Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/3737
Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: Towards a System Level Understanding of Non-Model Organisms Sampled from the Environment: A Network Biology Approach
Authors: Williams, Tim D
Turan, Nil
Diab, Amer
Wu, Huifeng
Mackenzie, Carolynn
Bartie, Katie L
Hrydziuszko, Olga
Lyons, Brett P
Stentiford, Grant D
Herbert, John M
Abraham, Joseph K
Katsiadaki, Ioanna
Leaver, Michael
Taggart, John
George, Stephen
Viant, Mark R
Chipman, James Kevin
Falciani, Francesco
Contact Email: m.j.leaver@stir.ac.uk
Keywords: pollution
environment
Issue Date: Aug-2011
Publisher: Public Library of Science
Citation: Williams TD, Turan N, Diab A, Wu H, Mackenzie C, Bartie KL, Hrydziuszko O, Lyons BP, Stentiford GD, Herbert JM, Abraham JK, Katsiadaki I, Leaver M, Taggart J, George S, Viant MR, Chipman JK & Falciani F (2011) Towards a System Level Understanding of Non-Model Organisms Sampled from the Environment: A Network Biology Approach, PLoS Computational Biology, 7 (Issue 8, Article e1002126).
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.
Type: Journal Article
URI: http://hdl.handle.net/1893/3737
URL: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002126#
DOI Link: http://dx.doi.org/10.1371/journal.pcbi.1002126
Rights: Published in PLoS Computational Biology by Public Library of Science. Citation: Williams TD, Turan N, Diab AM, Wu H, Mackenzie C, et al. (2011) Towards a System Level Understanding of Non-Model Organisms Sampled from the Environment: A Network Biology Approach. PLoS Comput Biol 7(8): e1002126. doi:10.1371/journal.pcbi.1002126. Copyright: © 2011 Williams et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons: http://creativecommons.org/licenses/by/2.5/
Affiliation: University of Birmingham
University of Birmingham
Aquaculture
University of Birmingham
University of Stirling
University of Stirling
University of Birmingham
Centre for Environment, Fisheries and Aquaculture Science (CEFAS)
Centre for Environment, Fisheries and Aquaculture Science (CEFAS)
University of Birmingham
Case Western Reserve University
Centre for Environment, Fisheries and Aquaculture Science (CEFAS)
Aquaculture
Aquaculture
Aquaculture
University of Birmingham
University of Birmingham
University of Birmingham

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