Evidence map›Paper›PMID 22044664›Full record

ArticleBMC systems biology2011

The evolution of metabolic networks of E. coli.

David J Baumler, Roman G Peplinski, Jennifer L Reed, Jeremy D Glasner, Nicole T Perna

Abstract read
In one paragraph

Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed.

  1. Article
  2. The EcoCyc database (2025).EcoSal Plus · 2025
    Review
  3. Article
  4. The EcoCyc Database (2023).EcoSal Plus · 2023
    Review
  5. Article
  6. Article
  7. Comparative Pathogenomics ofInfection and immunity · 2021
    Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. The EcoCyc Database.EcoSal Plus · 2018
    Review
  15. Article
  16. Review
  17. Review
  18. Gut pathogens · 2018
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

David J BaumlerGenome Center of Wisconsin, University of Wisconsin-Madison, Madison, Wisconsin, USA. dbaumler@wisc.edu
Roman G Peplinski
Jennifer L Reed
Jeremy D Glasner
Nicole T Perna

Funding

Research Training for Computation and Informatics in Biology and MedicineT15LM007359 · NLM · UNIVERSITY OF WISCONSIN-MADISON · PI Mark W. Craven, Colin Noel Dewey · 2002 to 2026
$22.6M
Molecular Evolution of Microbial Pathogen GenomesR01GM062994 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI PERNA, NICOLE T · 2001 to 2009
$1.9M
NIGMS NIH HHS GM62994-02NLM NIH HHS 5T15LM007359NLM NIH HHS T15 LM007359
6 · The paper itself

Abstract

backgroundDespite the availability of numerous complete genome sequences from E. coli strains, published genome-scale metabolic models exist only for two commensal E. coli strains. These models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for E. coli strains could enhance these efforts.

resultsWe used the genomic information from 16 E. coli strains to generate an E. coli pangenome metabolic network by evaluating their collective 76,990 ORFs. Each of these ORFs was assigned to one of 17,647 ortholog groups including ORFs associated with reactions in the most recent metabolic model for E. coli K-12. For orthologous groups that contain an ORF already represented in the MG1655 model, the gene to protein to reaction associations represented in this model could then be easily propagated to other E. coli strain models. All remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model (iEco1712_pan). The pangenome model included reactions from a metabolic model update for E. coli K-12 MG1655 (iEco1339_MG1655) and enabled development of five additional strain-specific genome-scale metabolic models. These additional models include a second K-12 strain (iEco1335_W3110) and four pathogenic strains (two enterohemorrhagic E. coli O157:H7 and two uropathogens). When compared to the E. coli K-12 models, the metabolic models for the enterohemorrhagic (iEco1344_EDL933 and iEco1345_Sakai) and uropathogenic strains (iEco1288_CFT073 and iEco1301_UTI89) contained numerous lineage-specific gene and reaction differences. All six E. coli models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with 0.2% (w/v) glucose. An ancestral genome-scale metabolic model based on conserved ortholog groups in all 16 E. coli genomes was also constructed, reflecting the conserved ancestral core of E. coli metabolism (iEco1053_core). Comparative analysis of all six strain-specific E. coli models revealed that some of the pathogenic E. coli strains possess reactions in their metabolic networks enabling higher biomass yields on glucose. Finally the lineage-specific metabolic traits were compared to the ancestral core model predictions to derive new insight into the evolution of metabolism within this species.

conclusionOur findings demonstrate that a pangenome-scale metabolic model can be used to rapidly construct additional E. coli strain-specific models, and that quantitative models of different strains of E. coli can accurately predict strain-specific phenotypes. Such pangenome and strain-specific models can be further used to engineer metabolic phenotypes of interest, such as designing new industrial E. coli strains.

Indexed as

Evolution, MolecularMetabolic Networks and PathwaysModels, BiologicalEscherichia coliGenome, BacterialPhylogenySalmonella

Identifiers

PMID22044664
PMCPMC3229490

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.