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Behavioral Metabolomics. October 21 st , 2010 By Joseph L McClay. Presentation Overview. The “omics” philosophy Metabolomics as an assay of biological function Technologies (MS, NMR) Neurochemical metabolomics in rodents Study of methamphetamine Summary Bioinformatics tools example. - PowerPoint PPT Presentation
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Center for Biomarker Research and Personalized Medicine
Behavioral Metabolomics
October 21st, 2010By Joseph L McClay
Center for
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Research
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Medicine
Presentation Overview
• The “omics” philosophy• Metabolomics as an assay of
biological function• Technologies (MS, NMR)• Neurochemical metabolomics in
rodents• Study of methamphetamine
• Summary • Bioinformatics tools example
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Hierarchies of Order
Oltavi & Barabasi (2002) Science 298, p763
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• Many omics variants:• DNA sequence
• GWAS
• Whole genome sequencing
• Epigenetics• Whole genome methylation
• Gene expression (RNA)• Expression arrays
• microRNA arrays
• Protein• Proteomics
• Metabolites• Metabolomics
• Metabonomics
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The omics “principle”
• Assume you know nothing• Try to measure everything
• Is this a hypothesis-driven approach to science?
• Advantages – new discovery• Disadvantages – false positives, cost
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Law of the Instrument
• “If you have a hammer, everything looks like a nail”
• Omics approaches are very technology driven
• Technology = assays + informatics• Pushing the limits of technology is
extraordinarily expensive• However, there is the opportunity to break
open the complexity of biology
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Metabolomics
• Biochemistry on a large scale
• Examination of all endogenous metabolites (under 1500Da) in a sample
• Several thousand in human metabolome
• Ultimate indicators of biological system response
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Possible applications
• Comparison of tissue-specific metabolic profiles
• Drug effects on metabolism• Personalized medicine
• Developmental effects• Metabolic disturbances in disease /
pathogenesis• In combination with other omics
• For example, GWAS to map quantitative trait loci for individual differences in metabolite leves (mQTLs)
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Technologies – characterizing the mixture
Nuclear Magnetic Resonance Mass Spectrometry
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What are the data like?
• Input is a complex mixture of metabolites
• Integrate across spectrum / identify specific compounds
• Examination of relative peak heights / integrals or compound levels
• So, quantitative in nature (more akin to gene expression than genotype data)
Brain mass spec (Woods et al 2006)
Urinary 1H NMR (McClay et al 2010)
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Methamphetamine
• percentage of past-year MA use among persons 12+ has remained relatively stable
• Estimates ranging from 0.7% in 2002 to 0.6% in 2007
However, admissionsto treatment programshave increased dramatically since themid 1990s
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Rationale for a metabolomics study of methamphetamine in mice
• Behavioral studies and animal models are well worked out
• While some gene expression and other studies have been carried out, to date no metabolomics study
• Returning to the “omics” principles outlined earlier, do we really know all the effects of meth?
• If we can better characterize the effects, we can perhaps see pathways that could mediate the addiction process
• Find candidate compounds for in vivo imaging
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Study design• 8 inbred strains of mice, chosen for
maximum genetic variation• 48 mice per strain• Acute vehicle, 1, 3 or 10mg/kg meth• Chronic vehicle or 3mg/kg meth for 5 days• 1 hour behavioral assessments of
locomotor activity using automated boxes• Followed by sacrifice, brain excision and
freezing in liquid nitrogen• Shipment to Metabolon, RTP, NC• GC and LC mass spectrometry
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Overview schematic
