Evidence map›Paper›PMID 24153255›Full record

ArticleBMC systems biology2013

A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions.

Helen L Kotze, Emily G Armitage, Kieran J Sharkey, James W Allwood, Warwick B Dunn, Kaye J Williams, Royston Goodacre

Open access · diamondAbstract read
In one paragraph

Article in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed
3.6field-weighted citation impact, top 6% 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

44 citing papers in PubMed, 77 citations in OpenAlex.

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  7. From Division to Death: Metabolomic Analysis ofPlants (Basel, Switzerland) · 2024
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  10. Correlations for untargeted GC × GC-HRTOF-MS metabolomics of colorectal cancer.Metabolomics : Official journal of the Metabolomic Society · 2023
    Article
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  13. Review
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  15. Article
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  17. Mycorrhiza-Induced Alterations in Metabolome ofPlants (Basel, Switzerland) · 2021
    Article
  18. Review
  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

7 authors at 3 institutions in 1 country.

Helen L Kotze
Emily G Armitage
Kieran J Sharkey
James W Allwood
Warwick B Dunn
Kaye J Williams
Royston GoodacreSchool of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK. roy.goodacre@manchester.ac.uk.
University of Manchester · GBUniversity of Birmingham · GBUniversity of Liverpool · GB

Funding

Biotechnology and Biological Sciences Research Council BBC0082191Biotechnology and Biological Sciences Research Council BB/C519038/1Medical Research Council MC_QA137293
6 · The paper itself

Abstract

backgroundMetabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments.

resultsCorrelation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia before. Lactate fermentation is one of the key themes discussed in the field of hypoxia; however, malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate, which are connected by a single pathway, may provide a more significant marker of hypoxia in cancer.

conclusionsMetabolic networks generated for each cell line were compared to identify conserved metabolite pathway responses to low oxygen environments. Furthermore, we believe this methodology will have general application within metabolomics.

Indexed as

Metabolic Networks and PathwaysHCT116 CellsHumansMetabolomicsOxygenOxygen

Identifiers

PMID24153255
PMCPMC3874763
OpenAlexW2171681747

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.