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PMID:21185072

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Contents

Citation

Nichols, RJ, Sen, S, Choo, YJ, Beltrao, P, Zietek, M, Chaba, R, Lee, S, Kazmierczak, KM, Lee, KJ, Wong, A, Shales, M, Lovett, S, Winkler, ME, Krogan, NJ, Typas, A and Gross, CA (2011) Phenotypic landscape of a bacterial cell. Cell 144:143-56

Abstract

The explosion of sequence information in bacteria makes developing high-throughput, cost-effective approaches to matching genes with phenotypes imperative. Using E. coli as proof of principle, we show that combining large-scale chemical genomics with quantitative fitness measurements provides a high-quality data set rich in discovery. Probing growth profiles of a mutant library in hundreds of conditions in parallel yielded > 10,000 phenotypes that allowed us to study gene essentiality, discover leads for gene function and drug action, and understand higher-order organization of the bacterial chromosome. We highlight new information derived from the study, including insights into a gene involved in multiple antibiotic resistance and the synergy between a broadly used combinatory antibiotic therapy, trimethoprim and sulfonamides. This data set, publicly available at http://ecoliwiki.net/tools/chemgen/, is a valuable resource for both the microbiological and bioinformatic communities, as it provides high-confidence associations between hundreds of annotated and uncharacterized genes as well as inferences about the mode of action of several poorly understood drugs.

Links

PubMed PMC3060659 Online version:10.1016/j.cell.2010.11.052

Keywords

Escherichia coli/drug effects; Escherichia coli/genetics; Escherichia coli/metabolism; Gene Deletion; Gene Expression Profiling; Genome, Bacterial; Genomics; Mutation

Significance

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Useful Materials and Methods

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Fitness scores (S-scores) were calculated as described in Collins et al [1] and Typas [2]. Fitness is based on colony size doubly normalized for the sizes of all colonies on the plate and the size of the specific strain under other conditions.

Annotations

Gene product Qualifier GO ID GO term name Evidence Code with/from Aspect Notes Status

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See also

References

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  1. Collins, SR et al. (2006) A strategy for extracting and analyzing large-scale quantitative epistatic interaction data. Genome Biol. 7 R63 PubMed EcoliWiki page
  2. Typas, A et al. (2008) High-throughput, quantitative analyses of genetic interactions in E. coli. Nat. Methods 5 781-7 PubMed EcoliWiki page