Experimental and Computational Assessment of Conditionally Essential Genes in Escherichia coli

Program in Bioinformatics, University of California, San Diego, La Jolla, California 92093, USA.
Journal of Bacteriology (Impact Factor: 2.81). 01/2007; 188(23):8259-71. DOI: 10.1128/JB.00740-06
Source: PubMed


Genome-wide gene essentiality data sets are becoming available for Escherichia coli, but these data sets have yet to be analyzed in the context of a genome scale model. Here, we present an integrative model-driven
analysis of the Keio E. coli mutant collection screened in this study on glycerol-supplemented minimal medium. Out of 3,888 single-deletion mutants tested,
119 mutants were unable to grow on glycerol minimal medium. These conditionally essential genes were then evaluated using
a genome scale metabolic and transcriptional-regulatory model of E. coli, and it was found that the model made the correct prediction in ∼91% of the cases. The discrepancies between model predictions
and experimental results were analyzed in detail to indicate where model improvements could be made or where the current literature
lacks an explanation for the observed phenotypes. The identified set of essential genes and their model-based analysis indicates
that our current understanding of the roles these essential genes play is relatively clear and complete. Furthermore, by analyzing
the data set in terms of metabolic subsystems across multiple genomes, we can project which metabolic pathways are likely
to play equally important roles in other organisms. Overall, this work establishes a paradigm that will drive model enhancement
while simultaneously generating hypotheses that will ultimately lead to a better understanding of the organism.

Download full-text


Available from: Tomoya Baba,
28 Reads
  • Source
    • "The model was modified to accommodate genetic differences between MG1655 and BW25113. Since the araBAD, rhaBAD, and lacZ genes are absent from the BW25113 strain, the associated metabolic reactions were removed (Joyce et al., 2006). The upper limits of the glucose and oxygen uptake rates were set to 10 and 0 mmol/gDCW/h, respectively, to simulate anaerobic growth on minimal glucose medium. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Finding all optimal solutions for a metabolic model is the challenge of metabolic modeling, but there is no practical algorithm for large scale models. A two-phase algorithm is proposed here to systematically identify all optimal solutions. In phase 1, the model is reduced using the FVA approach; in phase 2, all optimal solutions are searched by the addition of a binary variable to convert the model to an MILP problem. The proposed approach proved itself to be a more tractable method for large scale metabolic models when compared with the previously proposed algorithm. The algorithm was implemented on a metabolic model of Escherichia coli (iJR904) to find all optimal flux distributions. The model was reduced from 1076 to 80 fluxes and from 998 to 54 equations and the MILP problem was solved, resulting in 30,744 various flux distributions. For the first time, this number of optimal solutions has been reported.
    Computers & Chemical Engineering 02/2015; 73:64-69. DOI:10.1016/j.compchemeng.2014.11.006 · 2.78 Impact Factor
  • Source
    • "Along with data obtained by high-throughput systems, modeling of metabolism by mathematical approaches has become an important tool for analyzing cell responses and unravel the metabolic regulation between the cell information/control systems [111]. Moreover, genome-scale models of metabolism have been analyzed by constraint-based approaches [123]. Gene deletion effects over flux distributions have also been studied in order to find the combination that provides the best metabolic performance on a given condition. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The production of aromatic amino acids using fermentation processes with recombinant microorganisms can be an advantageous approach to reach their global demands. In addition, a large array of compounds with alimentary and pharmaceutical applications can potentially be synthesized from intermediates of this metabolic pathway. However, contrary to other amino acids and primary metabolites, the artificial channelling of building blocks from central metabolism towards the aromatic amino acid pathway is complicated to achieve in an efficient manner. The length and complex regulation of this pathway have progressively called for the employment of more integral approaches, promoting the merge of complementary tools and techniques in order to surpass metabolic and regulatory bottlenecks. As a result, relevant insights on the subject have been obtained during the last years, especially with genetically modified strains of Escherichia coli. By combining metabolic engineering strategies with developments in synthetic biology, systems biology and bioprocess engineering, notable advances were achieved regarding the generation, characterization and optimization of E. coli strains for the overproduction of aromatic amino acids, some of their precursors and related compounds. In this paper we review and compare recent successful reports dealing with the modification of metabolic traits to attain these objectives.
    Microbial Cell Factories 12/2014; 13(126). DOI:10.1186/s12934-014-0126-z · 4.22 Impact Factor
  • Source
    • "The human essential genes were downloaded from the OGEE database (build: 304) (Chen et al., 2012). Because the conditional essential genes were essential only under certain circumstances (Joyce et al., 2006), these genes were not used in this study. The protein product of an essential gene was regarded as an essential protein. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Proteins are responsible for performing the vast majority of cellular functions which are critical to a cell’s survival. The knowledge of the subcellular localization of proteins can provide valuable information about their molecular functions. Therefore, one of the fundamental goals in cell biology and proteomics is to analyze the subcellular localizations and functions of these proteins. Recent large-scale human genomics and proteomics studies have made it possible to characterize human proteins at a subcellular localization level. In this study, according to the annotation in Swiss-Prot, 8842 human proteins were classified into seven subcellular localizations. Human proteins in the seven subcellular localizations were compared by using topological properties, biological properties, codon usage indices, mRNA expression levels, protein complexity and physicochemical properties. All these properties were found to be significantly different in the seven categories. In addition, based on these properties and pseudo-amino acid compositions, a machine learning classifier was built for the prediction of protein subcellular localization. The study presented here was an attempt to address the aforementioned properties for comparing human proteins of different subcellular localizations. We hope our findings presented in this study may provide important help for the prediction of protein subcellular localization and for understanding the general function of human proteins in cells.
    Journal of Theoretical Biology 10/2014; 358:61–73. DOI:10.1016/j.jtbi.2014.05.008 · 2.12 Impact Factor
Show more