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PLoS One. 2016 Jan 15;11(1):e0146916. doi: 10.1371/journal.pone.0146916. eCollection 2016.

Parallel Mapping of Antibiotic Resistance Alleles in Escherichia coli.

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Department of Chemical and Biological Engineering, University of Colorado Boulder, 3415 Colorado Avenue, Boulder, Colorado, 80303, United States of America.
Department of Computer Science, University of Colorado Boulder, 1111 Engineering Drive ECOT 717, Boulder, CO 80303, United States of America.
Department of Pediatrics, University of California San Diego School of Medicine, 9500 Gilman Drive, MC 0602, La Jolla, CA 92093, United States of America.
Department of Computer Science & Engineering, University of California San Diego, 9500 Gilman Drive, MC 0404, La Jolla, CA 92093, United States of America.


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.

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