Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Detection of non-targeted transgenes by whole-genome resequencing for gene-doping control

Gene Therapy volume 28, pages 199–205 (2021)Cite this article

Subjects

Abstract

Gene doping has raised concerns in human and equestrian sports and the horseracing industry. There are two possible types of gene doping in the sports and racing industry: (1) administration of a gene-doping substance to postnatal animals and (2) generation of genetically engineered animals by modifying eggs. In this study, we aimed to identify genetically engineered animals by whole-genome resequencing (WGR) for gene-doping control. Transgenic cell lines, in which the erythropoietin gene (EPO) cDNA form was inserted into the genome of horse fibroblasts, were constructed as a model of genetically modified horse. Genome-wide screening of non-targeted transgenes was performed to find structural variation using DELLY based on split-read and paired-end algorithms and Control-FREEC based on read-depth algorithm. We detected the EPO transgene as an intron deletion in the WGR data by the split-read algorithm of DELLY. In addition, single-nucleotide polymorphisms and insertions/deletions artificially introduced in the EPO transgene were identified by WGR. Therefore, genome-wide screening using WGR can contribute to gene-doping control even if the targets are unknown. This is the first study to detect transgenes as intron deletions for gene-doping detection.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Detection of the horse erythropoietin (EPO) transgene as an intron deletion by DELLY.
Fig. 2: Detection concept as an intron deletion of a transgene by the split-read algorithm.

Data availability

Sequencing data of transgenic cell lines will be provided upon request to the corresponding author because of gene doping control in the horseracing industry.

References

  1. Neuberger EWI, Simon P. Gene and cell doping: the new frontier—beyond myth or reality. Med Sport Sci. 2017;62:91–106.

    Article  Google Scholar 

  2. Brzeziańska E, Domańska D, Jegier A. Gene doping in sport—perspectives and risks. Biol Sport. 2014;31:251–9.

    Article  Google Scholar 

  3. Martin NP, Myers P, Goulding E, Chen SH, Walker M, Porter TM, et al. Laser-assisted lentiviral gene delivery to mouse fertilized eggs. J Vis Exp. 2018;141:58327.

    Google Scholar 

  4. Eghbalsaied S, Kues WA. Development of a transposon-based technology for transfection of day 0 chicken embryos. Gene. 2020;730:144318.

    Article  CAS  Google Scholar 

  5. Chandrashekran A, Casimir C, Dibb N, Readhead C, Winston R. Generating transgenic mice by lentiviral transduction of spermatozoa followed by in vitro fertilization and embryo transfer. Methods Mol Biol. 2016;1448:95–106.

    Article  CAS  Google Scholar 

  6. Zakhartchenko V, Flisikowska T, Li S, Richter T, Wieland H, Durkovic M, et al. Cell-mediated transgenesis in rabbits: chimeric and nuclear transfer animals. Biol Reprod. 2011;84:229–37.

    Article  CAS  Google Scholar 

  7. Filipiak WE, Hughes ED, Gavrilina GB, LaForest AK, Saunders TL. Next generation transgenic rat model production. Methods Mol Biol. 2019;2018:97–114.

    Article  CAS  Google Scholar 

  8. Pellenz S, Phelps M, Tang W, Hovde BT, Sinit RB, Fu W, et al. New human chromosomal sites with “Safe Harbor” potential for targeted transgene insertion. Hum Gene Ther. 2019;30:814–28.

    Article  CAS  Google Scholar 

  9. Kolli N, Lu M, Maiti P, Rossignol J, Dunbar GL. Application of the gene editing tool, CRISPR-Cas9, for treating neurodegenerative diseases. Neurochem Int. 2018;112:187–96.

    Article  CAS  Google Scholar 

  10. Guo T, Feng YL, Xiao JJ, Liu Q, Sun XN, Xiang JF, et al. Harnessing accurate non-homologous end joining for efficient precise deletion in CRISPR/Cas9-mediated genome editing. Genome Biol. 2018;19:170.

    Article  Google Scholar 

  11. Tozaki T, Ohnuma A, Takasu M, Kikuchi M, Kakoi H, Hirota KI, et al. Droplet digital PCR detection of the erythropoietin transgene from horse plasma and urine for gene-doping control. Genes. 2019;10:243.

