Evidence map›Paper›PMID 30228271›Full record

ArticleNature communications2018

Evolution of gene knockout strains of E. coli reveal regulatory architectures governed by metabolism.

Douglas McCloskey, Sibei Xu, Troy E Sandberg, Elizabeth Brunk, Ying Hefner, Richard Szubin, Adam M Feist, Bernhard O Palsson

Abstract read
In one paragraph

Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
–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

39 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. ACS synthetic biology · 2021
    Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Macrolide resistance trajectories across threeFrontiers in microbiology · 2026
    Article
  8. Article
  9. Advances in adaptive laboratory evolution applications forSynthetic and systems biotechnology · 2025
    Review
  10. Article
  11. Article
  12. Article
  13. Experimental evolution for cell biology.Trends in cell biology · 2023
    Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. 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

8 authors.

Douglas McCloskeyDepartment of Bioengineering, University of California-San Diego, La Jolla, CA, 92093, USA.
Sibei XuDepartment of Bioengineering, University of California-San Diego, La Jolla, CA, 92093, USA.
Troy E SandbergDepartment of Bioengineering, University of California-San Diego, La Jolla, CA, 92093, USA.ORCID http://orcid.org/0000-0003-3240-3659
Elizabeth BrunkDepartment of Bioengineering, University of California-San Diego, La Jolla, CA, 92093, USA.
Ying HefnerDepartment of Bioengineering, University of California-San Diego, La Jolla, CA, 92093, USA.
Richard SzubinDepartment of Bioengineering, University of California-San Diego, La Jolla, CA, 92093, USA.
Adam M FeistDepartment of Bioengineering, University of California-San Diego, La Jolla, CA, 92093, USA.ORCID http://orcid.org/0000-0002-8630-4800
Bernhard O PalssonDepartment of Bioengineering, University of California-San Diego, La Jolla, CA, 92093, USA. bpalsson@ucsd.edu.ORCID http://orcid.org/0000-0003-2357-6785

Funding

Novo Nordisk NNF10CC1016517
6 · The paper itself

Abstract

Biological regulatory network architectures are multi-scale in their function and can adaptively acquire new functions. Gene knockout (KO) experiments provide an established experimental approach not just for studying gene function, but also for unraveling regulatory networks in which a gene and its gene product are involved. Here we study the regulatory architecture of Escherichia coli K-12 MG1655 by applying adaptive laboratory evolution (ALE) to metabolic gene KO strains. Multi-omic analysis reveal a common overall schema describing the process of adaptation whereby perturbations in metabolite concentrations lead regulatory networks to produce suboptimal states, whose function is subsequently altered and re-optimized through acquisition of mutations during ALE. These results indicate that metabolite levels, through metabolite-transcription factor interactions, have a dominant role in determining the function of a multi-scale regulatory architecture that has been molded by evolution.

Indexed as

Evolution, MolecularEscherichia coli K12Gene Knockout TechniquesGene Regulatory NetworksMetabolic Networks and PathwaysMutationPhenotype

Identifiers

PMID30228271
PMCPMC6143558

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.