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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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          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.

          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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          Most cited references65

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            A rapid method of total lipid extraction and purification.

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              DAVID: Database for Annotation, Visualization, and Integrated Discovery.

              Functional annotation of differentially expressed genes is a necessary and critical step in the analysis of microarray data. The distributed nature of biological knowledge frequently requires researchers to navigate through numerous web-accessible databases gathering information one gene at a time. A more judicious approach is to provide query-based access to an integrated database that disseminates biologically rich information across large datasets and displays graphic summaries of functional information. Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid.nih.gov) addresses this need via four web-based analysis modules: 1) Annotation Tool - rapidly appends descriptive data from several public databases to lists of genes; 2) GoCharts - assigns genes to Gene Ontology functional categories based on user selected classifications and term specificity level; 3) KeggCharts - assigns genes to KEGG metabolic processes and enables users to view genes in the context of biochemical pathway maps; and 4) DomainCharts - groups genes according to PFAM conserved protein domains. Analysis results and graphical displays remain dynamically linked to primary data and external data repositories, thereby furnishing in-depth as well as broad-based data coverage. The functionality provided by DAVID accelerates the analysis of genome-scale datasets by facilitating the transition from data collection to biological meaning.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                August 2011
                August 2011
                25 August 2011
                : 7
                : 8
                : e1002126
                Affiliations
                [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.

                Article
                PCOMPBIOL-D-11-00032
                10.1371/journal.pcbi.1002126
                3161900
                21901081
                a0123ffa-d36c-4239-b65b-df39a4225e49
                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.
                History
                : 7 January 2011
                : 26 May 2011
                Page count
                Pages: 20
                Categories
                Research Article
                Biology
                Computational Biology
                Genomics
                Microarrays
                Regulatory Networks
                Systems Biology
                Ecology
                Environmental Protection
                Genomics
                Functional Genomics
                Genome Expression Analysis
                Marine Biology
                Marine Monitoring
                Toxicology
                Predictive Toxicology
                Earth Sciences
                Environmental Sciences

                Quantitative & Systems biology
                Quantitative & Systems biology

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