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NIDA Genetics: An Update

Individual Differences in Vulnerability to Addiction . Not everyone who takes drugs becomes addicted

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NIDA Genetics: An Update

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    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 But, what we do not understand is why some people become addicted and others do not. We know from NIDA supported Twin studies that there is a heritable component to drug addiction. And now with the sequencing of the human genome we are able to ask what are the gene variants associated with drug addiction and what is their functional significance? We have taken a two-pronged approach to begin to answer this question: First NIDA’s intramural program is looking at genetic variants in heterogeneous populations of drug addicted individuals. Second our extramural research program through NIDA’s Genetic consortium and a contract with Perlegen Sciences has just completed a whole genome wide association study to uncover genetics variations associated with Tobacco dependence. A similar approach to uncover the genetic basis of disease now also moving forward under the NIH GAIN (Genetic Association Information Network) and GEI (Genes and Environment Initiative) But, what we do not understand is why some people become addicted and others do not. We know from NIDA supported Twin studies that there is a heritable component to drug addiction. And now with the sequencing of the human genome we are able to ask what are the gene variants associated with drug addiction and what is their functional significance? We have taken a two-pronged approach to begin to answer this question: First NIDA’s intramural program is looking at genetic variants in heterogeneous populations of drug addicted individuals. Second our extramural research program through NIDA’s Genetic consortium and a contract with Perlegen Sciences has just completed a whole genome wide association study to uncover genetics variations associated with Tobacco dependence. A similar approach to uncover the genetic basis of disease now also moving forward under the NIH GAIN (Genetic Association Information Network) and GEI (Genes and Environment Initiative)

    4. Goal: Identify Genetic Contributions to Drug Abuse

    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 Genetics Trans-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

    7. Human Genetics Trans-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 Genetics Trans-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. NIDA Genetics: A Coordinated Effort

    11. Human Genetics Trans-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

    12. Human Genetics Trans-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. 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 Human Genetics Trans-NIDA Genetics

    14. Converging Genetics Findings: NIDA IRP & NGC Data on Nicotine

    15. Smoking Phenotype The control subjects were non-smokers who had tried at least 100 cigarettes in a lifetime. And had a Fagorstrom Score of 0. The Fagorstrom test is a standard index of nicotine dependence, and is used as a smoking phenotype. Individuals are asked a series of questions about there smoking behavior and receive a score from 0-10 based on their answers. The cases had a Fagorstrom score of 4 or more. Usually, patients with Fagorstrom score of 6 or more need treatment to quit smoking. The control subjects were non-smokers who had tried at least 100 cigarettes in a lifetime. And had a Fagorstrom Score of 0. The Fagorstrom test is a standard index of nicotine dependence, and is used as a smoking phenotype. Individuals are asked a series of questions about there smoking behavior and receive a score from 0-10 based on their answers. The cases had a Fagorstrom score of 4 or more. Usually, patients with Fagorstrom score of 6 or more need treatment to quit smoking.

    16. We know that there are approximately 6milion commonly occurring SNPS in the human genome with an allele frequency of 5% or more. In this study we screened for 2.2M SNPS and examined the top 40,000. Of those top 40,000, 81 were significantly different between heavy smokers and control non-smokers.We know that there are approximately 6milion commonly occurring SNPS in the human genome with an allele frequency of 5% or more. In this study we screened for 2.2M SNPS and examined the top 40,000. Of those top 40,000, 81 were significantly different between heavy smokers and control non-smokers.

    17. 71 SNPS different between case and control Many were not the “usual suspects” Several nicotinic receptors implicated : a3 nicotinic receptor ß3 nicotinic receptor a5 nicotinic receptor SNP is highly associated with nicotine dependence

    18. CHRNA5 D398 is Conserved Across Species

    19. The human CHRNA5 D398N polymorphism alters nicotinic receptor function in vitro HEK293T cells were transfected with alpha4 and beta2 cDNAs and either alpha5D398 or alpha5N398. Response is epibatidine-evoked change in intracellular calcium as measured previously by Karadsheh et al. (2004). HEK293T cells were transfected with alpha4 and beta2 cDNAs and either alpha5D398 or alpha5N398. Response is epibatidine-evoked change in intracellular calcium as measured previously by Karadsheh et al. (2004).

    20. CHRNA5 encodes the nicotinic receptor alpha5 subunit

    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

    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

    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

    30. Differential expression of Grm7 in 4 brain regions

    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

    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

    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

    39. Chromosomal Regions are Conserved for Drug Response in Mice and Humans Formin LRP2 Alpha 6 integrin NFI/B NPAS2 NIMA-related 7 Kinase

    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 ?2 Nicotinic Receptor Do Not Exhibit Nicotine Place Preference .

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