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.

  • Article
  • Published:

Long-range PCR libraries and next-generation sequencing for pharmacogenetic studies of patients treated with anti-TNF drugs

Abstract

Biological therapy with anti-tumor necrosis factor-α (anti-TNF-α) monoclonal antibodies significantly increased the effectiveness of autoimmune disease treatment compared with conventional medicines. However, anti-TNF-α drugs are relatively expensive and a response to the therapy is reported in only 60–70% of patients. Moreover, in up to 5% of patients adverse drug reactions occur. The various effects of biological treatment may be a potential consequence of interindividual genetic variability. Only a few studies have been conducted in this field and which refer to single gene loci. Our aim was to design and optimize a methodology for a broader application of pharmacogenetic studies in patients undergoing anti-TNF-α treatment. Based on the current knowledge, we selected 16 candidate genes: TNFRSF1A, TNFRSF1B, ADAM17, CASP9, FCGR3A, LTA, TNF, FAS, IL1B, IL17A, IL6, MMP1, MMP3, S100A8, S100A9, and S100A12, which are potentially involved in the response to anti-TNF-α therapy. As a research model, three DNA samples from Crohn’s disease (CD) patients were used. Targeted genomic regions were amplified in 23 long-range (LR) PCR reactions and after enzymatic fragmentation amplicon libraries were prepared and analyzed by next-generation sequencing (NGS). Our results indicated 592 sequence variations located in all fragments with coverage range of 5–1089. We demonstrate a highly sensitive, flexible, rapid, and economical approach to the pharmacogenetic investigation of anti-TNF-α therapy using amplicon libraries and NGS technology.

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

Access options

Buy this article

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Atzeni F, Talotta R, Salaffi F, Cassinotti A, Varisco V, Battellino M, et al. Immunogenicity and autoimmunity during anti-TNF therapy. Autoimmun Rev. 2013;12:703–8.

    Article  CAS  Google Scholar 

  2. Roberts RL, Barclay ML. Current relevance of pharmacogenetics in immunomodulation treatment for Crohn’s disease. J Gastroenterol Hepatol. 2012;27:1546–54.

    Article  CAS  Google Scholar 

  3. Schnitzler F, Fidder H, Ferrante M, Noman M, Arijs I, Van Assche G, et al. Long-term outcome of treatment with infliximab in 614 patients with Crohn’s disease: results from a single-centre cohort. Gut. 2009;58:492–500.

    Article  CAS  Google Scholar 

  4. Altwegg R, Vincent T. TNF blocking therapies and immunomonitoring in patients with inflammatory bowel disease. Mediat Inflamm. 2014;2014:e172821. https://doi.org/10.1155/2014/172821

    Article  CAS  Google Scholar 

  5. Siegel CA, Melmed GY. Predicting response to anti-TNF agents for the treatment of Crohn’s disease. Ther Adv Gastroenterol. 2009;2:245–51.

    Article  Google Scholar 

  6. Prieto-Pérez R, Almoguera B, Cabaleiro T, Hakonarson H, Abad-Santos F. Association between genetic polymorphisms and response to anti-TNFs in patients with inflammatory bowel disease. Int J Mol Sci. 2016;17:e225 https://doi.org/10.3390/ijms17020225

    Article  CAS  PubMed  Google Scholar 

  7. Horiuchi T, Mitoma H, Harashima SI, Tsukamoto H, Shimoda T. Transmembrane TNF alpha: structure, function and interaction with anti-TNF agents. Rheumatology. 2010;49:1215–28.

    Article  CAS  Google Scholar 

  8. Taylor KD, Plevy SE, Yang H, Landers CJ, Barry MJ, Rotter JI, et al. ANCA pattern and LTA haplotype relationship to clinical responses to anti-TNF antibody treatment in Crohn’s disease. Gastroenterology. 2001;120:1347–55.

    Article  CAS  Google Scholar 

  9. Hlavaty T, Pierik M, Henckaerts L, Ferrante M, Joossens S, van Schuerbeek N, et al. Polymorphisms in apoptosis genes predict response to infliximab therapy in luminal and fistulizing Crohn’s disease. Aliment Pharmacol Ther. 2005;22:613–26.

    Article  CAS  Google Scholar 

  10. Leal RF, Planell N, Kajekar R, Lozano JJ, Ordás I, Dotti I, et al. Identification of inflammatory mediators in patients with Crohn’s disease unresponsive to anti-TNFα therapy. Gut. 2015;64:233–42.

    Article  CAS  Google Scholar 

  11. Bek S, Nielsen JV, Bojesen AB, Franke A, Bank S, Vogel U, et al. Systematic review: genetic biomarkers associated with anti-TNF treatment response in inflammatory bowel diseases. Aliment Pharmacol Ther. 2016;44:554–67.

    Article  CAS  Google Scholar 

  12. de Sousa Dias M, Hernan I, Pascual B, Borràs E, Mañé B, Gamundi MJ, et al. Detection of novel mutations that cause autosomal dominant retinitis pigmentosa in candidate genes by long-range PCR amplification and next-generation sequencing. Mol Vis. 2013;19:654–64.

