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Towards a System Level Understanding of Non-Model Organisms Sampled from the Environment: A Network Biology Approach

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      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.

      Author Summary

      Understanding how living organisms adapt to changes in their natural habitats is of paramount importance particularly in respect to environmental stressors, such as pollution or climate. Computational models integrating the multi-level molecular responses with organism physiology are likely to be indispensable tools to address this challenge. However, because of the difficulties in acquiring and integrating data from non-model species and because of the intrinsic complexity of field studies, such an approach has not yet been attempted. Here we describe the first example of a global network reconstruction linking transcriptional and metabolic responses to physiology in the flatfish, European flounder, a species currently used to monitor coastal waters around Northern Europe. The model we developed has revealed a remarkable similarity between network modules predictive of chemical exposure in the environment and pathways involved in relevant aspects of human pathophysiology. Generally, the approach we have pioneered has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.

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            Author and article information

            [1 ]School of Biosciences, The University of Birmingham, Birmingham, United Kingdom
            [2 ]Institute of Aquaculture, University of Stirling, Stirling, Scotland, United Kingdom
            [3 ]Yantai Institute of Coastal Zone Research, Academy of Sciences, Yantai, PR. China
            [4 ]Cefas, Weymouth Laboratory, Weymouth, Dorset, United Kingdom
            [5 ]Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
            [6 ]Department of Animal Science, Iowa State University, Ames, Iowa, United States of America
            University of Zurich and Swiss Institute of Bioinformatics, Switzerland
            Author notes

            Conceived and designed the experiments: MJL SGG MRV KJC FF. Performed the experiments: TDW AMD HW CM BPL GDS IK. Analyzed the data: TDW NT KLB OH JKA JBT JMH. Wrote the paper: TDW FF.

            Role: Editor
            PLoS Comput Biol
            PLoS Computational Biology
            Public Library of Science (San Francisco, USA )
            August 2011
            August 2011
            25 August 2011
            : 7
            : 8
            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.
            Pages: 20
            Research Article
            Computational Biology
            Regulatory Networks
            Systems Biology
            Environmental Protection
            Functional Genomics
            Genome Expression Analysis
            Marine Biology
            Marine Monitoring
            Predictive Toxicology
            Earth Sciences
            Environmental Sciences

            Quantitative & Systems biology


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