Mutant phenotypes for thousands of bacterial genes of unknown function

By Morgan N. Price, Kelly M. Wetmore, R. J. Waters, Mark Callaghan, J. Ray, Hualan Liu, J. Kuehl, R. A. Melnyk, Jacob S. Lamson, Yumi Suh and 12 more. 2018

532 citations recorded by the source corpus.

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. A large-scale mutagenesis screen identifies mutant phenotypes for over 11,000 protein-coding genes in bacteria that had previously not been assigned a specific function.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Fast automated reconstruction of genome-scale metabolic models for microbial species and communities (2018, methodology): “The experimental data used for validation was obtained from (33,37–40).”
Linking metagenomics to aquatic microbial ecology and biogeochemical cycles (2019, background): “Hence there is an urgent need to close the sequence-to-function gap inmicrobiology if we are tomodelmetabolismbased on ’omics data (Price et al. 2018).”
Using deep learning to annotate the protein universe (2019, background): “While these approaches are generally successful, at least one-third of microbial proteins cannot be annotated through alignment to characterized sequences [6, 7].”
Deep Semantic Protein Representation for Annotation, Discovery, and Engineering (2018, background): “A recent study by Price et al . described new experimental approaches to identifying gene function for 33 bacterial organisms 36 .”