Advertisement

Molecular Medicine

, Volume 21, Issue 1, pp 717–725 | Cite as

Mouse Model for Protein Tyrosine Phosphatase D (PTPRD) Associations with Restless Leg Syndrome or Willis-Ekbom Disease and Addiction: Reduced Expression Alters Locomotion, Sleep Behaviors and Cocaine-Conditioned Place Preference

  • Jana Drgonova
  • Donna Walther
  • Katherine J. Wang
  • G. Luke Hartstein
  • Bryson Lochte
  • Juan Troncoso
  • Noriko Uetani
  • Yoichiro Iwakura
  • George R. Uhl
Research Article

Abstract

The receptor type protein tyrosine phosphatase D (PTPRD) gene encodes a cell adhesion molecule likely to influence development and connections of addiction-, locomotion- and sleep-related brain circuits in which it is expressed. The PTPRD gene harbors genome-wide association signals in studies of restless leg syndrome (Willis-Ekbom disease (WED)/restless leg syndrome (RLS); p < 10−8) and addiction-related phenotypes (clusters of nearby single nucleotide polymorphisms (SNPs) with 10−2 > p > 10−8 associations in several reports). We now report work that seeks (a) association between PTPRD genotypes and expression of its mRNA in postmortem human brains and (b) RLS-related, addiction-related and comparison behavioral phenotypes in hetero- and homozygous PTPRD knockout mice. We identify associations between PTPRD SNPs and levels of PTPRD mRNA in human brain samples that support validity of mouse models with altered PTPRD expression. Knockouts display less behaviorally defined sleep at the end of their active periods. Heterozygotes move more despite motor weakness/impersistence. Heterozygotes display shifted dose-response relationships for cocaine reward. They display greater preference for places paired with 5 mg/kg cocaine and less preference for places paired with 10 or 20 mg/kg. The combined data provide support for roles for common, level-of-expression PTPRD variation in locomotor, sleep and drug reward phenotypes relevant to RLS and addiction. Taken together, mouse and human results identify PTPRD as a novel therapeutic target for RLS and addiction phenotypes.

Notes

Acknowledgments

This work was supported by the NIH-IRP, NIDA, U.S. Department of Health and Human Services (HHS) (GR Uhl) and by New Mexico VA Health Care System (NMVAHCS) (GR Uhl). We are grateful for help from C Johnson, D Arking, D Naimen, J Bader and J Schroder; for access to brain samples from the University of Maryland Brain Tissue Bank; and for access to data for PTPRD from A Hart and A Palmer. All human and/or animal studies were approved by the appropriate institutional committees.

Supplementary material

10020_2015_2101717_MOESM1_ESM.pdf (1.8 mb)
Supplementary material, approximately 1.76 MB.

