast genomic library, the mutant was found to carry an allele of RSP5 encoding an E3 ubiquitin ligase. A single amino acid change replacing Ala (GCA) at position 401 with Glu (GAA) showed that Ala-401 in the third WW domain (a protein interaction module) is not conserved in the domain. The addition of [Formula: see text] to yeast cells growing on l-proline induced rapid ubiquitination, endocytosis, and vacuolar degradation of the plasma membrane protein Gap1. However, immunoblot and permease assays indicated that Gap1 in the rsp5 mutant remained stable and active on the plasma membrane probably with no ubiquitination, leading to AZC accumulation and hypersensitivity. The rsp5 mutants also showed hypersensitivity to various stresses (toxic amino acid analogues, high temperature in a rich medium, and oxidative treatments) and defects in spore growth. These results suggest that Rsp5 is involved in selective degradation of abnormal proteins and specific proteins for spore growth, in addition to nitrogen-regulated degradation of Gap1. Furthermore, Ala-401 of Rsp5 was considered to have an important role in the ubiquitination of targeted proteins

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions.

Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes

Data from: Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions

Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes