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Reframing gene essentiality in terms of adaptive flexibility

ABSTRACT: Essentiality assays are commonly practiced as important tools for the discovery of gene functions. Growth/no growth screens of single gene knockout strain collections are often utilized to test the predictive power of genome-scale models. False positive predictions occur when computational analysis predicts a gene to be non-essential, however experimental screens deem the gene to be essential. One explanation for this inconsistency is that the model contains the wrong information, possibly an incorrectly annotated alternative pathway or isozyme reaction. Inconsistencies could also be attributed to experimental limitations, such as growth tests with arbitrary time cut-offs. The focus of this study was to resolve such inconsistencies to better understand isozyme activities and gene essentiality. Gene-deletion strains associated with false positive predictions of gene essentiality on defined minimal medium for Escherichia coli were targeted for extended growth tests followed by population sequencing.

ORGANISM(S): Escherichia coli BW25113

PROVIDER: GSE117303 | GEO | 2018/07/19

REPOSITORIES: GEO

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Project description:Suthers2009 - Genome-scale metabolic network of Mycoplasma genitalium (iPS189) This model is described in the article: A genome-scale metabolic reconstruction of Mycoplasma genitalium, iPS189. Suthers PF, Dasika MS, Kumar VS, Denisov G, Glass JI, Maranas CD. PLoS Comput. Biol. 2009 Feb; 5(2): e1000285 Abstract: With a genome size of approximately 580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms. This model is hosted on BioModels Database and identified by: MODEL1507180052. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Reframing gene essentiality in terms of adaptive flexibility

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