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Chromatin architecture in addiction circuitry identifies risk genes and potential biological mechanisms underlying cigarette smoking and alcohol use traits

Molecular Psychiatry volume 27, pages 3085–3094 (2022)Cite this article

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A Correction to this article was published on 07 July 2022

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Abstract

Cigarette smoking and alcohol use are among the most prevalent substances used worldwide and account for a substantial proportion of preventable morbidity and mortality, underscoring the public health significance of understanding their etiology. Genome-wide association studies (GWAS) have successfully identified genetic variants associated with cigarette smoking and alcohol use traits. However, the vast majority of risk variants reside in non-coding regions of the genome, and their target genes and neurobiological mechanisms are unknown. Chromosomal conformation mappings can address this knowledge gap by charting the interaction profiles of risk-associated regulatory variants with target genes. To investigate the functional impact of common variants associated with cigarette smoking and alcohol use traits, we applied Hi-C coupled MAGMA (H-MAGMA) built upon cortical and newly generated midbrain dopaminergic neuronal Hi-C datasets to GWAS summary statistics of nicotine dependence, cigarettes per day, problematic alcohol use, and drinks per week. The identified risk genes mapped to key pathways associated with cigarette smoking and alcohol use traits, including drug metabolic processes and neuronal apoptosis. Risk genes were highly expressed in cortical glutamatergic, midbrain dopaminergic, GABAergic, and serotonergic neurons, suggesting them as relevant cell types in understanding the mechanisms by which genetic risk factors influence cigarette smoking and alcohol use. Lastly, we identified pleiotropic genes between cigarette smoking and alcohol use traits under the assumption that they may reveal substance-agnostic, shared neurobiological mechanisms of addiction. The number of pleiotropic genes was ~26-fold higher in dopaminergic neurons than in cortical neurons, emphasizing the critical role of ascending dopaminergic pathways in mediating general addiction phenotypes. Collectively, brain region- and neuronal subtype-specific 3D genome architecture helps refine neurobiological hypotheses for smoking, alcohol, and general addiction phenotypes by linking genetic risk factors to their target genes.

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Fig. 1: Gene regulatory landscape in cortical and dopaminergic neurons.
Fig. 2: Genes and pathways associated with cigarette smoking and alcohol use traits.
Fig. 3: Cellular and brain regional expression profiles of cigarette smoking and alcohol use risk genes.
Fig. 4: Pleiotropic genes highlight shared neurobiological bases of cigarette smoking and alcohol use.

Data availability

CN (syn21760712) and DN (syn24184521) Hi-C datasets described in this manuscript are available via the PsychENCODE Knowledge Portal (https://psychencode.synapse.org/). The PsychENCODE Knowledge Portal is a platform for accessing data, analyses, and tools generated through grants funded by the National Institute of Mental Health (NIMH) PsychENCODE program. Data is available for general research use according to the following requirements for data access and data attribution: (https://psychencode.synapse.org/DataAccess). H-MAGMA input and output files are available in the Github repository (https://github.com/thewonlab/H-MAGMA). GWAS summary statistics for DPW and CPD were obtained from https://genome.psych.umn.edu/index.php/GSCAN. GWAS summary statistics for ND and PAU were obtained from dbGaP with the accession numbers and phs001532.v1.p1 and phs001672.v3.p1, respectively. RNA-seq and ATAC-seq data from hiPSC-derived CNs and DNs were obtained from GSE129017.

Code availability

All custom code used in this work is available in the following Github repository: https://github.com/thewonlab/H-MAGMA.

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Acknowledgements

We thank members of the Won lab for helpful discussions and comments about this paper, in particular, Nana Matoba, Won Mah, and Jessica McAfee. We also acknowledge helpful advice and discussion from Jonathan Pollock, Amy Lossie, and Susan Wright. We thank Stefano Marenco and Barbara Lipska from the Human Brain Collection Core (HBCC, Bethesda, MD) for providing postmortem brain specimens; Mette Peters, Kelsey Montgomery, and Juliane Schneider for assisting data deposition into synapse. This research was supported by the National Institute on Drug Abuse (R21DA051921, HW, DBH, EOJ; U01DA048279, SA), National Institute of Mental Health (R00MH113823, DP2MH122403, HW), the NARSAD Young Investigator Award from the Brain and Behavior Research Foundation (HW). NYAS was supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1650116 and in part by a grant to the University of North Carolina at Chapel Hill from the Howard Hughes Medical Institute through the James H. Gilliam Fellowship for Advanced Study Program. SL was supported by the National Institute of General Medical Sciences (5T32GM067553). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC, 27599, USA

    Nancy Y. A. Sey, Benxia Hu & Hyejung Won

  2. Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA

    Nancy Y. A. Sey, Benxia Hu, Sool Lee, Huaigu Sun & Hyejung Won

  3. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Marina Iskhakova, Neda Shokrian, Gabriella Ben Hutta & Schahram Akbarian

  4. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Marina Iskhakova, Neda Shokrian, Gabriella Ben Hutta & Schahram Akbarian

  5. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Marina Iskhakova, Neda Shokrian, Gabriella Ben Hutta & Schahram Akbarian

  6. GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, Chapel Hill, NC, 27709, USA

    Jesse A. Marks, Bryan C. Quach, Eric O. Johnson & Dana B. Hancock

  7. Fellow Program, RTI International, Research Triangle Park, Chapel Hill, NC, 27709, USA

    Eric O. Johnson

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Contributions

NYAS, BH, DBH, EOJ, SA, and HW designed the research. SA supervised DN Hi-C library generation. NS and GBH sorted dopaminergic nuclei from the midbrain. MI generated DN Hi-C libraries. BH and SL analyzed ATAC-seq, RNA-seq, and Hi-C data and developed DN H-MAGMA framework. NYAS performed H-MAGMA analysis and functional characterization of risk genes. BCQ, JAM, EOJ, and DBH performed meta-analysis of ND GWAS. HS performed EnrichR analysis. HW, NYAS, and BH generated and edited figures. HW, NYAS, and BH co-wrote the first draft of the manuscript, which was subsequently revised by all other co-authors.

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Correspondence to Schahram Akbarian or Hyejung Won.

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The original online version of this article was revised: In Fig. 3B of this article, a typo Hippocmapus was corrected to Hippocampus. The original article has been corrected.

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Sey, N.Y.A., Hu, B., Iskhakova, M. et al. Chromatin architecture in addiction circuitry identifies risk genes and potential biological mechanisms underlying cigarette smoking and alcohol use traits. Mol Psychiatry 27, 3085–3094 (2022). https://doi.org/10.1038/s41380-022-01558-y

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