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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). http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002126#; https://doi.org/10.1371/journal.pcbi.1002126

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.

Keywords
pollution; environment; Populations dynamics; Water Pollution; Computational Biology; Systems biology

Journal
PLoS Computational Biology: Volume 7, Issue Issue 8, Article e1002126

• WilliamsPLOSCompBiol2011storre.pdf