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Functional interrogation and mining of natively paired human VH:VL antibody repertoires

Abstract

We present a technology to screen millions of B cells for natively paired human antibody repertoires. Libraries of natively paired, variable region heavy and light (VH:VL) amplicons are expressed in a yeast display platform that is optimized for human Fab surface expression. Using our method we identify HIV-1 broadly neutralizing antibodies (bNAbs) from an HIV-1 slow progressor and high-affinity neutralizing antibodies against Ebola virus glycoprotein and influenza hemagglutinin.

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Figure 1: High-throughput cloning, yeast display, and functional analysis of the human natively paired VH:VL antibody repertoire.
Figure 2: Examples of natively paired antibody repertoire analysis and functional characterization.

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GenBank/EMBL/DDBJ

Sequence Read Archive

References

  1. Chan, A.C. & Carter, P.J. Nat. Rev. Immunol. 10, 301–316 (2010).

    Article  CAS  Google Scholar 

  2. Brekke, O.H. & Sandlie, I. Nat. Rev. Drug Discov. 2, 52–62 (2003).

    Article  CAS  Google Scholar 

  3. Corti, D. & Lanzavecchia, A. Annu. Rev. Immunol. 31, 705–742 (2013).

    Article  CAS  Google Scholar 

  4. Burton, D.R. & Hangartner, L. Annu. Rev. Immunol. 34, 635–659 (2016).

    Article  CAS  Google Scholar 

  5. Bradbury, A.R.M., Sidhu, S., Dübel, S. & McCafferty, J. Nat. Biotechnol. 29, 245–254 (2011).

    Article  CAS  Google Scholar 

  6. Ecker, D.M., Jones, S.D. & Levine, H.L. MAbs 7, 9–14 (2015).

    Article  CAS  Google Scholar 

  7. Wilson, P.C. & Andrews, S.F. Nat. Rev. Immunol. 12, 709–719 (2012).

    Article  CAS  Google Scholar 

  8. Georgiou, G. et al. Nat. Biotechnol. 32, 158–168 (2014).

    Article  CAS  Google Scholar 

  9. Jayaram, N., Bhowmick, P. & Martin, A.C.R. Protein Eng. Des. Sel. 25, 523–529 (2012).

    Article  CAS  Google Scholar 

  10. Ponsel, D., Neugebauer, J., Ladetzki-Baehs, K. & Tissot, K. Molecules 16, 3675–3700 (2011).

    Article  CAS  Google Scholar 

  11. DeKosky, B.J. et al. Nat. