Experimental and computational approaches for deep metabolome annotation with application to the ecotoxicological model organism Daphnia magna

BACKGROUND: Comprehensively characterising the metabolomes of model organisms with high coverage and confidence is a critical step towards interpreting the metabolic basis of human and environmental health, yet there are formidable challenges involved in annotating metabolomes. A wide range of genotypes and phenotypes should be sampled with multiple complementary analytical approaches to cover the large and dynamic biochemical space they exhibit. In addition, multiple computational tools and approaches are required to annotate the metabolites from raw analytical data. RESULTS: To address this, we developed the Deep Metabolome Annotation (DMA) workflow. Applied to the ecological sentinel species, Daphnia magna, a pooled sample comprising ten distinct strains exposed to both normal and stressed environmental conditions was extracted and systematically physicochemically separated via solid-phase extraction, liquid- and gas-chromatography prior to extensive multiple-stage mass spectrometric fragmentation, generating more than 8,000 raw data files, and supplemented by nuclear magnetic resonance spectroscopy. An extensive Galaxy-based computational approach was built to analyse these data, comprising over 30 tools. The overall DMA efforts resulted in 8,181 annotated polar metabolites and lipids in D. magna, with the raw and processed data, tools and annotations disseminated freely via public data repositories and a custom web-based interface to maximise reusability. CONCLUSIONS: The DMA workflow has generated one of the largest metabolome annotation datasets for any non-human model organism and provides the first in-depth characterisation of the D. magna metabolome, serving as both a resource and a valuable catalyst for future deep metabolome annotation studies of other model organisms.