Reconciling high-throughput gene essentiality data with metabolic network reconstructions.
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 in...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC6478342?pdf=render |
| _version_ | 1818013137706680320 |
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| author | Anna S Blazier Jason A Papin |
| author_facet | Anna S Blazier Jason A Papin |
| author_sort | Anna S Blazier |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T06:29:35Z |
| format | Article |
| id | doaj.art-201b031f3a17417e8309d39d8f9b9e36 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-14T06:29:35Z |
| publishDate | 2019-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-201b031f3a17417e8309d39d8f9b9e362022-12-22T02:07:40ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-04-01154e100650710.1371/journal.pcbi.1006507Reconciling high-throughput gene essentiality data with metabolic network reconstructions.Anna S BlazierJason A PapinThe 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.http://europepmc.org/articles/PMC6478342?pdf=render |
| spellingShingle | Anna S Blazier Jason A Papin Reconciling high-throughput gene essentiality data with metabolic network reconstructions. PLoS Computational Biology |
| title | Reconciling high-throughput gene essentiality data with metabolic network reconstructions. |
| title_full | Reconciling high-throughput gene essentiality data with metabolic network reconstructions. |
| title_fullStr | Reconciling high-throughput gene essentiality data with metabolic network reconstructions. |
| title_full_unstemmed | Reconciling high-throughput gene essentiality data with metabolic network reconstructions. |
| title_short | Reconciling high-throughput gene essentiality data with metabolic network reconstructions. |
| title_sort | reconciling high throughput gene essentiality data with metabolic network reconstructions |
| url | http://europepmc.org/articles/PMC6478342?pdf=render |
| work_keys_str_mv | AT annasblazier reconcilinghighthroughputgeneessentialitydatawithmetabolicnetworkreconstructions AT jasonapapin reconcilinghighthroughputgeneessentialitydatawithmetabolicnetworkreconstructions |