Evidence map›Paper›PMID 22363624›Full record

ArticlePloS one2012

Controlling the outcome of the Toll-like receptor signaling pathways.

Guilhem Richard, Calin Belta, A Agung Julius, Salomon Amar

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 44% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Combination therapy of insulin-like growth factor I and BTP-2 markedly improves lipopolysaccharide-induced liver injury in mice.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2022
    Article
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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

4 authors at 2 institutions in 1 country.

Guilhem RichardProgram in Bioinformatics, Boston University, Boston, Massachusetts, United States of America.
Calin Belta
A Agung Julius
Salomon Amar
Boston University · USRensselaer Polytechnic Institute · US

Funding

Role of Obesity in InfectionR01DE015989 · NIDCR · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AMAR, SALOMON · 2004 to 2012
$3.9M
NIDCR NIH HHS R01 DE015989NIDCR NIH HHS R01DE015989-S1
6 · The paper itself

Abstract

The Toll-Like Receptors (TLRs) are proteins involved in the immune system that increase cytokine levels when triggered. While cytokines coordinate the response to infection, they appear to be detrimental to the host when reaching too high levels. Several studies have shown that the deletion of specific TLRs was beneficial for the host, as cytokine levels were decreased consequently. It is not clear, however, how targeting other components of the TLR pathways can improve the responses to infections. We applied the concept of Minimal Cut Sets (MCS) to the ihsTLR v1.0 model of the TLR pathways to determine sets of reactions whose knockouts disrupt these pathways. We decomposed the TLR network into 34 modules and determined signatures for each MCS, i.e. the list of targeted modules. We uncovered 2,669 MCS organized in 68 signatures. Very few MCS targeted directly the TLRs, indicating that they may not be efficient targets for controlling these pathways. We mapped the species of the TLR network to genes in human and mouse, and determined more than 10,000 Essential Gene Sets (EGS). Each EGS provides genes whose deletion suppresses the network's outputs.

Indexed as

Signal TransductionAnimalsGenes, EssentialHumansMiceModels, BiologicalReactive Oxygen SpeciesReproducibility of ResultsToll-Like ReceptorsReactive Oxygen SpeciesToll-Like Receptors

Identifiers

PMID22363624
PMCPMC3282698
OpenAlexW1982549518

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.