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Phenotypic landscape of a bacterial cell |
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| Authors: | Nichols Robert J Sen Saunak Choo Yoe Jin Beltrao Pedro Zietek Matylda Chaba Rachna Lee Sueyoung Kazmierczak Krystyna M Lee Karis J Wong Angela Shales Michael Lovett Susan Winkler Malcolm E Krogan Nevan J Typas Athanasios Gross Carol A |
| Affiliation: | 1 Oral and Craniofacial Sciences Graduate Program, University of California, San Francisco, 513 Parnassus Avenue, San Francisco, CA 94143, USA 2 Department of Microbiology and Immunology, University of California, San Francisco, 600 16th Street, San Francisco, CA 94158, USA 3 Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94107, USA 4 Department of Cellular and Molecular Pharmacology and The California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, CA 94158, USA 5 Department of Biology, Indiana University, Bloomington, IN 47405, USA 6 Department of Biology and Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454-9110, USA 7 Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94158, USA |
| 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. |
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