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010
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Acute Behavioral Effects, Significant Outcomes
Behavioral pharmacology
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Pharmacometabolomics
• Acute vehicle, acute 3mg/kg meth and chronic 3mg/kg meth for 5 days
• 18 mice per strain, 8 strains total• Test for differences in metabolite
levels between groups• 300 metabolites in total were
identified by Metabolon and tested• False Discovery Rate control
necessary because of large number of tests
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Acute metabolic effects
Compound Beta p-value
q-
value Primary pathway Class
fructose -0.282 6.3E-06 0.001 Glycolysis, gluconeogenesis Carbohydrate
lactate 0.305 4.4E-05 0.003 pyruvate metabolism Carbohydrate
malate 0.223 0.0001 0.006 Krebs cycle Energy
2-hydroxyglutarate 0.129 0.0001 0.007
succinate 0.191 0.0003 0.015 Krebs cycle Energy
tryptophan 0.156 0.0008 0.025 Tryptophan metabolism Amino acid
fumarate 0.118 0.0009 0.027 Krebs cycle Energy
linoleate (18:2n6) 0.272 0.0027 0.059 Long chain fatty acid Lipid
citrate 0.078 0.0043 0.081 Krebs cycle Energy
sorbitol -0.224 0.0045 0.081 starch, and sucrose metabolism Carbohydrate
glycerophosphorylcholine -0.081 0.0052 0.081 Glycerolipid metabolism Lipid
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Chronic effects – part 1
Compound Beta p-value q-value Primary pathway Classlactate 0.405 1.4E-10 1.3E-07 Glycolysis, gluconeogenesis Carbohydrate
malate 0.305 3.6E-10 1.6E-07 Krebs cycle Energy
citrate 0.140 1.3E-09 3.7E-07 Krebs cycle Energy
2-hydroxyglutarate 0.167 3.8E-09 8.3E-07tryptophan 0.227 6.3E-09 1.1E-06 Tryptophan metabolism Amino acid
alanine 0.356 1.8E-06 0.0003 Alanine metabolism Amino acid
2-aminoadipate 0.156 7.0E-06 0.0007 Lysine metabolism Amino acid
3-hydroxybutyrate 0.320 4.5E-05 0.003 Ketone bodies Lipid
urea -0.149 8.4E-05 0.005 Urea, arginine metabolism Amino acid
maltotriose 0.773 0.0001 0.007 Sucrose metabolism Carbohydrate
choline phosphate 0.102 0.0003 0.013 Glycerolipid metabolism Lipid
gamma-aminobutyrate 0.132 0.0003 0.015 Glutamate metabolism Amino acid
ergothioneine 0.159 0.0004 0.015glycerophosphorylcholine -0.085 0.0005 0.019 Glycerolipid metabolism Lipid
fructose -0.177 0.0005 0.019 Sucrose metabolism Carbohydrate
gamma-glutamyl alanine 0.227 0.0006 0.022 Gamma-glutamyl
serine 0.067 0.0008 0.025 Glycine, serine and threonine Amino acid
glucose -0.177 0.0012 0.035 Glycolysis, gluconeogenesis Carbohydrate
ribose 0.299 0.0015 0.040 Nucleotide sugars Carbohydrate
glycerol 3-phosphate -0.085 0.0016 0.041 Glycerolipid metabolism Lipid
succinate 0.141 0.0018 0.044 Krebs cycle Energy
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Chronic effects – Part II
Compound Beta p-value q-value Primary pathway Class
uridine 0.283 0.002 0.058 Pyrimidine metabolism Nucleotide
adenosine 5'diphosphoribose 1.356 0.003 0.059 Nicotinamide metabolismCofactors and vitamins
nicotinamide 1.120 0.003 0.059 Nicotinamide metabolismCofactors and vitamins
guanosine 5'- monophosphate 0.822 0.003 0.065 Purine metabolism Nucleotide
glutamine -0.076 0.003 0.065 Glutamate metabolism Amino acid
dehydroascorbate 0.648 0.004 0.071 Ascorbate metabolismCofactors and vitamins
ribulose 5-phosphate 1.210 0.004 0.077 Nucleotide sugars Carbohydrate
phenylalanine 0.091 0.004 0.081 Tyrosine metabolism Amino acid
maltose 0.692 0.005 0.081 Sucrose metabolism Carbohydrate
cysteine 1.277 0.005 0.081 Cysteine metabolism Amino acid
butyrylcarnitine -0.127 0.005 0.081 Fatty acid metabolism Lipid
pipecolate -0.118 0.006 0.086 Lysine metabolism Amino acid
inosine 0.848 0.006 0.095 Purine metabolism Nucleotide
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Alternate parameterization•Between group comparison shows the extensive metabolic disruption due to meth administration•However, does not disaggregate acute from chronic meth effects. •For this we need a 2nd parameterization:
Intercept (a) represents the “simplest” condition--acute vehicle (av). Parameter 1 (d1) captures marginal effect of acute meth over acute vehicle. Parameter 2 (d2) captures marginal effect of chronic vehicle injection over “just” acute meth. Parameter 3 (d3) captures marginal effect of chronic meth over chronic vehicle injection + acute meth. We include with a random intercept to account for clustering within strain (u0).
