Mutant phenotypes for thousands of bacterial genes of unknown function

Morgan N. Price, Kelly M. Wetmore, R. Jordan Waters, Mark Callaghan, Jayashree Ray, Hualan Liu, Jennifer V. Kuehl, Ryan A. Melnyk, Jacob S. Lamson, Yumi Suh, Hans K. Carlson, Zuelma Esquivel, Harini Sadeeshkumar, Romy Chakraborty, Grant M. Zane, Benjamin E. Rubin, Judy D. Wall, Axel Visel, James Bristow, Matthew J. Blow (), Adam P. Arkin () and Adam M. Deutschbauer ()
Additional contact information
Morgan N. Price: Lawrence Berkeley National Laboratory
Kelly M. Wetmore: Lawrence Berkeley National Laboratory
R. Jordan Waters: Lawrence Berkeley National Laboratory
Mark Callaghan: Lawrence Berkeley National Laboratory
Jayashree Ray: Lawrence Berkeley National Laboratory
Hualan Liu: Lawrence Berkeley National Laboratory
Jennifer V. Kuehl: Lawrence Berkeley National Laboratory
Ryan A. Melnyk: Lawrence Berkeley National Laboratory
Jacob S. Lamson: Lawrence Berkeley National Laboratory
Yumi Suh: Lawrence Berkeley National Laboratory
Hans K. Carlson: Lawrence Berkeley National Laboratory
Zuelma Esquivel: Lawrence Berkeley National Laboratory
Harini Sadeeshkumar: Lawrence Berkeley National Laboratory
Romy Chakraborty: Lawrence Berkeley National Laboratory
Grant M. Zane: University of Missouri
Benjamin E. Rubin: University of California
Judy D. Wall: University of Missouri
Axel Visel: Lawrence Berkeley National Laboratory
James Bristow: Lawrence Berkeley National Laboratory
Matthew J. Blow: Lawrence Berkeley National Laboratory
Adam P. Arkin: Lawrence Berkeley National Laboratory
Adam M. Deutschbauer: Lawrence Berkeley National Laboratory

Nature, 2018, vol. 557, issue 7706, 503-509

Abstract: Abstract One-third of all protein-coding genes from bacterial genomes cannot be annotated with a function. Here, to investigate the functions of these genes, we present genome-wide mutant fitness data from 32 diverse bacteria across dozens of growth conditions. We identified mutant phenotypes for 11,779 protein-coding genes that had not been annotated with a specific function. Many genes could be associated with a specific condition because the gene affected fitness only in that condition, or with another gene in the same bacterium because they had similar mutant phenotypes. Of the poorly annotated genes, 2,316 had associations that have high confidence because they are conserved in other bacteria. By combining these conserved associations with comparative genomics, we identified putative DNA repair proteins; in addition, we propose specific functions for poorly annotated enzymes and transporters and for uncharacterized protein families. Our study demonstrates the scalability of microbial genetics and its utility for improving gene annotations.

Date: 2018
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DOI: 10.1038/s41586-018-0124-0

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