Evidence map›Paper›PMID 29907104›Full record

ArticleBMC systems biology2018

Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss.

Colin P McNally, Elhanan Borenstein

Abstract read
In one paragraph

Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Metabolic Modeling ofMetabolites · 2023
    Article
  2. Article
  3. Article
  4. Review
  5. Modeling approaches for probing cross-feeding interactions in the human gut microbiome.Computational and structural biotechnology journal · 2022
    Review
  6. Article
  7. Article
  8. The microbial exometabolome: ecological resource and architect of microbial communities.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020
    Review
  9. Review
  10. From Microbial Communities to Distributed Computing Systems.Frontiers in bioengineering and biotechnology · 2020
    Review
  11. Review
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. 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

2 authors.

Colin P McNallyDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Elhanan BorensteinDepartment of Genome Sciences, University of Washington, Seattle, WA, USA. elbo@uw.edu.

Funding

INTERDISCIPLINARY TRAINING IN GENOMIC SCIENCEST32HG000035 · NHGRI · UNIVERSITY OF WASHINGTON · PI Bruce Colston Trapnell · 1995 to 2026
$24.2M
A Computational Framework for Designing Microbiome ManipulationDP2AT007802 · NCCIH · UNIVERSITY OF WASHINGTON · PI BORENSTEIN, ELHANAN · 2012 to 2012
$2.3M
Metabolic model-based integrative study of the relationship between the gut microbiome, metabolome, and dietR01GM124312 · NIGMS · UNIVERSITY OF WASHINGTON · PI BORENSTEIN, ELHANAN · 2017 to 2018
$593k
NCCIH NIH HHS DP2 AT007802NHGRI NIH HHS T32 HG000035NIGMS NIH HHS R01 GM124312
6 · The paper itself

Abstract

backgroundMetabolic dependencies between microbial species have a significant impact on the assembly and activity of microbial communities. However, the evolutionary origins of such dependencies and the impact of metabolic and genomic architecture on their emergence are not clear.

resultsTo address these questions, we developed a novel framework, coupling a reductive evolution model with a multi-species genome-scale metabolic model to simulate the evolution of two-species microbial communities. Simulating thousands of independent evolutionary trajectories, we surprisingly found that under certain environmental and evolutionary settings metabolic dependencies emerged frequently even though our model does not include explicit selection for cooperation. Evolved dependencies involved cross-feeding of a diverse set of metabolites, reflecting constraints imposed by metabolic network architecture. We additionally found metabolic 'missed opportunities', wherein species failed to capitalize on metabolites made available by their partners. Examining the genes deleted in each evolutionary trajectory and the deletion timing further revealed both genome-wide properties and specific metabolic mechanisms associated with species interaction.

conclusionOur findings provide insight into the evolution of cooperative interaction among microbial species and a unique view into the way such relationships emerge.

Indexed as

Models, BiologicalBacteriaEvolution, MolecularGenes, Bacterial

Identifiers

PMID29907104
PMCPMC6003207

What OpenQuestion holds

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LicenceCC BY
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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.