Article

Abstract

• Chemical genomics expands our understanding of microbial tolerance to inhibitory chemicals, but its scope is often limited by the throughput of genome-scale library construction and genotype-phenotype mapping. Here we report a method for rapid, parallel, and deep characterization of the response to antibiotics in Escherichia coli using a barcoded genome-scale library, next-generation sequencing, and streamlined bioinformatics software. The method provides quantitative growth data (over 200,000 measurements) and identifies contributing antimicrobial resistance and susceptibility alleles. Using multivariate analysis, we also find that subtle differences in the population responses resonate across multiple levels of functional hierarchy. Finally, we use machine learning to identify a unique allelic and proteomic fingerprint for each antibiotic. The method can be broadly applied to tolerance for any chemical from toxic metabolites to next-generation biofuels and antibiotics.

Creator

• Gill, Ryan T
• Mansell, Thomas J
• Knight, Rob
• Weiss, Sophie J
• Mortazavi, Pooneh

Date Issued

• 2016-01-01

Academic Affiliation

• Chemical and Biological Engineering

Journal Title

• PLoS One

Journal Issue/Number

• 1

Journal Volume

• 11

File Extent

• 0146916-0146916

Subject

• Genome
• Chromosome Mapping
• Alleles
• Anti-Bacterial Agents
• Genomic Library
• Escherichia coli
• Microbial
• Drug Resistance
• Bacterial

Dernière modification

• 2019-12-05

Identifier

• PubMed ID: 26771672

Resource Type

• Article

Déclaration de droits

• In Copyright

DOI

•  https://doi.org/10.1371/journal.pone.0146916

ISSN

• 1932-6203

Language

• English [eng]

Des relations

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	parallelMappingOfAntibioticResistanceAllelesInEscherichia.pdf	2019-12-05	Public	Télécharger