Dataset Information

High-throughput identification of protein localization dependency networks.

ABSTRACT: Bacterial cells are highly organized with many protein complexes and DNA loci dynamically positioned to distinct subcellular sites over the course of a cell cycle. Such dynamic protein localization is essential for polar organelle development, establishment of asymmetry, and chromosome replication during the Caulobacter crescentus cell cycle. We used a fluorescence microscopy screen optimized for high-throughput to find strains with anomalous temporal or spatial protein localization patterns in transposon-generated mutant libraries. Automated image acquisition and analysis allowed us to identify genes that affect the localization of two polar cell cycle histidine kinases, PleC and DivJ, and the pole-specific pili protein CpaE, each tagged with a different fluorescent marker in a single strain. Four metrics characterizing the observed localization patterns of each of the three labeled proteins were extracted for hundreds of cell images from each of 854 mapped mutant strains. Using cluster analysis of the resulting set of 12-element vectors for each of these strains, we identified 52 strains with mutations that affected the localization pattern of the three tagged proteins. This information, combined with quantitative localization data from epitasis experiments, also identified all previously known proteins affecting such localization. These studies provide insights into factors affecting the PleC/DivJ localization network and into regulatory links between the localization of the pili assembly protein CpaE and the kinase localization pathway. Our high-throughput screening methodology can be adapted readily to any sequenced bacterial species, opening the potential for databases of localization regulatory networks across species, and investigation of localization network phylogenies.

SUBMITTER: Christen B

PROVIDER: S-EPMC2842071 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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High-throughput identification of protein localization dependency networks.

Christen Beat B Fero Michael J MJ Hillson Nathan J NJ Bowman Grant G Hong Sun-Hae SH Shapiro Lucy L McAdams Harley H HH

Proceedings of the National Academy of Sciences of the United States of America 20100222 10

Bacterial cells are highly organized with many protein complexes and DNA loci dynamically positioned to distinct subcellular sites over the course of a cell cycle. Such dynamic protein localization is essential for polar organelle development, establishment of asymmetry, and chromosome replication during the Caulobacter crescentus cell cycle. We used a fluorescence microscopy screen optimized for high-throughput to find strains with anomalous temporal or spatial protein localization patterns in ...[more]

PMID: 20176934

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Project description:BackgroundIdentification of a bacterial protein's subcellular localization (SCL) is important for genome annotation, function prediction and drug or vaccine target identification. Subcellular fractionation techniques combined with recent proteomics technology permits the identification of large numbers of proteins from distinct bacterial compartments. However, the fractionation of a complex structure like the cell into several subcellular compartments is not a trivial task. Contamination from other compartments may occur, and some proteins may reside in multiple localizations. New computational methods have been reported over the past few years that now permit much more accurate, genome-wide analysis of the SCL of protein sequences deduced from genomes. There is a need to compare such computational methods with laboratory proteomics approaches to identify the most effective current approach for genome-wide localization characterization and annotation.ResultsIn this study, ten subcellular proteome analyses of bacterial compartments were reviewed. PSORTb version 2.0 was used to computationally predict the localization of proteins reported in these publications, and these computational predictions were then compared to the localizations determined by the proteomics study. By using a combined approach, we were able to identify a number of contaminants and proteins with dual localizations, and were able to more accurately identify membrane subproteomes. Our results allowed us to estimate the precision level of laboratory subproteome studies and we show here that, on average, recent high-precision computational methods such as PSORTb now have a lower error rate than laboratory methods.ConclusionWe have performed the first focused comparison of genome-wide proteomic and computational methods for subcellular localization identification, and show that computational methods have now attained a level of precision that is exceeding that of high-throughput laboratory approaches. We note that analysis of all cellular fractions collectively is required to effectively provide localization information from laboratory studies, and we propose an overall approach to genome-wide subcellular localization characterization that capitalizes on the complementary nature of current laboratory and computational methods.

Dataset Information

High-throughput identification of protein localization dependency networks.

Publications

High-throughput identification of protein localization dependency networks.

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