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Behavioral Metabolomics

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.

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Behavioral Metabolomics

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  1. Behavioral Metabolomics October 21st, 2010 By Joseph L McClay

  2. 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

  3. Hierarchies of Order Oltavi & Barabasi (2002) Science 298, p763

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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)

  9. Technologies – characterizing the mixture Mass Spectrometry • Nuclear Magnetic Resonance

  10. 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)

  11. 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, admissions to treatment programs have increased dramatically since the mid 1990s

  12. 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

  13. 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

  14. Overview schematic

  15. Behavioral pharmacology

  16. 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

  17. Acute metabolic effects

  18. Chronic effects – part 1

  19. Chronic effects – Part II

  20. 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

  21. Results – alternative parameterization

  22. Behavioral Metabolomics • Sensitization: • 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

  23. Factor analysis 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

  24. Results – metabolomics analysis of sensitization In this analysis, we are correlating individual differences in the levels of specific metabolites with individual differences in sensitization to methamphetamine.

  25. 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

  26. 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)

  27. 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

  28. Acknowledgements CBRPM, School of Pharmacy Edwin van den Oord Daniel Adkins Shaunna Clark Renan Souza Department of Pharmacology and Toxicology Patrick Beardsley Rob Vann Sarah Vunck Angela Batman (now at Pfizer UK) Funding: NIDA http://www.pharmacy.vcu.edu/biomarker/

  29. Databases • What does my metabolite do? • Choline Phosphate • Gamma-glutamyl alanine • Search databases: • Reactome • KEGG – Kyoto Encyclopedia of Genes and Genomes • BioSystems @ NCBI

  30. Web sites • www.reactome.org • http://www.genome.jp/kegg/ • www.ncbi.nlm.nih.gov/biosystems/

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