Evidence map›Paper›PMID 26102206›Full record

ArticlePLoS computational biology2015

Integration of Metabolic and Quorum Sensing Signals Governing the Decision to Cooperate in a Bacterial Social Trait.

Kerry E Boyle, Hilary Monaco, Dave van Ditmarsch, Maxime Deforet, Joao B Xavier

Abstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Swarming ofBiophysics reviews · 2023
    Review
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Evidence for biosurfactant-induced flow in corners and bacterial spreading in unsaturated porous media.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
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.

Kerry E BoyleProgram in Immunology and Microbial Pathogenesis, Weill Cornell Graduate School of Medical Sciences, New York, New York, United States of America; Program in Computational Biology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
Hilary MonacoProgram in Computational Biology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America; Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, United States of America.
Dave van DitmarschProgram in Computational Biology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
Maxime DeforetProgram in Computational Biology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
Joao B XavierProgram in Immunology and Microbial Pathogenesis, Weill Cornell Graduate School of Medical Sciences, New York, New York, United States of America; Program in Computational Biology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America; Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, United States of America.

Funding

IMMUNOLOGY RESEARCH TRAINING PROGRAMT32AI007621 · NIAID · WEILL MEDICAL COLL OF CORNELL UNIV · PI CESARMAN, ETHEL · 1999 to 2014
$3.4M
Engineering microbial social interactions: Towards new anti-biofilm therapiesDP2OD008440 · OD · SLOAN-KETTERING INST CAN RESEARCH · PI XAVIER, JOAO · 2011 to 2015
$2.9M
NIAID NIH HHS T32 AI007621NIH HHS DP2 OD008440NIH HHS DP2OD008440
6 · The paper itself

Abstract

Many unicellular organisms live in multicellular communities that rely on cooperation between cells. However, cooperative traits are vulnerable to exploitation by non-cooperators (cheaters). We expand our understanding of the molecular mechanisms that allow multicellular systems to remain robust in the face of cheating by dissecting the dynamic regulation of cooperative rhamnolipids required for swarming in Pseudomonas aeruginosa. We combine mathematical modeling and experiments to quantitatively characterize the integration of metabolic and population density signals (quorum sensing) governing expression of the rhamnolipid synthesis operon rhlAB. The combined computational/experimental analysis reveals that when nutrients are abundant, rhlAB promoter activity increases gradually in a density dependent way. When growth slows down due to nutrient limitation, rhlAB promoter activity can stop abruptly, decrease gradually or even increase depending on whether the growth-limiting nutrient is the carbon source, nitrogen source or iron. Starvation by specific nutrients drives growth on intracellular nutrient pools as well as the qualitative rhlAB promoter response, which itself is modulated by quorum sensing. Our quantitative analysis suggests a supply-driven activation that integrates metabolic prudence with quorum sensing in a non-digital manner and allows P. aeruginosa cells to invest in cooperation only when the population size is large enough (quorum sensing) and individual cells have enough metabolic resources to do so (metabolic prudence). Thus, the quantitative description of rhlAB regulatory dynamics brings a greater understating to the regulation required to make swarming cooperation stable.

Indexed as

Gene Expression Regulation, BacterialQuorum SensingAlgorithmsBacterial ProteinsBiomassFoodGenes, BacterialGreen Fluorescent ProteinsIronKineticsLipidsMetabolic Networks and PathwaysMicroscopy, FluorescenceModels, TheoreticalNitrogenOperonBacterial ProteinsGreen Fluorescent ProteinsIronLipidsNitrogen

Identifiers

PMID26102206
PMCPMC4477906

What OpenQuestion holds

Textmetadata
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.