Metabolomics A New Tool in Molecular Toxicology

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1 Metabolomics A New Tool in Molecular Toxicology EU INFLAME Training, Birmingham 19 th January 2012 Mark Viant (Jinkang s co-supervisor)

2 Overview 1. Setting the scene from Wednesday s talks 2. What is metabolomics? General applications in toxicology 3. Example: discovery of novel biomarkers and novel molecular toxicity 4. Example: metabolic biomarkers can predict reproductive fitness

3 1. Setting the scene from Wednesday s talks

4 EU REACH - Regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals (2006) Strictest law to date regulating chemical substances: -considersimpacts on human health and environment Why was it enforced? - little safety information exists for >90% of ca. 100,000 chemicals on the market Challenges of toxicity testing (Hartung & Rovida, Nature, 2009): -existing test methods are crude, information-poor

5 EU WFD Water Framework Directive (2000) Central legislation on water quality: - commits EU member states to achieve good status of all water bodies by 2015 Methods for health assessment of water body: - measure the health of flora and fauna - BUT health of the plants and invertebrate animals is crudely determined from their composition & abundance in the water (i.e. which ones have not died) Assessments within EU REACH and EU WFD are largely based upon counting live vs. dead

Water flea (Daphnia magna) Alive or dead? 1 measurement 1.

nutrient enrichment, pollutant?) 3. No mechanistic understanding 4.

Cannot build predictive models 21 st century high throughput biology: >30,000

6 Water flea (Daphnia magna) Alive or dead? 1 measurement 1. No early indication of problem 2. No information on cause of death (e.g. nutrient enrichment, pollutant?) 3. No mechanistic understanding 4. Cannot extrapolate to other species 5. Cannot build predictive models 21 st century high throughput biology: >30,000 measurements 1. Early warning indicator, sub-lethal 2. Molecular fingerprint diagnoses cause of stress 3. Mechanistic understanding 4. Extrapolate to other species 5. Build predictive models - prognosis

7 2. What is metabolomics? What roles can it play?

8 Metabolomics is the study of metabolism

9 Toolset 1 Bioanalytical chemistry Mass spectrometry NMR spectrometry

10 Toolset 2 Bioinformatics / data mining

Primary applications of metabolomics: Climate change Ocean

biological (metabolic) responses to stressors Discover

Potential to link molecular responses to whole organism

11 Primary applications of metabolomics: Climate change Ocean acidification Environmental pollution Characterising biological (metabolic) responses to stressors Discover molecular mechanisms of toxicity Discover novel biomarkers Potential to link molecular responses to whole organism physiology Potential to predict ecologically relevant effects (survival, growth, reproduction)

12 3. Example: discovery of novel biomarkers and novel molecular toxicity

13 Aim of study To investigate the molecular mechanism(s) of toxicity of commercial zinc oxide nanoparticles in Daphnia magna TEM image of ZnO NPs A B bulk ZnO

14 Experimental design ZnO NPs 0.03, 0.1, 0.3, 1.0 ppm Zn 2+ control Neonatal daphnids 0.1, 0.3 ppm bulk ZnO control 0.3 ppm untreated control Whole organism extracts a FT-ICR mass spectrometry b 48-hr exposures a Wu et al, (2008) Anal Biochem 372, Data mining c b Southam et al, (2007) Anal Chem 79, c Payne et al, (2009) J Amer Soc Mass Spectrom 20,

15 FT-ICR mass spectrum of the polar extracts of D. magna Each spectrum: >4000 signals from low molecular weight metabolites Entire dataset: 80 spectra (n=10 replicates from each of 8 groups) Aim: data mine these >320,000 signals to investigate metabolic responses to four ZnO NP concs, two Zn 2+ concs, ZnO bulk, untreated control

metabolic effect along vertical axis ZnO NP specific

16 Multivariate statistical analysis of metabolic data PLS discriminant analysis scores plot Zn 2+ specific metabolic effect along vertical axis ZnO NP specific metabolic effect along horizontal axis untreated control

