Epigenomics at NIDA, NIH, and Beyond John Satterlee Ph.D. National - PowerPoint PPT Presentation

Epigenomics at NIDA, NIH, and Beyond John Satterlee Ph.D. National Institute on Drug Abuse/NIH Roadmap Epigenomics Program Co-coordinator NIDA Council Sept 3, 2014

One genome transcription factors epigenomics Many cell types

Epigenomic Changes Have Been Implicated in a Wide Variety of Human Diseases Adverse health outcomes Normal processes Cancer Development Cell differentiation Cardiopulmonary disease Autoimmune disease Aging Obesity Diabetes AIDS GENOME DISEASE EPIGENOME External influences Environmental exposures Nutrition Neurodevelopmental disorders Chemical toxins Schizophrenia Metals Depression Mediators of stress Alzheimer’s Disease Infection (including HIV) Addiction Drugs of abuse

Increasing Number of Publications in Neuroepigenetics Non-review Publications Year Dena Procaccini

Epigenetics and Epigenomics Epigenetics= the study of heritable or long lasting changes that are not caused by changes in the DNA sequence Epigenome = all of the epigenetic marks for a cell type DNA modifications 4 types? Histone modifications >150 types? Non-coding RNAs

Adapted from Brad Bernstein Epigenomic Modifications Mark Functional Genomic Elements

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Cumulative NIDA Epigenetics R01/R21/RC1 Grants # funded grants

Drugs of Abuse and Epigenetic Changes Cocaine Methamphetamine Nicotine Itzak lab, Mol Psychiatry, 2014 Nestler lab, Science, 2010 Kandel lab, Sci Transl Med, 2011 Cadet lab, PlosOne 2014 Nestler lab, PNAS 2011 Guidotti lab, PNAS 2008 Grant lab, PlosOne 2014 Cowan lab, Neuron, 2012 Cannabinoids Opioids Alcohol Hurd lab, Biol Psych. 2012 Kreek lab,Neuropsychopharm.2008 Goldman lab, PNAS 2011 Hurd lab, Neuropsychopharm 2014 Loh and Wei lab, PNAS 2012 Atkinson lab, PlosGenetics Nagarkatti lab, JBC 2014 2013Lanfumey lab, Mol Psych 2014

Why do we care about epigenetics and substance abuse? • Molecular mechanisms of SUD • Biomarkers • Intergenerational effects • Identify new targets for therapeutics • Epigenetic therapeutics

Histone acetylation controls chromatin structure and gene expression COMPRESSED EXPANDED CHROMATIN CHROMATIN Histone Acetyltransferase HAT (e.g. CBP) HAT (CBP) Histone Deacetylase HDAC Less histone acetylation More histone acetylation Decreased gene expression Increased gene expression Adapted from Marcelo Wood

HDAC3 inhibitor (RGFP966) enhances extinction of cocaine-seeking behavior Single dose HDAC3i: • enhances extinction of cocaine CPP Malvaez, 2013, PNAS Marcelo Wood lab. PNAS 2013, J. Neuro 2013.

Environmental Exposures and Intergenerational Phenotypes PARENTAL (F0) PHENOTYPIC EFFECT (generation) EXPOSURE Food availability Impaired glucose tolerance (F2) (Zambrano J. Physiol, 2005) (caloric restriction) Chemical toxins Male fertility (F1, F2, F3, F4) (Anway, Science, 2005) (endocrine disruptors) Anxiety/gene expression brain (F1, F2, F3) (Skinner, PLOS One 2008) Psychosocial stress/ Maternal behavior (F1, F2) Response to novelty (F1, F2) Social environment (Champagne, Behav. Neuro., 2007) Enriched environment Enhanced LTP (F1, not F2) (Arai, J. Neuro, 2009) ???? Drugs of Abuse

Intergenerational Effects of Drugs of Abuse PHENOTYPE (generation) EXPOSURE Morphine ( i.p. ) Increased morphine analgesia, male F1 progeny adolescent female Byrnes et. Al. Brain Behav. Res 2011, 218: 200-205 rat Compulsive heroin seeking and altered THC ( i.p. ) striatal plasticity, male F1 progeny adolescents Szutorisz et al. Neuropsychopharm. 2014, 39: 1215- rat 1323 Cocaine self admin. Delayed acquisition of cocaine self- adult male administration, male F1 progeny rat Vassoler et al. 2013 Nat. Neuro. 16: 42-47

What is the mechanism of cocaine ‐ associated information transmission from father to son? F0 Sperm 75 * Increased BDNF promoter Saline Cocaine acetylation in sperm of cocaine- AcH3 Association with 60 BDNF Promoters exposed fathers. 45 * Cocaine can reprogram * 30 the sperm epigenome. 15 0 I IV VI Vassoler et al. 2013 Nat. Neuro. 16: 42-47 BDNF Promoter

Epigenetics and HIV Latency

Epigenetic Manipulation of Latent HIV Repression HDACi “Shock and Kill” ? Repression Barton et al 2014 PLoS One 9:e102684 “Lockdown” Lucera et al, 2014 J. Virol 88:10803-12

