PMID:21185072

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 Link to Supplemental data Publicly-available data set
Keywords	phenotype; phenomic profiling; phenotypic signature; hierarchical clustering; responsive genome; high-throughput; chemical genomics; stress; essential; function; antibiotic resistance; synergy
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Main Points of the Paper

• Key Motivation- provide phenotypes for mutants of genes without functional annotation
• Central Goal- systematically evaluate the impact of every gene deletion on E.coli fitness in diverse environments

• Phenomic Profiling- quantitative description of the response of all single-gene deletions to physiologically relevant stresses and drug challenges
  • profiled ~4,000 genes in 324 conditions covering 114 unique stresses (more than half were antimicrobial/antibiotic stress)
  • identified thousands of phenotypes
  • identified a diverse set of conditionally essential genes
    • Identified 116 rich-media conditionally essential (CE) genes
  • facilitates high-confidence association of genes of unknown function to those of known function
  • generates numerous leads concerning drug function

• Hierarchical clustering
• Phenotypic Signature- response of each mutant strain across all conditions
  • high correlation b/t two phenotypic signatures implies a functional connection b/t genes

Materials and Methods Used

Strain Details

• Keio single-gene deletion library
• essential gene hypomorphs
• RNA/small protein knockout library

Procedure

Phenomic Profiling

Data Analysis

Hierarchical clustering

Phenotype Annotations

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Species	Taxon ID	Strain	Gene (if known)	OMP	Phenotype	Details	Evidence	Notes
Escherichia coli		K-12 BW25113	ECK3620-RFAP	sensitivity to 0.1% bile salts	Growth	slow growth	Growth Curve	rfaP phosphorylates core heptose of lipopolysaccharide
Escherichia coli		K-12 BW25113	ECK0223-LPCA	sensitivity to 1.2 mM DIBUCAINE	Growth	slow growth	Growth Curve	the target of DIBUCAINE is the membrane (pmf)
Escherichia coli		K-12 BW25113	ECK3610-RFAF	sensitivity to 0.1% bile salts	Growth	slow growth	Growth Curve	rfaF = ADP-heptose:LPS heptosyltransferase II target of bile salts: membrane
Escherichia coli		K-12 BW25113	ECK3042-RFAE	sensitivity to 30 µg/ml NOVOBIOCIN	Growth	slow growth	Growth Curve	ECK3042-RFAE = heptose 7-phosphate kinase/heptose 1-phosphate adenyltransferase the target of NOVOBIOCIN is DNA gyrase
Escherichia coli		K-12 BW25113	ECK3610-RFAE	sensitivity to 0.1% bile salts	Growth	slow growth	Growth Curve	ECK3042-RFAE = heptose 7-phosphate kinase/heptose 1-phosphate adenyltransferase target of bile salts: membrane
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References

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