17 Which metabolites are perturbed by ZnO NPs? Metabolite database searching Interpretation Patterns began of to mass emerge spectra from the thousands of numbers - accurate mass measurements - adduct patterns MS/MS and MS 3 fragmentation 1E20N_neg_277_CID #1-225 RT: AV: 225 NL: 1.48E3 T: ITMS - p ESI Full ms @cid28.00 [ ] - isotope patterns x5 x Further statistical analyses correlations between signals Relative Abundance E20N_neg_ _CID #1-185 RT: AV: 185 NL: 8.51 m/z T: ITMS - p ESI Full ms @cid @cid22.00 [ ] months later E20N_neg_ _CID_ #1-185 RT: AV: 185 NL: m/z 4.68 T: ITMS - p ESI Full ms @cid @cid28.00 [ ] m/z Relative Abundance Relative Abundance 1E20N_neg_ _CID #1-190 RT: AV: 190 NL: 7.36 T: ITMS - p ESI Full ms @cid @cid28.00 [ ] Relative Abundance m/z

18 Which metabolites are perturbed by ZnO NPs? Identified 29 endogenous aliphatic sulfates and sulfamates, spanning 4 families, that decreased concentration significantly upon exposure C x H y SO 4 family C x H y SO 5 family C x H y SO 6 family C x H y NSO 3 family

solubilisation of food Function 2: known kairomone chemical messengers that are

19 What are function(s) of these sulfated metabolites? Function 1: likely anionic gut surfactants to induce micelle formation and enhance the solubilisation of food Function 2: known kairomone chemical messengers that are excreted by Daphnia and sensed by algae, which then change their morphology Freshwater algae sense kairomones... change morphology

20 Hypothesised mechanism 2. Ingested, enter gut 1. ZnO nanoparticles in media 3. Suspect NPs bind the sulfated surfactants 4. Metabolomics measures a loss of sulfated metabolites 5. Ecological implications?

21 4. Example: metabolic biomarkers can predict reproductive fitness in Daphnia magna?

Experimental design Control N=8 Half-feed control Cadmium Low dose Cadmium Medium dose Cadmium High dose N=8 N=8 N=8 N=8 beakers Measure reproductive output of individual daphnids

22 Experimental design Control N=8 Half-feed control Cadmium Low dose Cadmium Medium dose Cadmium High dose N=8 N=8 N=8 N=8 beakers Measure reproductive output of individual daphnids over a 21-day period (standard test) On day 21, measure metabolism of same individuals using metabolomics Search for molecular markers that are predictive of reduced reproductive fitness

23 Data: reproductive output and metabolism Total no. of offspring Individual adult daphnids

24 Predictive mathematical model cadmium study Predicted reproductive output using metabolic biomarker signature r 2 (CV) = Measured reproductive output Discovered a metabolic biomarker signature that is highly predictive of reproductive fitness

Which metabolites predict reproductive fitness? 441.17365 Da 404.21238 175.02481 331.06003 241.18136 376.18080 432.24378 446.22306 243.19703 448.

25 Which metabolites predict reproductive fitness? Da Challenge of metabolite identification Ascorbic acid is 3 rd most predictive metabolite (confirmed by MS/MS) Ascorbic acid has long been associated with fertility Luck et al., Biol. Reprod. 52, (1995) We conclude that ascorbic acid is a leading nutrient in reproductive tissue functions [in teleost fish] Dabrowski & Ciereszko, Aquacult. Res. 32, (2001)

26 predict Reproductive fitness Built mathematical model that can predict the scope for growth of a mussel from its metabolic biomarker signature predict Tox. Sci. (2010) 115 (2): energetic fitness

27 Acknowledgements Daphnia (repro) Dr. Nadine Taylor Alex Gavin FT-ICR mass spectrometry Dr. Ulf Sommer Daphnia (nanoparticle) Prof. Charles Tyler (Exeter) Dr. Tamara Gallaway (Exeter) Dr. Julia Fabrega-Climent (Exeter)

Probing the water flea's biochemistry: a truly complex but high information content environmental sample

Probing the water flea's biochemistry: a truly complex but high information content environmental sample RSC Recent Advances in the Analysis of Complex Environmental Matrices 28th February 2013 Mark Viant,