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Current Common Fund Programs Illuminating the Druggable Undiagnosed Extracellular RNA Epigenomics Genome Diseases Strengthening Communication the Program PROMIS: Big Data to Health Biomedical Research Single Regulatory Clinical NIH Knowledge Economics Workforce Cell Science Outcomes Center for (BD2K) Analysis Assessment Regenerative Increasing the Diversity Bridging NIH Medical Medicine Molecular of the NIH-Funded Interventional Research Protein Libraries Workforce Development Scholars Capture and Imaging Gulf Oil Spill Gaps (BrIDGs) Long Term Human Follow Up Nanomedicine Microbiome HCS Research Collaboratory Knockout High-Risk NIH Global Mouse Pioneer Awards Research Health Common Fund Phenotyping New Innovator Awards Transformative Research Awards Bioinformatics and Early Independence Awards Computational Biology Structural Biology Library of Building Blocks, Science of Metabolomics Genotype- Integrated Network- Biological Pathways Behavior ExRNA Tissue Based Cellular And Networks Change Communication Expression Signatures (LINCS) http://commonfund.nih.gov/

NIH Epigenomics Working Group Co ‐ Chairs: Nora Volkow (NIDA), Linda Birnbaum (NIEHS), James Battey (NIDCD) Co ‐ Coordinators: John Satterlee (NIDA), Pat Mastin (NIEHS) Christine Colvis NCATS Roderic Pettigrew NIBIB Astrid Haugen NIEHS Carol Pontzer NCCAM Carol Kasten ‐ Sportes NICHD Jerry Heindel NIEHS Laurie Johnson NIEHS Grace Ault NCI Lisa Freund NICHD Kimberly McAllister NIEHS Jennifer Couch NCI Susan Taymans NICHD Srikanth Nadadur NIEHS Mark Caulder NIDA Paul Okano NCI Kristi Pettibone NIEHS Genevieve deAlmeida ‐ Morris NIDA Richard Piekarz NCI Fred Tyson NIEHS Sharon Ross NCI Donna Jones NIDA Leroy Worth NIEHS Jonathan Pollock NIDA Mukesh Verma NCI Anthony Carter NIGMS Dena Procaccini NIDA Hemin Chin NEI Andrea Beckel ‐ Mitchener NIMH Elise Feingold NHGRI Joni Rutter NIDA Michelle Freund NIMH Bracie Watson NIDCD Mike Pazin NHGRI Thomas Lehner NIMH Weiniu Gan NHLBI Lillian Shum NIDCR Roger Little NIMH Kristin Abraham NIDDK Susan Old NHLBI Aleksandra Vicentic NIMH Olivier Blondel NIDDK Pothur Srinivas NHLBI Robert Riddle NINDS Anna McCormick NIA Jessica Faupel ‐ Badger NIDDK Randall Stewart NINDS Philip Smith NIDDK Suzana Petanceska NIA Stephanie Courchesne OSC Julie Wallace NIDDK Conrad Malia NIAID Patricia Labosky OSC Lisa Chadwick NIEHS Nasrin Nabavi NIAID Johanna Dwyer ODS Gwen Collman NIEHS Ashley Xia NIAID Deborah Olster OBSSR Christie Drew NIEHS William Sharrock NIAMS Guoying Liu NIBIB

NIH Roadmap Epigenomics Program Epigenetic Assay Mapping Centers Improvement 88 grants $230M In vivo Epigenetic Health and Data Coord. Disease Center Imaging Functional dbGAP/GEO Epigenomic Manipulation Computational Novel Marks Epigenomics

Epigenome Mapping Centers GOAL: Generate comprehensive epigenomic maps for “normal” human cells and tissues  First human methylomes ( Nature 2009)  92 comprehensive epigenome datasets  Data publically accessible: http://www.roadmapepigenomics.org/

A Diversity of Human Cells and Tissues http://www.roadmapepigenomics.org/

Integrative Analysis of 92 Roadmap Epigenomes  Analysis Lead: Manolis Kellis, MIT/Broad  Integrative paper: • Analysis of 92 epigenomes • 236 authors  25 companion papers: • detailed investigations into epigenetic marks, diseases  In revision with Nature and Nature Family journals  Publication of “Manolis Bolus” in Nature mid-February? Lisa Chadwick, NIEHS

The Utility of Epigenomic Information  Functional genomic prediction  Environmental exposures  Understanding development and differentiation  Regenerative medicine (stem and iPS cells)  Human disease  Interpreting GWAS  Biomarkers, diagnostics and therapies  Exploring cross-talk between epigenomic mechanisms

Published GWAS Hits for a Wide Variety of Diseases http://www.genome.gov/gwastudies /

Using Epigenomic Data to Interpret GWAS Data Gene variants in Epigenomic data for many human disease normal human cell/tissue types +  77% of disease variants are in/near enhancer elements or promoters*  Variants are in regulatory regions NOT protein coding regions  Generate hypotheses about function *DNAse I hypersensitive sites Stamatoyannopoulos, Science 337:1190, 2012

Use Epigenomic Information for “Normal” Cells/Tissues to Identify Pathogenic Cell Types Identify cell types involved in disease Stamatoyannopoulos, Science 337:1190, 2012

Computational Epigenomics RFA (R01) GOAL: Computational analyses taking advantage of the publicly available reference epigenomic maps along with other data sets. GRANTS: Fund 10 2-year R01s in September, 2014

NIH Roadmap Epigenomics Program Epigenetic Assay Mapping Centers Improvement In vivo Epigenetic Health and Data Coord. Disease Center Imaging Functional dbGAP/GEO Epigenomic Manipulation Computational Novel Marks Epigenomics