Evidence map›Paper›PMID 21339814›Full record

ArticlePloS one2011

Controlling the response: predictive modeling of a highly central, pathogen-targeted core response module in macrophage activation.

Jason E McDermott, Michelle Archuleta, Brian D Thrall, Joshua N Adkins, Katrina M Waters

Open access · goldAbstract readEvaluation StudyValidation Study
In one paragraph

Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
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

23 citing papers in PubMed, 36 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Modeling dynamic regulatory processes in stroke.PLoS computational biology · 2012
    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 at 1 institution in 1 country.

Jason E McDermottComputational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, Washington, United States of America. Jason.McDermott@pnl.gov
Michelle Archuleta
Brian D Thrall
Joshua N Adkins
Katrina M Waters
Pacific Northwest National Laboratory · US

Funding

Sytems Analysis of Nanoparticle BiocompatibilityR01ES016212 · NIEHS · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI THRALL, BRIAN D. · 2007 to 2011
$1.9M
SURFACE CHEMISTRY OF MYCOBACTERIAR01AI008401 · NIAID · NATIONAL JEWISH HEALTH · PI GOREN, MAYER B. · 1989 to 1989
–
SURFACE CHEMISTRY OF MYCOBACTERIAR22AI008401 · NIAID · NATIONAL JEWISH HEALTH · PI GOREN, MAYER B. · 1985 to 1989
–
NIAID NIH HHS Y01 AI008401NIAID NIH HHS Y1-AI-8401NIEHS NIH HHS R01 ES016212NIEHS NIH HHS R01ES016212
6 · The paper itself

Abstract

We have investigated macrophage activation using computational analyses of a compendium of transcriptomic data covering responses to agonists of the TLR pathway, Salmonella infection, and manufactured amorphous silica nanoparticle exposure. We inferred regulatory relationship networks using this compendium and discovered that genes with high betweenness centrality, so-called bottlenecks, code for proteins targeted by pathogens. Furthermore, combining a novel set of bioinformatics tools, topological analysis with analysis of differentially expressed genes under the different stimuli, we identified a conserved core response module that is differentially expressed in response to all studied conditions. This module occupies a highly central position in the inferred network and is also enriched in genes preferentially targeted by pathogens. The module includes cytokines, interferon induced genes such as Ifit1 and 2, effectors of inflammation, Cox1 and Oas1 and Oasl2, and transcription factors including AP1, Egr1 and 2 and Mafb. Predictive modeling using a reverse-engineering approach reveals dynamic differences between the responses to each stimulus and predicts the regulatory influences directing this module. We speculate that this module may be an early checkpoint for progression to apoptosis and/or inflammation during macrophage activation.

Indexed as

Models, StatisticalAdaptive ImmunityAnimalsCells, CulturedCluster AnalysisForecastingGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHost-Pathogen InteractionsHumansInflammationMacrophage ActivationMiceMicroarray AnalysisModels, BiologicalSilicon Dioxide

Identifiers

PMID21339814
PMCPMC3038849
OpenAlexW2067750165

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.