Reconciling high-throughput gene essentiality data with metabolic network reconstructions.

ABSTRACT: The identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets. Essential genes can be identified on a genome-scale using transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes. To illustrate the scope of these challenges, we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets, revealing substantial differences between the screens. We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional exp

SUBMITTER: Blazier AS 

PROVIDER: S-EPMC6478342 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature