Evidence map›Paper›PMID 22355360›Full record

ArticlePloS one2012

Identifying neighborhoods of coordinated gene expression and metabolite profiles.

Timothy Hancock, Nicolas Wicker, Ichigaku Takigawa, Hiroshi Mamitsuka

Abstract 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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
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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.

Timothy HancockBioinformatics Center, Kyoto University, Kyoto, Japan. timhancock@kuicr.kyoto-u.ac.jp
Nicolas Wicker
Ichigaku Takigawa
Hiroshi Mamitsuka

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper we investigate how metabolic network structure affects any coordination between transcript and metabolite profiles. To achieve this goal we conduct two complementary analyses focused on the metabolic response to stress. First, we investigate the general size of any relationship between metabolic network gene expression and metabolite profiles. We find that strongly correlated transcript-metabolite profiles are sustained over surprisingly long network distances away from any target metabolite. Secondly, we employ a novel pathway mining method to investigate the structure of this transcript-metabolite relationship. The objective of this method is to identify a minimum set of metabolites which are the target of significantly correlated gene expression pathways. The results reveal that in general, a global regulation signature targeting a small number of metabolites is responsible for a large scale metabolic response. However, our method also reveals pathway specific effects that can degrade this global regulation signature and complicates the observed coordination between transcript-metabolite profiles.

Indexed as

Gene Expression ProfilingMetabolic Networks and PathwaysMetabolomeOxidative StressComputational BiologyEscherichia coliEscherichia coli ProteinsGene Expression Regulation, BacterialTemperatureEscherichia coli Proteins

Identifiers

PMID22355360
PMCPMC3280297

What OpenQuestion holds

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Registered trials

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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.