The quest for the needle (antigen) in the haystack (pathogen): Immunoinformatics to the rescue

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Presented by Nicholas Svitek, Lucilla Steinaa, Rosemary Saya, Elias Awino, Morten Nielsen, Søren Buus, Vishvanath Nene at the ILRI BioSciences Day, Nairobi, 27 November 2013


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The quest for the needle (antigen) in the haystack (pathogen): Immunoinformatics to the rescue

  1. 1. The quest for the needle (antigen) in the haystack (pathogen): Immunoinformatics to the rescue Nicholas Svitek, Lucilla Steinaa, Rosemary Saya, Elias Awino, Morten Nielsen, Søren Buus, Vishvanath Nene ILRI BioSciences Day, Nairobi, 27 November 2013
  2. 2. Immune Response Elicited during Theileria parva Infection: Cellular Immunology 101 T. parva infected white blood cells T. parva T. parva peptides The butler T CD8+= CTL (killers of infected cells) The butler (the MHC class I molecule) is serving T. parva peptides to the “Killer T cell” Killing
  3. 3. The Challenge: Many Locks & Keys Are Available and these need to be Tracked Down CTL need to find infected cells through a lock-key mechanism T. parva T. parva T. parva peptides The butler T. parva peptides The butler
  4. 4. The Challenge: Searching for an Antigen is like Searching for a Needle in a Haystack
  5. 5. The Problem: using Conventional Methods for Ag ID are Expensive & Time Consuming Infection & Treatment Method (ITM) Induction of lifelong CTL response Restricting BoLA class I molecule 214VGYPKVKEEML 224 Tp2 27SHEELKKLGML 37 3) 2*01201 Tp2 49KSSHGMGKVGK 59 4) BoLA-T2c Tp2 96FAQSLVCVL 104 5) BoLA-T5 Tp5 87SKADVIAKY 95 6) 3*00101 Tp8 379CGAELNHFL 387 7) 1*02301 Oxytetracycline Tp1 2) 6*04101 + CTL Epitope 1) 6*01301 Muguga cocktail T. parva antigen Tp9 199AKFPGMKKSK 208
  6. 6. Reverse Immunology as a Method to Speed Up Antigen Identification Whole Genome Sequence from T. parva In silico antigen predictions In vitro characterization of predicted antigens Computer algorithms trained on biological data Selection of immunodominant peptides Challenge with T. parva Vaccinate Naïve cattle
  7. 7. Reverse Immunology : the NetMHCpan Platform T. parva genome Prediction of parasite peptides that binds to bovine MHC class I molecules Immunoinformatics Computer algorithms trained on biological data (NetMHCpan)
  8. 8. Results 1: The True CTL Epitope that Binds to BoLA-6*04101 is Tp229-37 NetMHCpan: Antigen Binder Tp2 27SHEELKKLGML Tp2 29EELKKLGML 37 6*04101 NO (FP 0.058) 37 6*04101 YES (FP 0.002) Binding assay: 0.9 O.D. (@450 nm) 0.8 0.7 0.6 Tp2.29-37 (Alt) Tp229-37 0.5 Tp2.27-37 Tp2 27-37 0.4 0.3 0.2 0.1 0 [Peptide] Variants: SHEELKKLGML Var1 SDEELKKLGML Var2 SDDELDTLGML Var3 SNEELKKLGMV Var4 TNEELKKLGMV
  9. 9. Using peptide-MHC Class I Tetramers to Confirm CTL Specificity towards Predicted Epitope Tetramerization Recombinant MHC Production Incubation for 24-48 hours β2m CD8+/Tetramer + CD8+ CD8 http://flow.csc.mrc.ac.uk/?page_id=852
  10. 10. Results 2: Identification of Tp1 p-MHC Tetramer Positive Cells in Ex Vivo Assays Animal 1 Animal 2 Animal 3 Tp1+ (PE) Day 8 p.i. Day 15-17 p.i. CD8+ (PerCP)
  11. 11. Conclusion • Immunoinformatic (NetMHCpan) can potentially be used to identify new CTL epitopes. • p-MHC class I tetramers & NetMHCpan can be used for correct epitope mapping.
  12. 12. Where to from now? - Combining NetMHCpan with other prediction parameters; in collaboration with Dr. Morten Nielsen: • • • Amino acid composition Protein functional category Subcellular localization - T. parva comparative genomics to identify proteins that are under positive selection [potential targets of the immune response] ; in collaboration with Dr. Joana Silva. - Use of immunoinformatics for ASF virus antigen identification (Dr. Richard Bishop).
  13. 13. Acknowledgements Søren Buus, tetramers Morten Nielsen, NetMHCpan
  14. 14. Partners & Donors
  15. 15. better lives through livestock ilri.org The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is given to ILRI.

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