    Article  CAS  Google Scholar 

  12. Tozaki T, Gamo S, Takasu M, Kikuchi M, Kakoi H, Hirota KI, et al. Digital PCR detection of plasmid DNA administered to the skeletal muscle of a microminipig: a model case study for gene doping detection. BMC Res Notes. 2018;11:708.

    Article  CAS  Google Scholar 

  13. Aoki K, Sugasawa T, Yanazawa K, Watanabe K, Takemasa T, Takeuchi Y, et al. The detection of trans gene fragments of hEPO in gene doping model mice by Taqman qPCR assay. PeerJ. 2020;8:e8595.

    Article  Google Scholar 

  14. Haughan J, Jiang Z, Stefanovski D, Moss KL, Ortved KF, Robinson MA. Detection of intra-articular gene therapy in horses using quantitative real time PCR in synovial fluid and plasma. Drug Test Anal. 2020. https://doi.org/10.1002/dta.2785.

  15. Zhang JJ, Xu JF, Shen YW, Ma SJ, Zhang TT, Meng QL, et al. Detection of exogenous gene doping of IGF-I by a real-time quantitative PCR assay. Biotechnol Appl Biochem. 2017;64:549–54.

    Article  CAS  Google Scholar 

  16. Neuberger EW, Perez I, Le Guiner C, Moser D, Ehlert T, Allais M, et al. Establishment of two quantitative nested qPCR assays targeting the human EPO transgene. Gene Ther. 2016;23:330–9.

    Article  CAS  Google Scholar 

  17. Baoutina A, Coldham T, Fuller B, Emslie KR. Improved detection of transgene and nonviral vectors in blood. Hum Gene Ther Methods. 2013;24:345–54.

    Article  CAS  Google Scholar 

  18. Tozaki T, Kikuchi M, Kakoi H, Hirota KI, Nagat SI. A genome-wide association study for body weight in Japanese Thoroughbred racehorses clarifies candidate regions on chromosomes 3, 9, 15, and 18. J Equine Sci. 2017;28:127–34.

    Article  CAS  Google Scholar 

  19. Gu J, MacHugh DE, McGivney BA, Park SD, Katz LM, Hill EW. Association of sequence variants in CKM (creatine kinase, muscle) and COX4I2 (cytochrome c oxidase, subunit 4, isoform 2) genes with racing performance in Thoroughbred horses. Equine Vet J Suppl. 2010;38:569–75.

    Article  Google Scholar 

  20. Hill EW, Gu J, Eivers SS, Fonseca RG, McGivney BA, Govindarajan P, et al. A sequence polymorphism in MSTN predicts sprinting ability and racing stamina in thoroughbred horses. PLoS ONE. 2010;5:8645.

    Article  Google Scholar 

  21. Tozaki T, Miyake T, Kakoi H, Gawahara H, Sugita S, Hasegawa T, et al. A genome-wide association study for racing performances in Thoroughbreds clarifies a candidate region near the MSTN gene. Anim Genet. 2010;41(Suppl 2):28–35.

    Article  CAS  Google Scholar 

  22. Tozaki T, Ohnuma A, Kikuchi M, Ishige T, Kakoi H, Hirota KI, et al. Microfluidic quantitative PCR detection of 12 transgenes from horse plasma for gene-doping control. Genes. 2020;11:457.

    Article  CAS  Google Scholar 

  23. Arboleda VA, Xian RR. An overview of DNA analytical methods. Methods Mol Biol. 2019;1897:385–402.

    Article  CAS  Google Scholar 

  24. Yan Y, Yi G, Sun C, Qu L, Yang N. Genome-wide characterization of insertion and deletion variation in chicken using next generation sequencing. PLoS ONE. 2014;9:104652.

    Article  Google Scholar 

  25. Neerman N, Faust G, Meeks N, Modai S, Kalfon L, Falik-Zaccai T, et al. A clinically validated whole genome pipeline for structural variant detection and analysis. BMC Genomics. 2019;20(Suppl 8):545.

    Article  Google Scholar 

  26. Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, et al. Genome sequence, comparative analysis, and population genetics of the domestic horse. Science. 2009;326:865–7.

    Article  CAS  Google Scholar 

  27. de Boer EN, van der Wouden PE, Johansson LF, van Diemen CC, Haisma HJ. A next-generation sequencing method for gene doping detection that distinguishes low levels of plasmid DNA against a background of genomic DNA. Gene Ther. 2019;26:338–46.

    Article  Google Scholar 

  28. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.

    Article  CAS  Google Scholar 

  29. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. The ensembl variant effect predictor. Genome Biol. 2016;17:122.

    Article  Google Scholar 

  30. Rausch T, Zichner T, Schlattl A, Stütz AM, Benes V, Korbel JO. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics. 2012;28:333–9.

    Article  Google Scholar 

  31. Boeva V, Popova T, Bleakley K, Chiche P, Cappo J, Schleiermacher G, et al. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics. 2012;28:423–5.

    Article  CAS  Google Scholar 

  32. Kosugi S, Momozawa Y, Liu X, Terao C, Kubo M, Kamatani Y. Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing. Genome Biol. 2019;20:117.

    Article  Google Scholar 

  33. Kosugi S, Momozawa Y, Liu X, Terao C, Kubo M, Kamatani Y. Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing. Genome Biol. 2019;20:117.

    Article  Google Scholar 

  34. Ito T, Kawashima Y, Fujikawa T, Honda K, Makabe A, Kitamura K, et al. Rapid screening of copy number variations in STRC by droplet digital PCR in patients with mild-to-moderate hearing loss. Hum Genome Var. 2019;6:41.

    Article  CAS  Google Scholar 

  35. International Stud Book Committee, Statistical Information Booklet, 2019:6–7. http://internationalstudbook.com/wp-content/uploads/2019/11/ISBC-Information-Booklet-2019-2016-to-2018-statistics.pdf.

Download references

Acknowledgements

We acknowledge Yuko Miyatake and Miyuki Kubokawa at Amelieff Corporation, Japan, for providing technical support and helpful discussions. We also thank Noriko Tanaka for her assistance with this study and the Japan Racing Association (JRA) for approving and supporting this study through a grant-in-aid (2017–2019).

Funding

This research was funded by the Japan Racing Association (2017–2019).

Author information

Author notes

  1. These authors contributed equally: Teruaki Tozaki, Aoi Ohnuma

Authors and Affiliations

  1. Genetic Analysis Department, Laboratory of Racing Chemistry, 1731-2, Tsurutamachi, Utsunomiya, Tochigi, 320-0851, Japan

    Teruaki Tozaki, Aoi Ohnuma, Mio Kikuchi, Taichiro Ishige, Hironaga Kakoi, Kei-ichi Hirora & Shun-ichi Nagata

  2. Department of Veterinary Medicine, Faculty of Applied Biological Sciences, Gifu University, 1-1, Yanagido, Gifu, Gifu, 501-1193, Japan

    Masaki Takasu & Kotono Nakamura

  3. Equine Research Institute, Japan Racing Association, 1400-4, Shiba, Shimotsuke, Tochigi, 329-0412, Japan

    Norihisa Tamura

  4. Equine Department, Japan Racing Association, 6-11-1, Roppongi, Minato, Tokyo, 106-8401, Japan

    Kanichi Kusano

Authors
  1. Teruaki Tozaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aoi Ohnuma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masaki Takasu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kotono Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mio Kikuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Taichiro Ishige
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hironaga Kakoi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kei-ichi Hirora
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Norihisa Tamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kanichi Kusano
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Shun-ichi Nagata
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

TT and AO conceived and designed the experiments; TT and AO performed the experiments; MK, TI, HK, KH; KK and SN performed the animal experiments; MT, KN, and NT generated transgenic cells; KK provided critical comments and contributed to the discussion of the results and TT and AO drafted the paper. All authors reviewed and revised the paper.

Corresponding author

Correspondence to Teruaki Tozaki.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Animal experiments in this study were approved by the Animal Care Committee of the Equine Research Institute of the Japan Racing Association (approval number 19-9 on January 31, 2019). Animal experiments were conducted at the facilities of the Equine Research Institute, Japan Racing Association (Shimotsuke, Japan).

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tozaki, T., Ohnuma, A., Takasu, M. et al. Detection of non-targeted transgenes by whole-genome resequencing for gene-doping control. Gene Ther 28, 199–205 (2021). https://doi.org/10.1038/s41434-020-00185-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41434-020-00185-y

This article is cited by

Search

Advanced search

Quick links