Metabolite level = a + b1*d1 + b2*d2 + b3*d3 + u0 + e
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Results – alternative parameterization
parm Compound Beta p-value q-value Primary pathway Class
d1 2-hydroxyglutarate 0.129 1.20E-04 0.048 Citric acid cycle energy
d3 ergothioneine 0.19 3.00E-04 0.069 Dietary
d3 choline phosphate 0.118 3.50E-04 0.069 Ceramide signaling phospholipid
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Behavioral MetabolomicsSensitization:•Essentially is an increase in response to the same dose of drug after repeated exposure•We are measuring locomotor activity•In locomotor terms, sensitization means that mice will move around more after their dose of drug on the last day, as compared to the first day•However, the automated boxes measure locomotor activity in several ways•Around 20 locomotor activity variables are collected
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Factor analysis
Factor Variance Difference Proportion CumulativeFactor1 6.40 2.04 0.36 0.36Factor2 4.36 2.13 0.25 0.61Factor3 2.23 0.57 0.13 0.74Factor4 1.66 0.24 0.09 0.83Factor5 1.42 0.60 0.08 0.91Factor6 0.82 0.53 0.05 0.96Factor7 0.29 0.10 0.02 0.97Factor8 0.18 0.01 0.01 0.98Factor9 0.17 0.11 0.01 0.99Factor10 0.06 0.01 0.00 1.00Factor11 0.06 0.05 0.00 1.00Factor12 0.01 0.00 0.00 1.00
Variable Factor1 Factor2 Factor3 Factor4-------------+--------------------------------------------------------------------------------rtime_sens~p 0.08 0.34 0.04 -0.08rmovno_sen~p -0.01 0.98 0.05 0.00ractv_sens~p -0.01 0.96 0.04 -0.01ctrtime_se~p -0.06 -0.06 -0.99 0.05ctrdist_se~p 0.84 0.03 -0.23 0.17mrgtime_se~p 0.07 0.06 0.99 -0.05mrgdist_se~p 0.89 0.01 0.34 0.00strtime_se~p 0.58 -0.19 -0.19 0.33strno_sens~p 0.33 -0.09 -0.03 0.80strcnt_sen~p 0.83 -0.10 -0.06 0.19vtime_sens~p 0.03 0.67 0.02 -0.10vmovno_sen~p 0.02 0.97 0.06 -0.03vactv_sens~p -0.02 0.95 0.02 -0.06restime_se~p -0.98 -0.02 0.00 -0.09movtime_se~p 0.98 0.02 0.00 0.09movno_sens~p 0.29 -0.02 -0.12 0.82totdist_se~p 0.97 0.02 0.16 0.07hactv_sens~p 0.90 -0.02 0.05 0.34
4 factors: horizontal/total movement, vertical movement, center/margin time, stereotypy
Create BLUPs for each animal for sensitization, i.e. increase in horizontal movement over course of study
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Results – metabolomics analysis of sensitization
compound beta p-value q-value primary pathway class
malonylcarnitine 0.817 7.74E-05 0.014 Amino acid conj Lipid / Energy
serine -0.084 1.54E-04 0.014 Serine threonine met Amino acid
homocarnosine -0.155 1.56E-04 0.014 Amino acid conj Peptide
ergothioneine 0.2 2.64E-03 0.177 Unknown N/A
histamine 0.777 5.00E-03 0.238 Histidine metabolism Amino acid
NADH 0.167 6.20E-03 0.238Nicotinamide /
energyCofactors and vitamins
In this analysis, we are correlating individual differences in the levels of specific metabolites with individual differences in sensitization tomethamphetamine.
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Summary
• Metabolomics analysis can yield insights into the metabolic sequelae of drug administration
• In this study, we observed extensive and dramatic alterations to neurochemistry following meth administration
• Among specific findings were changes to glutamine / alanine-related metabolites and choline phosphate following chronic adminsitration
• Associations with sensitization implicated histamine and homocarnosine
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Summary (contd)• Previous studies have implicated GABA,
histamine, phospholipids etc in relation to stimulant drug abuse / administration
• This first attempt at neurochemical / behavioral metabolomics appears promising
• Much additional work to be done • Application to other drug / behavior
pairings (e.g. PPI and antipsychotics)
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Many statistical development opportunities
For example, identify subsets of metabolites whose concentrations are always coupled.
Use that to define test statistic:– Multivariate– Eliminates some of
the dynamics
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Acknowledgements
http://www.pharmacy.vcu.edu/biomarker/
CBRPM, School of PharmacyEdwin van den OordDaniel AdkinsShaunna ClarkRenan Souza
Department of Pharmacology and ToxicologyPatrick BeardsleyRob VannSarah VunckAngela Batman (now at Pfizer UK)
Funding: NIDA
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Databases
• What does my metabolite do?• Choline Phosphate• Gamma-glutamyl alanine
• Search databases:• Reactome• KEGG – Kyoto Encyclopedia of Genes
and Genomes• BioSystems @ NCBI
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Web sites
• www.reactome.org• http://www.genome.jp/kegg/• www.ncbi.nlm.nih.gov/biosystems/