ArticlePLoS computational biology2019
Reconciling high-throughput gene essentiality data with metabolic network reconstructions.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 22 citations in OpenAlex.
- Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathogens.PLoS biology · 2024Article
- Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections.NPJ systems biology and applications · 2024Article
- Transcriptome-guided metabolic network analysis reveals rearrangements of carbon flux distribution inmSystems · 2023Article
- Gradients in gene essentiality reshape antibacterial research.FEMS microbiology reviews · 2022Review
- Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis.mSystems · 2021Article
- An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism.NPJ systems biology and applications · 2021Article
- Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments.PLoS computational biology · 2020Article
- Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning.Cell systems · 2020Article
- A systematic assessment of current genome-scale metabolic reconstruction tools.Genome biology · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 explanations for essentiality and reconciling differences between screens. Genome-scale metabolic network reconstructions also enable a high-throughput, quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes. Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.