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NIDA Genetics: An Update

NIDA. NATIONAL INSTITUTE ON DRUG ABUSE. NIDA Genetics: An Update . Jonathan Pollock, PhD Branch Chief, Genetics and Molecular Neurobiology Research Branch DBNBR And Joni L Rutter, PhD Associate Director for Population and Applied Genetics DBNBR.

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NIDA Genetics: An Update

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  1. NIDA NATIONAL INSTITUTE ON DRUG ABUSE NIDA Genetics: An Update Jonathan Pollock, PhD Branch Chief, Genetics and Molecular Neurobiology Research Branch DBNBR And Joni L Rutter, PhD Associate Director for Population and Applied Genetics DBNBR

  2. Individual Differences in Vulnerability to Addiction • Not everyone who takes drugs becomes addicted • “has characteristics of chronic disease” Piazza Science 13 August 2004 • There are individual differences • Chronic exposure to drugs combined with a vulnerability phenotype leads to addiction • Genetics contributes to some of these individual differences

  3. Drug Addiction Is Influenced by Interactions of Genes and Environment Twin studies consistently show that there is a heritable component to drug abuse and addiction. Now we are able to examine genetic variants, or single nucleotide polymorphisms that contribute to addiction vulnerability

  4. Genome Chromosome Microband Region 50-100 Genes Single Gene (exons/introns) Single Nucleotide Polymorphism (SNPs) Goal: Identify Genetic Contributions to Drug Abuse Genome-wide Association Studies (GWAS) or Linkage Low-density Markers Fine- mapping Genotyping/ Resequencing

  5. NIDA Genetics:Identify Genetic Contributions to Drug Abuse • 2 Main Programmatic Themes • Trans-NIH initiatives • Leverage ongoing programs • Roadmap, GAIN, GEI, PGRN, etc • Trans-NIDA programs • Coordination among divisions • Multidisciplinary/Interdisciplinary • Data

  6. Human GeneticsTrans-NIH Involvement • Roadmap 1.0 • National Centers for Biomedical Computing (NCBC) - NIDA Lead, Karen Skinner • i2b2 (informatics for integrating biology and the bedside) • Zak Kohane, PI--evaluate smoking data in medical records • forge collaborations on genetics and bioinformatics of smoking • Roadmap 1.5 – Epigenomics • Nora Volkow, John Satterlee, Christine Colvis, Genevieve de-Almeida-Morris, Jonathan Pollock, David Shurtleff, Joni Rutter http://nihroadmap.nih.gov/epigenomics/

  7. Gene variant in CYP2B6 is associated with greater effectiveness of bupropion Human GeneticsTrans-NIH Involvement • Pharmacogenetics Research Network (PGRN) • A collaboration studying the effects of genes on people’s responses to medicines • Supported by 9 NIH ICs • Pharmacogenetics of Nicotine Addiction and Treatment (PNAT) – Neal Benowitz, PI • Addresses pharmacogenetics of nicotine addiction • NRTs • Bupropion • Varenicline • Rimonabant

  8. Human GeneticsTrans-NIH Involvement • Genetic Association Information Network (GAIN) • Public-private partnership led by Foundation for NIH • Genome-wide association study policy development for NIH-- Jonathan Pollock • Genes, Environment and Health Initiative (GEI) • Genetics Program- Pipeline for analyzing genetic variation in groups of patients with specific illnesses • Genetics of Addiction – Laura Bierut, PI • Exposure Biology Program- Measuring environmental exposures • NIDA RFA: Technologies measuring exposure to psychosocial stress and addictive substances--Kay Wanke, Kevin Conway

  9. NIDA Genetics:Identify Genetic Contributions to Drug Abuse • 2 Main Programmatic Themes • Trans-NIH initiatives • Leverage ongoing programs • Roadmap, GAIN, GEI, PGRN, etc • Trans-NIDA programs • Coordination among divisions • Multidisciplinary/Interdisciplinary • Data

  10. Model Organisms NIDA Genetics Workgroup Members NGCC members Christine Colvis Jim Glass Hal Gordon Mark Green Jag Khalsa Diane Lawrence Mary Ellen Michel Ivan Montoya Jonathan Pollock, Chair John Satterlee David Shurtleff Karen Skinner Mark Swieter Yonette Thomas George Uhl (IRP) Susan Volman Da-Yu Wu Scientific Goals & Services • Develop avenues of research through FOAs • Sponsor seminars to inform NIDA of new areas of genetics research • Develop staff knowledge in areas of genetics research NIDA Genetics: A Coordinated Effort Human NIDA Genetic Coordinating Committee (NGCC) Members Beth Babecki Kevin Conway Ahmed Elkashef Steve Grant Rita Liu Raul Mandler Cindy Miner JJ Pan Amrat Patel Jonathan Pollock Joni Rutter, Chair Larry Stanford Kay Wanke Naimah Weinberg Scientific Goals & Services • Written ARA, supplement, and funding recommendations to the Director • Recommend genetic applications assignment • Provide standards for data collection, data sharing, and informed consent • Evaluate human genetics program • Serve as the NGC Steering Committee

