Evidence map›Paper›PMID 25636852›Full record

ArticleApplied and environmental microbiology2015

Gas chromatography-mass spectrometry-based metabolite profiling of Salmonella enterica serovar Typhimurium differentiates between biofilm and planktonic phenotypes.

Hui San Wong, Garth L Maker, Robert D Trengove, Ryan M O'Handley

Open access · bronzeAbstract read
In one paragraph

Article in Applied and environmental microbiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

16 citing papers in PubMed, 25 citations in OpenAlex.

  1. Review
  2. Article
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  5. Frontiers in cellular and infection microbiology · 2024
    Article
  6. Review
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  8. Review
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  13. Article
  14. Frontiers in microbiology · 2017
    Article
  15. 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 at 2 institutions in 1 country.

Hui San WongThe University of Adelaide, School of Animal and Veterinary Science, Adelaide, South Australia, Australia Murdoch University, School of Veterinary and Life Sciences, Perth, Western Australia, Australia h.wong@mail.com.
Garth L MakerMurdoch University, School of Veterinary and Life Sciences, Perth, Western Australia, Australia Murdoch University, Metabolomics Australia, Perth, Western Australia, Australia.
Robert D TrengoveMurdoch University, Metabolomics Australia, Perth, Western Australia, Australia.
Ryan M O'HandleyThe University of Adelaide, School of Animal and Veterinary Science, Adelaide, South Australia, Australia.
Murdoch University · AUUniversity of Adelaide · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to utilize gas chromatography coupled with mass spectrometry (GC-MS) to compare and identify patterns of biochemical change between Salmonella cells grown in planktonic and biofilm phases and Salmonella biofilms of different ages. Our results showed a clear separation between planktonic and biofilm modes of growth. The majority of metabolites contributing to variance between planktonic and biofilm supernatants were identified as amino acids, including alanine, glutamic acid, glycine, and ornithine. Metabolites contributing to variance in intracellular profiles were identified as succinic acid, putrescine, pyroglutamic acid, and N-acetylglutamic acid. Principal-component analysis revealed no significant differences between the various ages of intracellular profiles, which would otherwise allow differentiation of biofilm cells on the basis of age. A shifting pattern across the score plot was illustrated when analyzing extracellular metabolites sampled from different days of biofilm growth, and amino acids were again identified as the metabolites contributing most to variance. An understanding of biofilm-specific metabolic responses to perturbations, especially antibiotics, can lead to the identification of novel drug targets and potential therapies for combating biofilm-associated diseases. We concluded that under the conditions of this study, GC-MS can be successfully applied as a high-throughput technique for "bottom-up" metabolomic biofilm research.

Indexed as

BiofilmsGas Chromatography-Mass SpectrometryMetabolomePhenotypePlanktonSalmonella typhimuriumTime Factors

Identifiers

PMID25636852
PMCPMC4375307
OpenAlexW1978887025

What OpenQuestion holds

Textmetadata
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.