### 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.
### introduction ###
Modelling the responses and compensatory adaptations of living organisms to a changing environment is extremely important both in terms of scientific understanding and for its potential impact on global health.
Although computational modelling of ecological systems has been utilised in ecotoxicology, the application of systems biology approaches to non-model organisms in general presents formidable difficulties, partly due to limited sequence information for environmentally relevant sentinel species.
Moreover, the number of samples and the depth of information available are often limited and there may be a lack of truly relevant physiological endpoints.
Thus, omics have proven effective in finding responses of aquatic organisms to model toxicants in laboratory-based experiments CITATION but the environment poses a greater challenge as anthropogenic contaminants are present as complex mixtures and responses will additionally be dependent upon natural life history traits and other environmental factors.
Relatively few omics studies have focussed upon the ecotoxicology of environmentally sampled fish CITATION CITATION.
Although we have previously shown CITATION, CITATION that expression of stress response genes could be used to distinguish fish from environmental sampling sites with different underlying contaminant burdens, this gave little insight to the health outcomes of these molecular differences.
In this context, identifying molecular mechanisms of compensatory and toxic responses from observational data, an approach that has been so successful in clinical studies and in laboratory model organisms, is highly challenging in field studies.
We addressed this challenge by developing a novel network inference strategy based on the integration of multi-level measurements of populations of fish exposed to a diverse spectrum of environmental pollutants.
This provides a useful model for a network biology approach generally applicable to non-model species and represents a breakthrough in the way we study the mechanisms whereby organisms respond to chemical exposure in the environment.
We directed our efforts towards modelling molecular networks representative of populations of the flatfish European flounder sampled from marine environments of North Western Europe, including locations significantly impacted by anthropogenic chemical contaminants.
The study integrated measurements representing a broad spectrum of samples characterized using transcriptomics, metabolomics, conventional biomarkers and analysis of chemicals in sediments from the sampling sites.
Previous studies have shown both anthropogenic contamination and higher prevalence of pre-neoplastic and neoplastic lesions in flounder from the Elbe estuary CITATION and from the Mersey and Tyne CITATION, together with elevated levels of hepatic DNA adducts at these sites CITATION.
Data integration was achieved by implementing a systems biology framework for network reconstruction, starting from cross-species mapping of sequence information to the integration of multi-level datasets within a framework for network inference CITATION and culminating in the identification of network modules predictive of physiological responses to chemical exposure, valuable for marine monitoring CITATION .
The networks we identified demonstrate a remarkable parallel between human liver carcinogenesis and environmental effects on fish liver as well as revealing potentially novel adaptation mechanisms.
The broader application of network biology approaches to other non-model species sampled from the environment is therefore likely to profoundly change our understanding of how living systems are likely to adapt to complex environments.
