scholarly journals Experimental and Computational Assessment of Conditionally Essential Genes in Escherichia coli

2006 ◽  
Vol 188 (23) ◽  
pp. 8259-8271 ◽  
Author(s):  
Andrew R. Joyce ◽  
Jennifer L. Reed ◽  
Aprilfawn White ◽  
Robert Edwards ◽  
Andrei Osterman ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Minimal Medium ◽  
Essential Genes ◽  
Integrative Model ◽  
Scale Model ◽  
Data Sets ◽  
Data Set ◽  
E Coli ◽  
A Genome ◽  
Genome Scale

ABSTRACT 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.

scholarly journals EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Oliver Hädicke ◽  
Steffen Klamt
Keyword(s):  
Escherichia Coli ◽  
Reference Model ◽  
Model Organism ◽  
Solution Space ◽  
Scale Model ◽  
Reference Network ◽  
Central Metabolism ◽  
E Coli ◽  
A Genome ◽  
Genome Scale

Abstract Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli’s central metabolism.

scholarly journals Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation

Microbiology ◽  
2014 ◽  
Vol 160 (6) ◽  
pp. 1252-1266 ◽  
Author(s):  
Hassan B. Hartman ◽  
David A. Fell ◽  
Sergio Rossell ◽  
Peter Ruhdal Jensen ◽  
Martin J. Woodward ◽  
...  
Keyword(s):  
Salmonella Enterica ◽  
Energy Demand ◽  
Drug Targets ◽  
Minimal Medium ◽  
Model Organism ◽  
Metabolic Model ◽  
Scale Model ◽  
Glucose Minimal Medium ◽  
A Genome ◽  
Genome Scale

Salmonella enterica sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of S. Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.

scholarly journals Structural Systems Biology Evaluation of Metabolic Thermotolerance in Escherichia coli

Science ◽  
2013 ◽  
Vol 340 (6137) ◽  
pp. 1220-1223 ◽  
Author(s):  
Roger L. Chang ◽  
Kathleen Andrews ◽  
Donghyuk Kim ◽  
Zhanwen Li ◽  
Adam Godzik ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Systems Biology ◽  
Structural Information ◽  
Protein Structures ◽  
Limiting Factors ◽  
Scale Model ◽  
Structural Systems ◽  
A Genome ◽  
Structural Systems Biology ◽  
Genome Scale

Genome-scale network reconstruction has enabled predictive modeling of metabolism for many systems. Traditionally, protein structural information has not been represented in such reconstructions. Expansion of a genome-scale model of Escherichia coli metabolism by including experimental and predicted protein structures enabled the analysis of protein thermostability in a network context. This analysis allowed the prediction of protein activities that limit network function at superoptimal temperatures and mechanistic interpretations of mutations found in strains adapted to heat. Predicted growth-limiting factors for thermotolerance were validated through nutrient supplementation experiments and defined metabolic sensitivities to heat stress, providing evidence that metabolic enzyme thermostability is rate-limiting at superoptimal temperatures. Inclusion of structural information expanded the content and predictive capability of genome-scale metabolic networks that enable structural systems biology of metabolism.

scholarly journals Genome-Scale Analysis of the Uses of the Escherichia coli Genome: Model-Driven Analysis of Heterogeneous Data Sets

2003 ◽  
Vol 185 (21) ◽  
pp. 6392-6399 ◽  
Author(s):  
Timothy E. Allen ◽  
Markus J. Herrgård ◽  
Mingzhu Liu ◽  
Yu Qiu ◽  
Jeremy D. Glasner ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Protein Synthesis ◽  
In Silico ◽  
Half Life ◽  
Data Sets ◽  
Expression Data ◽  
E Coli ◽  
Model Driven ◽  
Life Data ◽  
Genome Scale

ABSTRACT The recent availability of heterogeneous high-throughput data types has increased the need for scalable in silico methods with which to integrate data related to the processes of regulation, protein synthesis, and metabolism. A sequence-based framework for modeling transcription and translation in prokaryotes has been established and has been extended to study the expression state of the entire Escherichia coli genome. The resulting in silico analysis of the expression state highlighted three facets of gene expression in E. coli: (i) the metabolic resources required for genome expression and protein synthesis were found to be relatively invariant under the conditions tested; (ii) effective promoter strengths were estimated at the genome scale by using global mRNA abundance and half-life data, revealing genes subject to regulation under the experimental conditions tested; and (iii) large-scale genome location-dependent expression patterns with approximately 600-kb periodicity were detected in the E. coli genome based on the 49 expression data sets analyzed. These results support the notion that a structured model-driven analysis of expression data yields additional information that can be subjected to commonly used statistical analyses. The integration of heterogeneous genome-scale data (i.e., sequence, expression data, and mRNA half-life data) is readily achieved in the context of an in silico model.

scholarly journals Combining Shigella Tn-seq data with Gold-standard E. coli Gene Deletion Data Suggests Rare Transitions between Essential and Non-essential Gene Functionality

2016 ◽  
Author(s):  
Nikki E Freed ◽  
Dirk Bumann ◽  
Olin K Silander
Keyword(s):  
Gold Standard ◽  
Essential Genes ◽  
Cellular Growth ◽  
Data Set ◽  
Protein Coding ◽  
Empirical Measures ◽  
Gene Essentiality ◽  
E Coli ◽  
Protein Coding Genes ◽  
A Genome

Gene essentiality - whether or not a gene is necessary for cell growth - is a fundamental component of gene function. It is not well established how quickly gene essentiality can change, as few studies have compared empirical measures of essentiality between closely related organisms. Here we present the results of a Tn-seq experiment designed to detect essential protein coding genes in the bacterial pathogen Shigella flexneri 2a 2457T on a genome-wide scale. Superficial analysis of this data suggested that 451 protein-coding genes in this Shigella strain are critical for robust cellular growth on rich media. Comparison of this set of genes with a gold-standard data set of essential genes in the closely related Escherichia coli K12 BW25113 suggested that an excessive number of genes appeared essential in Shigella but non-essential in E. coli. Importantly, and in converse to this comparison, we found no genes that were essential in E. coli and non-essential in Shigella, suggesting that many genes were artefactually inferred as essential in Shigella. Controlling for such artefacts resulted in a much smaller set of discrepant genes. Among these, we identified three sets of functionally related genes, two of which have previously been implicated as critical for Shigella growth, but which are dispensable for E. coli growth. The data presented here highlight the small number of protein coding genes for which we have strong evidence that their essentiality status differs between the closely related bacterial taxa E. coli and Shigella. A set of genes involved in acetate utilization provides a canonical example. These results leave open the possibility of developing strain-specific antibiotic treatments targeting such differentially essential genes, but suggest that such opportunities may be rare in closely related bacteria.

scholarly journals A constraint-based modeling approach to reach an improved chemically defined minimal medium for recombinant antiEpEX-scFv production by Escherichia coli

2021 ◽  
Author(s):  
Aidin Behravan ◽  
atieh hashemi ◽  
Sayed-Amir Marashi
Keyword(s):  
Escherichia Coli ◽  
Amino Acids ◽  
Cell Growth ◽  
Recombinant Protein ◽  
Minimal Medium ◽  
Protein Production ◽  
Feeding Strategy ◽  
E Coli ◽  
A Genome ◽  
Constraint Based Modeling

Increasing demand for recombinant therapeutic proteins highlights the necessity of their yield improvement. Culture medium formulation is a popular approach for bioprocess optimization to improve therapeutic protein production. Constraint-based modeling can empower high-precision optimization through information on how media compounds affect metabolism and cell growth. In the current study, a genome-scale metabolic model (GEMM) of Escherichia coli cells was employed to design strategies of minimal medium supplementation for higher antiEpEX-scFv production. Dynamic flux balance analysis of the recombinant E. coli cell model predicted that ammonium was depleted during the process. Based on the simulations, three amino acids (Asn, Gln and Arg) were chosen to be added to the medium to compensate for ammonium depletion. Experimental validation suggested that the addition of these amino acids (one-by-one, or in combinations) can indeed improve cell growth and recombinant protein production. Then, design of experiment was used to optimize the concentrations of amino acids in the growth medium. About two-fold increase in the growth rate and total scFv expression level was observed using this strategy. We conclude that the GEMM-based approach can provide insights into an effective feeding strategy to improve the production of recombinant protein in E. coli.

scholarly journals Improving Designer Glycan Production inEscherichia colithrough Model-Guided Metabolic Engineering

2017 ◽  
Author(s):  
Joseph A. Wayman ◽  
Cameron Glasscock ◽  
Thomas J. Mansell ◽  
Matthew P. DeLisa ◽  
Jeffrey D. Varner
Keyword(s):  
Protein Modification ◽  
Gene Knockout ◽  
Optimal Growth ◽  
Fluorescent Labeling ◽  
Protein Glycosylation ◽  
Scale Model ◽  
E Coli ◽  
A Genome ◽  
Glycosylation Pathway ◽  
Genome Scale

AbstractAsparagine-linked (N-linked) glycosylation is the most common protein modification in eukaryotes, affecting over two-thirds of the proteome. Glycosylation is also critical to the pharmacokinetic activity and immunogenicity of many therapeutic proteins currently produced in complex eukaryotic hosts. The discovery of a protein glycosylation pathway in the pathogenCampylobacter jejuniand its subsequent transfer into laboratory strains ofEscherichia colihas spurred great interest in glycoprotein production in prokaryotes. However, prokaryotic glycoprotein production has several drawbacks, including insufficient availability of non-native glycan precursors. To address this limitation, we used a constraint-based model ofE. colimetabolism in combination with heuristic optimization to design gene knockout strains that overproduced glycan precursors. First, we incorporated reactions associated withC. jejuniglycan assembly into a genome-scale model ofE. colimetabolism. We then identified gene knockout strains that coupled optimal growth to glycan synthesis. Simulations suggested that these growth-coupled glycan overproducing strains had metabolic imbalances that rerouted flux toward glycan precursor synthesis. We then validated the model-identified knockout strains experimentally by measuring glycan expression using a flow cytometric-based assay involving fluorescent labeling of cell surface-displayed glycans. Overall, this study demonstrates the promising role that metabolic modeling can play in optimizing the performance of a next-generation microbial glycosylation platform.

scholarly journals Development of a Web Tool for Escherichia coli Subtyping Based on fimH Alleles

2017 ◽  
Vol 55 (8) ◽  
pp. 2538-2543 ◽  
Author(s):  
Louise Roer ◽  
Veronika Tchesnokova ◽  
Rosa Allesøe ◽  
Mariya Muradova ◽  
Sujay Chattopadhyay ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Phylogenetic Network ◽  
Outbreak Detection ◽  
Whole Genome Sequence ◽  
Complete Agreement ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
E Coli ◽  
Link Type

ABSTRACT The aim of this study was to construct a valid publicly available method for in silico fimH subtyping of Escherichia coli particularly suitable for differentiation of fine-resolution subgroups within clonal groups defined by standard multilocus sequence typing (MLST). FimTyper was constructed as a FASTA database containing all currently known fimH alleles. The software source code is publicly available at https://bitbucket.org/genomicepidemiology/fimtyper , the database is freely available at https://bitbucket.org/genomicepidemiology/fimtyper_db , and a service implementing the software is available at https://cge.cbs.dtu.dk/services/FimTyper . FimTyper was validated on three data sets: one containing Sanger sequences of fimH alleles of 42 E. coli isolates generated prior to the current study (data set 1), one containing whole-genome sequence (WGS) data of 243 third-generation-cephalosporin-resistant E. coli isolates (data set 2), and one containing a randomly chosen subset of 40 E. coli isolates from data set 2 that were subjected to conventional fimH subtyping (data set 3). The combination of the three data sets enabled an evaluation and comparison of FimTyper on both Sanger sequences and WGS data. FimTyper correctly predicted all 42 fimH subtypes from the Sanger sequences from data set 1 and successfully analyzed all 243 draft genomes from data set 2. FimTyper subtyping of the Sanger sequences and WGS data from data set 3 were in complete agreement. Additionally, fimH subtyping was evaluated on a phylogenetic network of 122 sequence type 131 (ST131) E. coli isolates. There was perfect concordance between the typology and fimH -based subclones within ST131, with accurate identification of the pandemic multidrug-resistant clonal subgroup ST131- H 30. FimTyper provides a standardized tool, as a rapid alternative to conventional fimH subtyping, highly suitable for surveillance and outbreak detection.

