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A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.

Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ - Mol. Syst. Biol. (2007)

Bottom Line: A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates.Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented.The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.

ABSTRACT
An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

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Classification of the ORFs, reactions and metabolites included in iAF1260. (A) Coverage of characterized ORFs from each of the COGs functional classes included in iAF1260 and five previous reconstructions. The percentage given is the total coverage accounted for in iAF1260 for each class. Some ORFs included in the reconstructions did not have a COG functional class assignment (see Supplemental information). (B) The number of reactions (both gene-associated and non-gene associated) that are associated to ORFs from each COG functional class. Since ORFs can belong to multiple classes, the percent unique in each class is listed. Non-gene-associated reactions were assigned to a class manually. (C) The number of metabolites that participate in reactions from each functional class and the percent unique in each class. Other (OT) includes classes J, K, L, O, T, U, V (see Supplementary information). NC, no COG assignment.
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f1: Classification of the ORFs, reactions and metabolites included in iAF1260. (A) Coverage of characterized ORFs from each of the COGs functional classes included in iAF1260 and five previous reconstructions. The percentage given is the total coverage accounted for in iAF1260 for each class. Some ORFs included in the reconstructions did not have a COG functional class assignment (see Supplemental information). (B) The number of reactions (both gene-associated and non-gene associated) that are associated to ORFs from each COG functional class. Since ORFs can belong to multiple classes, the percent unique in each class is listed. Non-gene-associated reactions were assigned to a class manually. (C) The number of metabolites that participate in reactions from each functional class and the percent unique in each class. Other (OT) includes classes J, K, L, O, T, U, V (see Supplementary information). NC, no COG assignment.

Mentions: A breakdown of ORFs, reactions and metabolites included in iAF1260 and earlier reconstructions (Majewski and Domach, 1990; Varma and Palsson, 1993; Varma et al, 1993; Pramanik and Keasling, 1997, 1998; Edwards and Palsson, 2000; Reed et al, 2003) are given in Figure 1 and Supplementary information. Figure 1 was generated using the functional categories assigned through the clusters of orthologous groups (COGs) ontology (http://www.ncbi.nlm.nih.gov/COG/) to classify the reactions included in the E. coli metabolic reconstruction. Figure 1A details the number of ORFs from each COG functional class that were included in iAF1260, as well as five previous versions of the E. coli reconstruction, to indicate the areas in which the network reconstruction has matured with each successive release. The largest increase in coverage compared with iJR904 (Reed et al, 2003) is found in inorganic ion transport and metabolism (26–56%, respectively, 73 ORFs). Overall, amino acid and nucleotide transport and metabolism have the highest number of ORFs and percent coverage in iAF1260 (256 and 89%, respectively). Ion transport and utilization was recognized as an underrepresented area of metabolism in previous reconstructions and was specifically expanded and incorporated into simulations using iAF1260. Figure 1B and C depict the classification of reactions and metabolites in iAF1260 tied to each COG functional class. The largest number of reactions and metabolites associated to ORFs in one COG functional class is in amino-acid transport and metabolism and cell wall/membrane/envelope biosynthesis, respectively; furthermore, lipid transport and metabolism has the highest reaction to ORF ratio (5.8), followed by secondary metabolites biosynthesis, transport and catabolism (4.6) and cell wall/membrane/envelope biogenesis (3.4). The large reaction to ORF ratio highlights the fact that the proteins in these classes act on a large number of molecules that only differ slightly in structure. Furthermore, the highest number of unique metabolites that participate in reactions from one class was from coenzyme transport and metabolism. This finding points out the specialized nature of the proteins in coenzyme transport and metabolism pathways (Figure 1).


A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.

Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ - Mol. Syst. Biol. (2007)

Classification of the ORFs, reactions and metabolites included in iAF1260. (A) Coverage of characterized ORFs from each of the COGs functional classes included in iAF1260 and five previous reconstructions. The percentage given is the total coverage accounted for in iAF1260 for each class. Some ORFs included in the reconstructions did not have a COG functional class assignment (see Supplemental information). (B) The number of reactions (both gene-associated and non-gene associated) that are associated to ORFs from each COG functional class. Since ORFs can belong to multiple classes, the percent unique in each class is listed. Non-gene-associated reactions were assigned to a class manually. (C) The number of metabolites that participate in reactions from each functional class and the percent unique in each class. Other (OT) includes classes J, K, L, O, T, U, V (see Supplementary information). NC, no COG assignment.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC1911197&req=5

f1: Classification of the ORFs, reactions and metabolites included in iAF1260. (A) Coverage of characterized ORFs from each of the COGs functional classes included in iAF1260 and five previous reconstructions. The percentage given is the total coverage accounted for in iAF1260 for each class. Some ORFs included in the reconstructions did not have a COG functional class assignment (see Supplemental information). (B) The number of reactions (both gene-associated and non-gene associated) that are associated to ORFs from each COG functional class. Since ORFs can belong to multiple classes, the percent unique in each class is listed. Non-gene-associated reactions were assigned to a class manually. (C) The number of metabolites that participate in reactions from each functional class and the percent unique in each class. Other (OT) includes classes J, K, L, O, T, U, V (see Supplementary information). NC, no COG assignment.
Mentions: A breakdown of ORFs, reactions and metabolites included in iAF1260 and earlier reconstructions (Majewski and Domach, 1990; Varma and Palsson, 1993; Varma et al, 1993; Pramanik and Keasling, 1997, 1998; Edwards and Palsson, 2000; Reed et al, 2003) are given in Figure 1 and Supplementary information. Figure 1 was generated using the functional categories assigned through the clusters of orthologous groups (COGs) ontology (http://www.ncbi.nlm.nih.gov/COG/) to classify the reactions included in the E. coli metabolic reconstruction. Figure 1A details the number of ORFs from each COG functional class that were included in iAF1260, as well as five previous versions of the E. coli reconstruction, to indicate the areas in which the network reconstruction has matured with each successive release. The largest increase in coverage compared with iJR904 (Reed et al, 2003) is found in inorganic ion transport and metabolism (26–56%, respectively, 73 ORFs). Overall, amino acid and nucleotide transport and metabolism have the highest number of ORFs and percent coverage in iAF1260 (256 and 89%, respectively). Ion transport and utilization was recognized as an underrepresented area of metabolism in previous reconstructions and was specifically expanded and incorporated into simulations using iAF1260. Figure 1B and C depict the classification of reactions and metabolites in iAF1260 tied to each COG functional class. The largest number of reactions and metabolites associated to ORFs in one COG functional class is in amino-acid transport and metabolism and cell wall/membrane/envelope biosynthesis, respectively; furthermore, lipid transport and metabolism has the highest reaction to ORF ratio (5.8), followed by secondary metabolites biosynthesis, transport and catabolism (4.6) and cell wall/membrane/envelope biogenesis (3.4). The large reaction to ORF ratio highlights the fact that the proteins in these classes act on a large number of molecules that only differ slightly in structure. Furthermore, the highest number of unique metabolites that participate in reactions from one class was from coenzyme transport and metabolism. This finding points out the specialized nature of the proteins in coenzyme transport and metabolism pathways (Figure 1).

Bottom Line: A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates.Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented.The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.

ABSTRACT
An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

Show MeSH
Related in: MedlinePlus