    PubMed  Google Scholar 

  13. Tan AY, Michaeel A, Liu G, Elemento O, Blumenfeld J, Donahue S, et al. Molecular diagnosis of autosomal dominant polycystic kidney disease using next-generation sequencing. J Mol Diagn. 2014;16:216–28.

    Article  CAS  Google Scholar 

  14. Profaizer T, Coonrod EM, Delgado JC, Kumánovics A. Report on the effects of fragment size, indexing, and read length on HLA sequencing on the Illumina MiSeq. Hum Immunol. 2015;76:897–902.

    Article  CAS  Google Scholar 

  15. Dames S, Chou LS, Xiao Y, Wayman T, Stocks J, Singleton M, et al. The development of next-generation sequencing assays for the mitochondrial genome and 108 nuclear genes associated with mitochondrial disorders. J Mol Diagn. 2013;15:526–34.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  17. McKenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.

    Article  CAS  Google Scholar 

  18. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinform. 2013;43:11.10.1–33.

    Google Scholar 

  19. Ramos AH, Lichtenstein L, Gupta M, Lawrence MS, Pugh TJ, Saksena G, et al. Oncotator: cancer variant annotation tool. Hum Mutat. 2015;36:2423–9.

    Article  Google Scholar 

  20. Hernan I, Borràs E, de Sousa Dias M, Gamundi MJ, Mañé B, Llort G, et al. Detection of genomic variations in BRCA1 and BRCA2 genes by long-range PCR and next-generation sequencing. J Mol Diagn. 2012;14:286–93.

    Article  CAS  Google Scholar 

  21. Ozcelik H, Shi X, Chang MC, Tram E, Vlasschaert M, Di Nicola N, et al. Long-range PCR and next-generation sequencing of BRCA1 and BRCA2 in breast cancer. J Mol Diagn. 2012;14:467–75.

    Article  CAS  Google Scholar 

  22. Jia H, Guo Y, Zhao W, Wang K. Long-range PCR in next-generation sequencing: comparison of six enzymes and evaluation on the MiSeq sequencer. Sci Rep. 2014;4:e5737. https://doi.org/10.1038/srep05737

    Article  CAS  Google Scholar 

  23. Su Y, Lin L, Tian G, Chen C, Liu T, Xu X, et al. Preparing a re-sequencing DNA library of 2 cancer candidate genes using the ligation-by-amplification protocol by two PCR reactions. Sci China C Life Sci. 2009;52:483–91.

    Article  CAS  Google Scholar 

  24. Prieto-Pérez R, Cabaleiro T, Daudén E, Ochoa D, Roman M, Abad-Santos F. Genetics of psoriasis and pharmacogenetics of biological drugs. Autoimmune Dis. 2013;2013:e613086. https://doi.org/10.1155/2013/613086

    Article  CAS  Google Scholar 

  25. Dideberg V, Théâtre E, Farnir F, Vermeire S, Rutgeerts P, De Vos M, et al. The TNF/ADAM 17 system: implication of an ADAM 17 haplotype in the clinical response to infliximab in Crohn’s disease. Pharm Genom. 2006;16:727–34.

    Article  CAS  Google Scholar 

  26. Ternant D, Berkane Z, Picon L, Gouilleux-Gruart V, Colombel JF, Allez M, et al. Assessment of the influence of inflammation and FCGR3A genotype on infliximab pharmacokinetics and time to relapse in patients with Crohn’s disease. Clin Pharmacokinet. 2015;54:551–62.

    Article  CAS  Google Scholar 

  27. Mascheretti S, Hampe J, Kühbacher T, Herfarth H, Krawczak M, Fölsch UR, et al. Pharmacogenetic investigation of the TNF/TNF-receptor system in patients with chronic active Crohn’s disease treated with infliximab. Pharm J. 2002;2:127–36.

    CAS  Google Scholar 

  28. Mu W, Lu HM, Chen J, Li S, Elliott AM. Sanger confirmation is required to achieve optimal sensitivity and specificity in next- generation sequencing panel testing. J Mol Diagn. 2016;18:923–32.

    Article  CAS  Google Scholar 

  29. Linares-Pineda TM, Cañadas-Garre M, Sánchez-Pozo A, Calleja-Hernández MÁ. Pharmacogenetic biomarkers of response in Crohn’s disease. Pharm J. 2018;18:1–13.

    CAS  Google Scholar 

  30. Rufini S, Ciccacci C, Novelli G, Borgiani P. Pharmacogenetics of inflammatory bowel disease: a focus on Crohn’s disease. Pharmacogenomics. 2017;18:1095–114.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the Foundation for the Development of Biotechnology and Genetics POLBIOGEN. S-EK is recipient of a fellowship for young researcher from Poznan Medical University (Poland) grant no 502–14–02223359–10715.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marzena Skrzypczak-Zielinska.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Walczak, M., Skrzypczak-Zielinska, M., Plucinska, M. et al. Long-range PCR libraries and next-generation sequencing for pharmacogenetic studies of patients treated with anti-TNF drugs. Pharmacogenomics J 19, 358–367 (2019). https://doi.org/10.1038/s41397-018-0058-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41397-018-0058-9

This article is cited by

Search

Quick links