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Toxicological studies in sentinel organisms frequently use biomarkers to assess biological effect. Development of "omic" technologies has enhanced biomarker discovery at the molecular level, providing signatures unique to toxicant mode-of-action (MOA). However, these signatures often lack relevance to organismal responses, such as growth or reproduction, limiting their value for environmental monitoring. Our primary objective was to discover metabolic signatures in chemically exposed organisms that can predict physiological toxicity. Marine mussels (Mytilus edulis) were exposed for 7 days to 12 and 50 microg/l copper and 50 and 350 microg/l pentachlorophenol (PCP), toxicants with unique MOAs. Physiological responses comprised an established measure of organism energetic fitness, scope for growth (SFG). Metabolic fingerprints were measured in the same individuals using nuclear magnetic resonance-based metabolomics. Metabolic signatures predictive of SFG were sought using optimal variable selection strategies and multivariate regression and then tested upon independently field-sampled mussels from rural and industrialized sites. Copper and PCP induced rational metabolic and physiological changes. Measured and predicted SFG were highly correlated for copper (r(2) = 0.55, P = 2.82 x 10(-7)) and PCP (r(2) = 0.66, P = 3.20 x 10(-6)). Predictive metabolites included methionine and arginine/phosphoarginine for copper and allantoin, valine, and methionine for PCP. When tested on field-sampled animals, metabolic signatures predicted considerably reduced fitness of mussels from the contaminated (SFG = 6.0 J/h/g) versus rural (SFG = 15.2 J/h/g) site. We report the first successful discovery of metabolic signatures in chemically exposed environmental organisms that inform on molecular MOA and that can predict physiological toxicity. This could have far-reaching implications for monitoring impacts on environmental health.

An important area of research in systems biology involves the analysis and integration of genome-wide functional datasets. In this context, a major goal is the identification of a putative molecular network controlling physiological response from experimental data. With very fragmentary mechanistic information, this is a challenging task. A number of methods have been developed, each one with the potential to address an aspect of the problem. Here, we review some of the most widely used methodologies and report new results in support of the usefulness of modularization and other modelling techniques in identifying components of the molecular networks that are predictive of physiological response. We also discuss how system identification in biology could be approached, using a combination of methodologies that aim to reconstruct the relationship between molecular pathways and physiology at different levels of the organizational complexity of the molecular network.