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Nature. 2018 May;557(7706):503-509. doi: 10.1038/s41586-018-0124-0. Epub 2018 May 16.

Mutant phenotypes for thousands of bacterial genes of unknown function.

Author information

1
Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
2
Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
3
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
4
Department of Biochemistry, University of Missouri, Columbia, MO, USA.
5
Division of Biological Sciences, University of California, San Diego, CA, USA.
6
School of Natural Sciences, University of California, Merced, CA, USA.
7
Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. mjblow@lbl.gov.
8
Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. aparkin@lbl.gov.
9
Department of Bioengineering, University of California, Berkeley, CA, USA. aparkin@lbl.gov.
10
Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. amdeutschbauer@lbl.gov.
11
Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA. amdeutschbauer@lbl.gov.

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.

PMID:
29769716
DOI:
10.1038/s41586-018-0124-0
[Indexed for MEDLINE]
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