References

  1. 1.
    Lein ES, et al. (2007) Genome-wide atlas of gene expression in the adult mouse brain. Nature. 445:168–76.CrossRefGoogle Scholar
  2. 2.
    Wang J, Bixby JL. (1999) Receptor tyrosine phosphatase-delta is a homophilic, neurite-promoting cell adhesion molecular for CNS neurons. Mol. Cell. Neurosci. 14:370–84.CrossRefGoogle Scholar
  3. 3.
    Takahashi H, et al. (2012) Selective control of inhibitory synapse development by Slitrk3-PT-Pdelta trans-synaptic interaction. Nat. Neurosci. 15:389–98, S381–2.CrossRefGoogle Scholar
  4. 4.
    Yim YS, et al. (2013) Slitrks control excitatory and inhibitory synapse formation with LAR receptor protein tyrosine phosphatases. Proc. Natl. Acad. Sci. U. S. A. 110:4057–62.CrossRefGoogle Scholar
  5. 5.
    Brose N. (2013) Why we need more synaptogenic cell-adhesion proteins. Proc. Natl. Acad. Sci. U. S. A. 110:3717–8.CrossRefGoogle Scholar
  6. 6.
    Freeman AA, Rye DB. (2013) The molecular basis of restless legs syndrome. Curr. Opin. Neurobiol. 23:895–900.CrossRefGoogle Scholar
  7. 7.
    Schormair B, et al. (2008) PTPRD (protein tyrosine phosphatase receptor type delta) is associated with restless legs syndrome. Nat. Genet. 40:946–8.CrossRefGoogle Scholar
  8. 8.
    Yang Q, et al. (2011) Family-based and population-based association studies validate PTPRD as a risk factor for restless legs syndrome. Mov. Disord. 26:516–9.CrossRefGoogle Scholar
  9. 9.
    Uhl GR, et al. (2008) Molecular genetics of successful smoking cessation: convergent genome-wide association study results. Arch. Gen. Psychiatry. 65:683–93.CrossRefGoogle Scholar
  10. 10.
    Uhl GR, et al. (2014) Smoking quit success genotype score predicts quit success and distinct patterns of developmental involvement with common addictive substances. Mol. Psychiatry. 19:50–4.CrossRefGoogle Scholar
  11. 11.
    Uhl GR, et al. (2008) “Higher order” addiction molecular genetics: convergent data from genome-wide association in humans and mice. Biochem. Pharmacol. 75:98–111.CrossRefGoogle Scholar
  12. 12.
    Drgon T, et al. (2010) Genome wide association for addiction: replicated results and comparisons of two analytic approaches. PLoS One. 5:e8832.CrossRefGoogle Scholar
  13. 13.
    Drgon T, et al. (2011) “Replicated” genome wide association for dependence on illegal substances: genomic regions identified by overlapping clusters of nominally positive SNPs. Am. J. Med. Genet B. Neuropsychiatr. Genet. 156:125–38.CrossRefGoogle Scholar
  14. 14.
    Drgon T, et al. (2009) Genome-wide association for smoking cessation success: participants in a trial with adjunctive denicotinized cigarettes. Mol. Med. 15:268–74.CrossRefGoogle Scholar
  15. 15.
    Johnson C, et al. (2008) Genome wide association for substance dependence: convergent results from epidemiologic and research volunteer samples. BMC Med. Genet. 9:113.CrossRefGoogle Scholar
  16. 16.
    Johnson C, Drgon T, Walther D, Uhl GR. (2011) Genomic regions identified by overlapping clusters of nominally-positive SNPs from genomewide studies of alcohol and illegal substance dependence. PLoS One. 6:e19210.CrossRefGoogle Scholar
  17. 17.
    Drgon T, et al. (2009) Genome-wide association for nicotine dependence and smoking cessation success in NIH research volunteers. Mol. Med. 15:21–7.CrossRefGoogle Scholar
  18. 18.
    Uhl GR, et al. (2010) Genome-wide association for smoking cessation success: participants in the Patch in Practice trial of nicotine replacement. Pharmacogenomics. 11:357–67.CrossRefGoogle Scholar
  19. 19.
    Uhl GR, et al. (2010) Genome-wide association for smoking cessation success in a trial of precessation nicotine replacement. Mol. Med. 16:513–26.CrossRefGoogle Scholar
  20. 20.
    Tzschentke TM. (1998) Measuring reward with the conditioned place preference paradigm: a comprehensive review of drug effects, recent progress and new issues. Prog. Neurobiol. 56:613–72.CrossRefGoogle Scholar
  21. 21.
    Thierry-Mieg D, Thierry-Mieg J. (2006) AceView: a comprehensive cDNA-supported gene and transcripts annotation. Genome Biol. 7 Suppl 1:S12.CrossRefGoogle Scholar
  22. 22.
    Hishimoto A, et al. (2007): Neurexin 3 polymorphisms are associated with alcohol dependence and altered expression of specific isoforms. Hum. Mol. Genet. 16:2880–91.CrossRefGoogle Scholar
  23. 23.
    Tobacco and Genetics Consortium. (2010) Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42:441–7.CrossRefGoogle Scholar
