Difference between revisions of "PMID:21185072"

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'''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
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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.
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[http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=21185072 PubMed]
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Online version:[http://dx.doi.org/10.1016/j.cell.2010.11.052 10.1016/j.cell.2010.11.052]
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==Main Points of the Paper ==
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== Materials and Methods Used ==
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==Phenotype Annotations==
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!|Species!!Taxon ID!!Strain!!Gene (if known)!!OMP!!Phenotype!!Details!!Evidence!!Notes
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==Notes==
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==References==
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Revision as of 13:33, 13 January 2011

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 Online version:10.1016/j.cell.2010.11.052

Keywords


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Main Points of the Paper

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Materials and Methods Used

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Phenotype Annotations

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Species Taxon ID Strain Gene (if known) OMP Phenotype Details Evidence Notes
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Notes

References

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