Evidence map›Paper›PMID 21901081›Full record

ArticlePLoS computational biology2011

Towards a system level understanding of non-model organisms sampled from the environment: a network biology approach.

Tim D Williams, Nil Turan, Amer M Diab, Huifeng Wu, Carolynn Mackenzie, Katie L Bartie, Olga Hrydziuszko, Brett P Lyons, Grant D Stentiford, John M Herbert and 8 more

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
5.1field-weighted citation impact, top 4% 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

20 citing papers in PubMed, 89 citations in OpenAlex.

  1. Article
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  9. Tissue-Based Mapping of the Fathead Minnow (Frontiers in endocrinology · 2018
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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

18 authors at 4 institutions in 3 countries.

Tim D WilliamsSchool of Biosciences, The University of Birmingham, Birmingham, United Kingdom.
Nil Turan
Amer M Diab
Huifeng Wu
Carolynn Mackenzie
Katie L Bartie
Olga Hrydziuszko
Brett P Lyons
Grant D Stentiford
John M Herbert
Joseph K Abraham
Ioanna Katsiadaki
Michael J Leaver
John B Taggart
Stephen G George
Mark R Viant
Kevin J Chipman
Francesco Falciani
University of Birmingham · GBUniversity of Stirling · GBCentre for Environment, Fisheries and Aquaculture Science · GBCase Western Reserve University · US

Funding

Biotechnology and Biological Sciences Research Council 6/JIF 13209Medical Research Council G.4500017
6 · The paper itself

Abstract

The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.

Indexed as

EcosystemModels, BiologicalAnalysis of VarianceAnimalsCluster AnalysisEnvironmental ExposureFlounderGene Expression RegulationGene Regulatory NetworksGeologic SedimentsHumansLiverMetabolic Networks and PathwaysMetabolomicsSystems BiologyTranscriptome

Identifiers

PMID21901081
PMCPMC3161900
OpenAlexW2163602934

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.