Deep metabolome annotation of the freshwater model species, Daphnia magna

• Main Author: Jones, Martin Robert
• Published: University of Birmingham 2017
• Subjects: 500; Q Science (General)
• Online Access: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760300

In the 21st century - the era of big data science - chemical risk assessment procedures remain woefully dependent upon a suite of basic toxicological assays that offer little, if any, biochemical information pertaining to the underlying mechanism of toxicity. Metabolomics, defined as the holistic study of all naturally occurring, low molecular weight metabolites present within a biological system, holds huge potential as a tool to fill this knowledge gap, and thereby, to revolutionise the chemical risk assessment process through provision of rich molecular information . Owing to on-going challenges in the area of metabolite identification, however, which ultimately serves to impede derivation of biological knowledge from metabolomics data sets, the full potential of the metabolomics platform has yet to be realised in the context of (eco-)toxicological research. In this thesis, I present the experiments undertaken in establishing a bespoke bioanalytical workflow specifically designed and optimised to resolve this bottleneck. Ultimately, I demonstrate application of select components of this workflow in the characterisation of the metabolome of D. magna, a model organism for eco-toxicological research.