Experimental and computational assessment of conditionally essential genes in E. coli

1. Experimental and computational assessment of conditionally essential genes in E. coli Chao WANG, Oct 11 2006

2. Knowledge of which genes in an organism are essential and under what conditions they areessential is of fundamental and practical importance. This knowledge provides us with a uniquetool to refine the interpretation of cellular networks and to map critical points in these networks. From a modeling perspective, a major limitation of the previous gene essentiality studies in E.coli was that they were performed using only partialor heterogenous data. In this study, they used this strain collection to integrate highthroughput experimental data and computational modeling to assess E. coli gene essentiality forgrowth on glycerol-supplemented minimal medium. The results of this conditional essentialityscreen were analyzed in the context of the most current genome-scale metabolic andtranscriptional regulatory model.

3. High-throughput phenotyping of the E. coli gene knock-out collection A recently described collection of E. coli single gene deletion mutants comprising 3,888 deletionmutants were constructedby the method of Datsenko and Wanner. Thisinitial screen yielded about 230 deletion mutants which had slow or no growth on M9-glycerolmedium. A secondary screenwas repeatedand yielded a final set of 119 E. coli deletion mutantsthat represents the conditionally essential complement of genes required for growth on glycerol.

4. Computational Predictions for Essentiality Based on recent updatesto the E. coli genome annotation, two additional metabolic genes (dfp and coaE) wereincluded in the metabolic model. Furthermore, atpI was removedfrom the model. Additional changes in the genome annotationalso have merged (tdcG, araH, and ytfR) andsplit (dgoAD and glcEF) some genes included in the model. As a result 899 metabolic genes areaccounted for in the metabolic model and an additional 104 transcription factors are used in thecombined metabolic and regulatory model. Growth on glycerol minimal medium was simulated bymaximizing flux through a definedbiomass objective function and allowing the uptake of glycerol, NH4, SO4, O2, Pi and the freeexchange of H+, H2O, and CO2.

5. Maximum growth rates of gene knockout strains were calculated with each gene independentlyremoved from the network. When simulating the deletion of a gene, all associated reactionswere removed from the network except for those reactions with isozymes. Gene deletions wherethe predicted maximum growth rate was zero were categorized as essential. To evaluate theeffects of transcription factor mutants, a combined metabolic and regulatory model was used toevaluate whether the deletion of a transcription factor is lethal for growth on glycerol minimalmedium.

6. Cross-genome comparison of conditionally essential genes They used The SEED genomic platform for a cross-genome comparison of metabolic subsystems implicated by the set of conditionally essential E. coli genes identified in this study. A subsystem is defined in The SEED environment as acollection of functional roles (enzymes, transporters, regulators) known to be involved in a well-defined biological process, such as a subnetwork (a cluster of pathways) associated with a particular aspect of metabolism. Through analysis, they monitored only presence or absence of at least a minimal functional variant for each subsystem and each genome in the set. The results were hierarchically clustered for visualization and analysis purposes using the Hamming distance metric andaverage linkage.

7. Quantitative RT-PCR measurements of gene expression Real-time RT-PCR was used to quantify gene expression levels for genes related to glycerolmetabolism.

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