Evidence map›Paper›PMID 20661428›Full record

ArticlePLoS pathogens2010

Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

Masanao Sato, Kenichi Tsuda, Lin Wang, John Coller, Yuichiro Watanabe, Jane Glazebrook, Fumiaki Katagiri

Abstract read
In one paragraph

Article in PLoS pathogens, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.

0numbers the graph read from it
0cells of the map it votes in
70citing 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

70 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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  7. Review
  8. mBio · 2021
    Article
  9. Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  10. Article
  11. Robustness of plant quantitative disease resistance is provided by a decentralized immune network.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  12. Article
  13. Article
  14. Article
  15. Heterologous Expression of the GrapevineInternational journal of molecular sciences · 2018
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. TheLife science alliance · 2018
    Article

10 more citing papers are in PubMed but not listed here.

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

7 authors.

Masanao SatoDepartment of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, Minnesota, United States of America.
Kenichi Tsuda
Lin Wang
John Coller
Yuichiro Watanabe
Jane Glazebrook
Fumiaki Katagiri

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; and (ii) negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector-switching" network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness.

Indexed as

ArabidopsisBacterial ProteinsGene Expression ProfilingGene Expression Regulation, PlantModels, BiologicalPlant DiseasesPseudomonas syringaeRegulatory Elements, TranscriptionalRNA, PlantSignal TransductionavrRpt2 protein, Pseudomonas syringaeBacterial ProteinsRNA, Plant

Identifiers

PMID20661428
PMCPMC2908620

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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.