scholarly journals Genome-Wide Identification of Fitness Factors in Mastitis-Associated Escherichia coli

2017 ◽  
Vol 84 (2) ◽  
Author(s):  
Michael A. Olson ◽  
Timothy W. Siebach ◽  
Joel S. Griffitts ◽  
Eric Wilson ◽  
David L. Erickson
Keyword(s):  
Escherichia Coli ◽  
Mammary Glands ◽  
Data Set ◽  
Bacterial Gene ◽  
Content Type ◽  
E Coli ◽  
Global Comparison ◽  
Genome Wide ◽  
A Genome ◽  
Highly Virulent

ABSTRACTVirulence factors of mammary pathogenicEscherichia coli(MPEC) have not been identified, and it is not known how bacterial gene content influences the severity of mastitis. Here, we report a genome-wide identification of genes that contribute to fitness of MPEC under conditions relevant to the natural history of the disease. A highly virulent clinical isolate (M12) was identified that killedGalleria mellonellaat low infectious doses and that replicated to high numbers in mouse mammary glands and spread to spleens. Genome sequencing was combined with transposon insertion site sequencing to identify MPEC genes that contribute to growth in unpasteurized whole milk, as well as duringG. mellonellaand mouse mastitis infections. These analyses show that strain M12 possesses a unique genomic island encoding a group III polysaccharide capsule that greatly enhances virulence inG. mellonella. Several genes appear critical for MPEC survival in bothG. mellonellaand in mice, including those for nutrient-scavenging systems and resistance to cellular stress. Insertions in the ferric dicitrate receptor genefecAcaused significant fitness defects under all conditions (in milk,G. mellonella, and mice). This gene was highly expressed during growth in milk. Targeted deletion offecAfrom strain M12 caused attenuation inG. mellonellalarvae and reduced growth in unpasteurized cow's milk and lactating mouse mammary glands. Our results confirm that iron scavenging by the ferric dicitrate receptor, which is strongly associated with MPEC strains, is required for MPEC growth and may influence disease severity in mastitis infections.IMPORTANCEMastitis caused byE. coliinflicts substantial burdens on the health and productivity of dairy animals. Strains causing mastitis may express genes that distinguish them from otherE. colistrains and promote infection of mammary glands, but these have not been identified. Using a highly virulent strain, we employed genome-wide mutagenesis and sequencing to discover genes that contribute to mastitis. This extensive data set represents a screen for mastitis-associatedE. colifitness factors and provides the following contributions to the field: (i) global comparison of genes required for different aspects of mastitis infection, (ii) discovery of a unique capsule that contributes to virulence, and (iii) conclusive evidence for the crucial role of iron-scavenging systems in mastitis, particularly the ferric dicitrate transport system. Similar approaches applied to other mastitis-associated strains will uncover conserved targets for prevention or treatment and provide a better understanding of their relationship to otherE. colipathogens.

scholarly journals Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

2008 ◽  
Vol 190 (8) ◽  
pp. 2790-2803 ◽  
Author(s):  
Matthew A. Oberhardt ◽  
Jacek Puchałka ◽  
Kimberly E. Fryer ◽  
Vítor A. P. Martins dos Santos ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
Opportunistic Pathogen ◽  
Scale Model ◽  
Modeling Framework ◽  
Major Life ◽  
Metabolic Versatility ◽  
A Genome ◽  
Scale Properties ◽  
Genome Scale

ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.

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