  24. 24.
    Hebbard LW, et al. (2008) T-cadherin supports angiogenesis and adiponectin association with the vasculature in a mouse mammary tumor model. Cancer Res. 68:1407–16.CrossRefGoogle Scholar
  25. 25.
    Uetani N, et al. (2000) Impaired learning with enhanced hippocampal long-term potentiation in PTPdelta-deficient mice. EMBO J. 19:2775–85.CrossRefGoogle Scholar
  26. 26.
    Drgonova J, Zimonjic DB, Hall FS, Uhl GR. (2010) Effect of KEPI (Ppp1r14c) deletion on morphine analgesia and tolerance in mice of different genetic backgrounds: when a knockout is near a relevant quantitative trait locus. Neuroscience. 165:882–95.CrossRefGoogle Scholar
  27. 27.
    Hall FS, et al. (2012) Effects of neurotensin gene knockout in mice on the behavioral effects of cocaine. Psychopharmacology (Berl.). 219:35–45.CrossRefGoogle Scholar
  28. 28.
    Morris R (1984). Developments of a water-maze procedure for studying spatial learning in the rat. J. Neurosci. Methods. 11:47–60.CrossRefGoogle Scholar
  29. 29.
    Brambilla F. (1938) Statistica metodologica e calcolo delle probabilità (a proposito di recenti studi del prof. Bonferroni). Giornale degli Economisti e Rivista di Statistica. 78(Anno 53):398–415.Google Scholar
  30. 30.
    Sora I, Li B, Igari M, Hall FS, Ikeda K. (2010) Transgenic mice in the study of drug addiction and the effects of psychostimulant drugs. Ann. N. Y. Acad. Sci. 1187:218–46.CrossRefGoogle Scholar
  31. 31.
    Pontius JU, Wagner L, Schuler GD. (2003) UniGene: a Unified View of the Transcriptome [Internet]. In: The NCBI Handbook. McEntyre J, Ostell J (eds.). (2002-) National Center for Biotechnology Information, Bethesda (MD). Available from: https://doi.org/www.ncbi.nlm.nih.gov/books/NBK21101/?report=readerGoogle Scholar
  32. 32.
    Hawrylycz MJ, et al. (2012) An anatomically comprehensive atlas of the adult human transcriptome. Nature. 489:391–9.CrossRefGoogle Scholar
  33. 33.
    Treutlein J, et al. (2009) Genome-wide association study of alcohol dependence. Arch. Gen. Psychiatry. 66:773–84.CrossRefGoogle Scholar
  34. 34.
    Ishiguro H, et al. (2006) NrCAM in addiction vulnerability: positional cloning, drug-regulation, haplotype-specific expression, and altered drug reward in knockout mice. Neuropsychopharmacology. 31:572–84.CrossRefGoogle Scholar
  35. 35.
    Jackson KJ, et al. (2010) Role of alpha5 nicotinic acetylcholine receptors in pharmacological and behavioral effects of nicotine in mice. J. Pharmacol. Exp. Ther. 334:137–46.CrossRefGoogle Scholar
  36. 36.
    DeAndrade MP, et al. (2012) Motor restlessness, sleep disturbances, thermal sensory alterations and elevated serum iron levels in Btbd9 mutant mice. Hum. Mol. Genet. 21:3984–92.CrossRefGoogle Scholar
  37. 37.
    Uhl GR, et al. (2008) Molecular genetics of addiction and related heritable phenotypes: genome-wide association approaches identify “connectivity constellation” and drug target genes with pleiotropic effects. Ann. N. Y. Acad. Sci. 1141:318–81.CrossRefGoogle Scholar
  38. 38.
    Uhl GR, Drgonova J. (2014) Cell adhesion molecules: druggable targets for modulating the connectome and brain disorders? Neuropsychopharmacology. 39:235.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2015

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, and provide a link to the Creative Commons license. You do not have permission under this license to share adapted material derived from this article or parts of it.

The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this license, visit (https://doi.org/creativecommons.org/licenses/by-nc-nd/4.0/)

Authors and Affiliations

  • Jana Drgonova
    • 1
  • Donna Walther
    • 1
  • Katherine J. Wang
    • 1
  • G. Luke Hartstein
    • 1
  • Bryson Lochte
    • 1
  • Juan Troncoso
    • 2
  • Noriko Uetani
    • 3
  • Yoichiro Iwakura
    • 4
  • George R. Uhl
    • 1
    • 5
  1. 1.Molecular Neurobiology Branch, National Institute on Drug Abuse (NIDA)National Institutes of Health (NIH)-Intramural Research Program (IRP)BaltimoreUSA
  2. 2.Division of NeuropathologyJohns Hopkins School of MedicineBaltimoreUSA
  3. 3.Rosalind and Morris Goodman Cancer Research CenterMcGill UniversityMontrealCanada
  4. 4.Center for Experimental MedicineUniversity of TokyoTokyoJapan
  5. 5.Research, New Mexico VA Health Care SystemAlbuquerqueUSA

Personalised recommendations