metabolomics – a ‘new’ tool in molecular toxicology · metabolomics – a ‘new’ tool in...
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Metabolomics – A ‘New’ Tool in Molecular Toxicology
Mark Viant(Jinkang’s co-supervisor)
EU INFLAME Training, Birmingham19th January 2012
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
Overview
1. Setting the scene from Wednesday’s talks……
EU REACH - Regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals (2006)
Strictest law to date regulating chemical substances:- considers impacts 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
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. No early indication of problem2. No information on cause of death
(e.g. nutrient enrichment, pollutant?)3. No mechanistic understanding4. Cannot extrapolate to other species5. Cannot build predictive models
21st century high throughput biology:
>30,000 measurements1. Early warning indicator, sub-lethal2. Molecular fingerprint diagnoses
cause of stress3. Mechanistic understanding4. Extrapolate to other species5. Build predictive models - prognosis
2. What is metabolomics? What roles can it play?
Metabolomics is the study of metabolism
Mass spectrometry
NMR spectrometry
Toolset 1 – Bioanalytical chemistry
Toolset 2 – Bioinformatics / data mining
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)
3. Example: discovery of novel biomarkers and novel molecular ‘toxicity’
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
bulk ZnOcontrol
untreatedcontrol
Zn2+
control
ZnONPs
48-hr exposures
Experimental design
Neonatal daphnids
Whole organism
extracts a
a Wu et al, (2008) Anal Biochem 372, 204-212b Southam et al, (2007) Anal Chem 79, 4595-4602
c Payne et al, (2009) J Amer Soc Mass Spectrom 20, 1087-1095
FT-ICR mass spectrometry b
Data mining c
0.03, 0.1, 0.3, 1.0 ppm
0.1, 0.3 ppm
0.3 ppm
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 Zn2+ concs, ZnO bulk, untreated control
Multivariate statistical analysis of metabolic dataPLS discriminant analysis scores plot
ZnO NP specific metabolic effect along
horizontal axis
Zn2+ specific metabolic effect along vertical axis
untreatedcontrol
• Metabolite database searching…• Interpretation of mass spectra…
- accurate mass measurements- adduct patterns- isotope patterns…
• Further statistical analyses…- correlations between signals…
Which metabolites are perturbed by ZnO NPs?
Patterns began to emerge from the thousands of numbers…
1E20N_neg_277-137_CID #1-190 RT: 0.00-1.03 AV: 190 NL: 7.36T: ITMS - p ESI Full ms3 [email protected] [email protected] [50.00-300.00]
50 60 70 80 90 100 110 120 130 140 150 160m/z
0
20
40
60
80
100
Rel
ative
Abu
ndan
ce
137.00
80.00
81.08 109.0865.00 73.17 93.0083.08 97.08 101.42
1E20N_neg_277-165_CID #1-185 RT: 0.00-1.01 AV: 185 NL: 8.51T: ITMS - p ESI Full ms3 [email protected] [email protected] [50.00-300.00]
50 60 70 80 90 100 110 120 130 140 150 160 170 180m/z
0
20
40
60
80
100
Rel
ative
Abu
ndan
ce
165.08
81.00
97.00
85.0880.00 137.0073.00 121.08 164.33 165.7599.2564.92 95.83 107.17 146.7557.17 124.92
1E20N_neg_277-165_CID_100826160620 #1-185 RT: 0.00-1.01 AV: 185 NL: 4.68T: ITMS - p ESI Full ms3 [email protected] [email protected] [50.00-300.00]
50 60 70 80 90 100 110 120 130 140 150 160 170 180m/z
0
20
40
60
80
100
Rel
ative
Abu
ndan
ce
81.00
97.00
165.0080.00
85.08 137.0073.00 164.00107.00 121.0857.17 62.67 93.17 142.08125.17
1E20N_neg_277_CID #1-225 RT: 0.00-1.00 AV: 225 NL: 1.48E3T: ITMS - p ESI Full ms2 [email protected] [75.00-300.00]
80 100 120 140 160 180 200 220 240 260 280 300m/z
0
50
100
Rel
ative
Abun
danc
e 97.00
277.25137.08
165.08209.1799.0080.00 167.08 249.17233.25 259.17164.08147.00129.17 197.17110.00 294.92
x5 x5
2868
110138
180
28
68
84+85
28
28
57
MS/MS and MS3 fragmentation…
3 months later…
Which metabolites are perturbed by ZnO NPs?
Identified 29 endogenous aliphatic sulfates and sulfamates, spanning 4 families, that decreased concentration
significantly upon exposure
CxHySO4 family
CxHySO5 family
CxHySO6 family
CxHyNSO3 family
Function 1: likely anionic gut surfactants to induce micelle formation and enhance the solubilisation of food
What are function(s) of these sulfated metabolites?
Function 2: known kairomone chemical messengers that are excreted by Daphnia and sensed by algae, which then change
their morphology
sense kairomones...Freshwater algae change morphology
Hypothesised mechanism
1. ZnO nanoparticles in media
3. Suspect NPs bind the sulfated surfactants
2. Ingested, enter gut
4. Metabolomics measures a loss of
sulfated metabolites
5. Ecological implications?
4. Example: metabolic biomarkers can predict reproductive fitness in Daphnia magna?
• Measure reproductive output of individual daphnids over a 21-day period (standard test)
• On day 21, measure metabolism of same individualsusing metabolomics
• Search for molecular markers that are predictive of reduced reproductive fitness
Experimental design
CadmiumHigh dose
CadmiumLow dose
CadmiumMedium
dose
Half-feedcontrol
Control
N=8N=8 N=8 beakersN=8 N=8
Tota
l no.
of
offs
prin
g
Individual adult daphnids
Data: reproductive output and metabolism
Predictive mathematical model – cadmium study
r2 (CV) = 0.937
Measured reproductive outputPred
icte
d re
prod
ucti
ve o
utpu
t us
ing
met
abol
ic b
iom
arke
r si
gnat
ure
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
448.23874
…
Challenge of metabolite identification
Ascorbic acid is 3rd most predictive metabolite (confirmed by MS/MS)
“Ascorbic acid has long been associated with fertility”Luck et al., Biol. Reprod. 52, 262-266 (1995)
“We conclude that ascorbic acid is a leading nutrient in reproductive tissue functions [in teleost fish]”
Dabrowski & Ciereszko, Aquacult. Res. 32, 623-638 (2001)
predict
Reproductive fitness
Built mathematical model that can predict the ‘scope for growth’of a mussel from its metabolic biomarker signature
energetic fitness
predict
Tox. Sci. (2010) 115 (2): 369-378
Acknowledgements
Daphnia (repro)Dr. Nadine Taylor
Alex Gavin
FT-ICR mass spectrometryDr. Ulf Sommer
Daphnia (nanoparticle)Prof. Charles Tyler (Exeter)
Dr. Tamara Gallaway (Exeter)Dr. Julia Fabrega-Climent (Exeter)