  11. Human GeneticsTrans-NIDA Genetics • NIDA Genetics Consortium (NGC) • NIDA Division Reps (+1 NCI rep) • >20 PIs; 24 studies; 1 contract • http://zork.wustl.edu/nida/ • ~30,000 samples in Repository • DNA and clinical information (DSMIIIR or IV) • Nicotine, Opioids, Cocaine, Polysubstance • Data to be publicly available through controlled access • NIDA Phenotyping Consortium (NPC) • DESPR-led initiative (Kevin Conway); 5 PIs • Produce precise and specific phenotypes for drug abuse Addiction “Bioseverity” Develop a research instrument measuring addiction severity Validated with biological processes (ex. Genetic, imaging, etc) Michael Vanyukov (NGC), Gary Swan (NGC), Michael Neale (NPC) Kevin Conway (DESPR), Janet Levy (DESPR), Joni Rutter (DBNBR)

  12. Human GeneticsTrans-NIDA Genetics • Collaboration between NGC, CTN, DPMCDA • “START” Study – Starting Treatment on Agonist Replacement Therapies –added pharmacogenetics • Targeting ~600 opioid dependent persons • Randomized, open-labeled CTN multi-site design to either buprenorphine or methadone • Primary outcomes are: • Liver toxicity (parent trial) • Individual genetic variation in treatment effectiveness (Pharmacogenetics) • Wade Berrettini and Lindsay DeVane

  13. Human GeneticsTrans-NIDA Genetics • Collaboration among NIDA Divisions • Genes, Environment, and Development Initiative (GEDI I & II) (DBNBR, DCNBR, DESPR) • Support research that investigates interplay among genetic, environmental, and developmental factors in the etiology of substance abuse and related phenotypes • Notice in NIH Guide for re-issue – Naimah Weinberg

  14. Saccone – Nicotine dependence HMG, 2007 Gelernter-Nicotine dependence HMG, 2006 Li - Nicotine dependence AJHG, 2006 Areas of converging results Madden – Maximum cigarettes in 24-hrs AJHG, 2007 Converging Genetics Findings:NIDA IRP & NGC Data on Nicotine Uhl GR, et al., Biochem Pharmacol (2007) Smoking-related phenotypes

  15. Smoking Phenotype Controls(lifetime >100 cigarettes; FTND=0) Cases (FTND 4 or more) Number of Subjects 0 4 5 6 7 8 9 10 Nicotine Dependence Score (Fagerstrom Test – FTND, scale 0-10)

  16. Top 40,000 SNPs 2.2 Million Perlegen SNPs Phase 2 Screen Phase 1 Screen Whole Genome-wide Association Study Targeting Nicotine Dependence 6 Million Common SNPs in Genome 71 Bullseye 71 SNPs show the most difference among the cases and controls

  17. Several SNPS Found to be Different Between Cases and Control • 71 SNPS different between case and control • Many were not the “usual suspects” • Several nicotinic receptors implicated : • a3 nicotinic receptor • β3 nicotinic receptor • a5 nicotinic receptor SNP is highly associated with nicotine dependence **CHRNA5 non-synonymous SNP D398N Individuals with this SNP have 2-fold increase risk of developing nicotine dependence OR=1.9 (1.4-2.6) Saccone et al. HMG 2007 Bierut et al. HMG 2007

  18. CHRNA5 D398 is Conserved Across Species D398N Function?

  19. The human CHRNA5 D398N polymorphism alters nicotinic receptor function in vitro Common allele P < 0.01 ** Variant allele Courtesy of Jerry Stitzel, PhD

  20. CHRNA5 encodes the nicotinic receptor alpha5 subunit Figure from Gotti et al., 2006

  21. Summary • Trans-NIH involvement • Roadmap, GAIN, GEI, PGRN, others • NIDA staff play leadership roles • NIDA PIs positioned to take advantage of programs • NIDA Genetics • Divisional coordination, programmatic synergy • Phenotype, environment, development, family, clinical, animal models • Results are converging Functional information  Mechanism • Inform treatment, prevention, intervention approaches Molecular Genetics of Drug Addiction and Related Co-Morbidities (R01)PA-07-073 (R01) - November 20, 2006 Functional Genetics and Genomics of Drug AddictionPA-07-166 (R01), PA-07-167 (R21), PA-07-168 (R03) - December 14, 2006 Genetic Epidemiology of Substance Use DisordersPA-07-413 (R01), PA-07-415 (R21), PA-07-414 (R03) - July 27, 2007

  22. Genes Development Family/ Clinical Drug Abuse Genetics Environment/ Epidemiology Model organisms • Define genetic contribution • Define environmental and developmental factors • Understand neurobiological mechanisms • Improve treatment and prevention/intervention approaches

  23. Genetic model organisms, such as the mouse, can provide clues to the genetic basis of complex diseases

  24. Three Different Methods to Identify Gene Function in Mice • Haplotype Associated Mapping of Natural Variation in Inbred Strains • Selective Breeding • Induced Mutations, e.g. Knockouts

  25. Three Different Methods to Identify Gene Function in Mice • Haplotype Associated Mapping of Natural Variation in Inbred Strains • Selective Breeding • Induced Mutations, e.g. Knockouts

  26. Power of Inbred Strains • Inbred strains are a way to determine environmental effects through development across defined genotypes under controlled conditions • Confidence in the underlying genotype is strengthened when phenotypes map to homologous chromosomal loci in mice and humans

  27. I/LnJ A/J C3H/HeJ C57L/J C57BL/6J DBA/2J LP/J C57BR/cdJ CE/J

  28. Haplotype Associated Mapping of Natural Variation in Inbred Strains • Many mouse genomes have been sequenced e.g. C57BL6/J, AJ, 129 • Genetic variation in over 15 strains of mice have been identified • Use variation in gene expression profiling as a “signature” for a trait

  29. Strain Distribution of Percent Time Spent in Center of Open Field Time Spent in Center Mouse Strain Courtesy of Tim Wilshire

  30. Differential expression of Grm7 in 4 brain regions High “Anxiety” Low “Anxiety” Gene Expression Brain Region Low Anxiety -decreased expression of Grm7- increase in percent time spent in center of open field

  31. Three Different Methods to Identify Gene Function in Mice • Haplotype Associated Mapping of Natural Variation in Inbred Strains • Selective Breeding • Induced Mutations, e.g. Knockouts

  32. Mapping Genes Using Selective Breeding • Takes advantage of the mouse genome and gene variants (SNPs) among different mouse strains • NIH proposed project will increase genetic diversity in inbred recombinant mice making it possible to map any trait in the mouse (i.e., The “Collaborative Cross”)

  33. Selection for Locomotor Stimulant Response to Methamphetamine in Mice Locomotor response to MA (2 mg/kg) Kamens et al. Genes, Brain & Behavior, 2005

  34. Csnk1e Gene Expressed More in Methamphetamine Sensitive Mice The most differentially expressed gene was Csnk1e (10 fold different) There is a massive eQTL for Csnk1e Palmer et al. Mammalian Genome, 2005

  35. Csnk1e Variant Enhances Dopamine Signaling Through Darpp-32 Signaling Protein Palmer et al. Mammalian Genome, 2005

  36. Translational Genetic Models of Methamphetamine Sensitivity Humans vary in methamphetamine sensitivity Differences in sensitivity are partially under genetic control in both mice and humans Differences in initial effects of drugs linked to future use An predictor or intermediate phenotype to addiction in humans

  37. Effect of CSNK1E Polymorphisms on Response to Methamphetamine • 3 polymorphisms in CSNK1E in humans • All 3 SNPs occur at high frequency • Analyzed their relationship with 3 primary outcome measures: • DEQ “Feel Drug” • ACRI “Euphoria” • POMS “Anxiety” Veenstra-VanderWeele et al. Neuropsychopharm., 2005

  38. ACRI “Euphoria” is Also Significantly Influenced by SNPs in CSNK1E * * * Veenstra-VanderWeele et al. Neuropsychopharmacology, 2005

  39. Chromosomal Regions are Conserved for Drug Response in Mice and Humans • Formin • LRP2 • Alpha 6 integrin • NFI/B • NPAS2 • NIMA-related 7 Kinase Uhl et al, 2007 Biochemical Pharmacology

  40. Three Different Methods to Identify Gene Function in Mice • Haplotype Associated Mapping of Natural Variation in Inbred Strains • Selective Breeding • Induced Mutations, e.g. Knockouts

  41. NIH Knockout Mouse Project (KOMP) • KOMP is a trans-NIH project involving 18 Institutes • The NIH has committed ~ $52 M over 5 years • KOMP is cooperating with other international efforts to minimize overlap

  42. Why are Knockouts and Transgenic Animals Useful? • Help identify essential molecules in reward pathways mediating drug abuse • Help separate cellular signaling pathways that mediate different phenomena associated with addiction (e.g., withdrawal, tolerance)

  43. Mice Lacking the Β2 Nicotinic Receptor Do Not Exhibit Nicotine Place Preference. Walters CL, et al Psychopharmacology (Berl). 2006 Mar;184(3-4):339-44

  44. Global Gene Knockout Programs

  45. Model Organism Genetics Genes Development knockout Drug Abuse Genetics Environment Epigenetics • Define genetic contribution • Define environmental and developmental factors • Understand neurobiological mechanisms • Provide genetic resources to community • Test putative medications

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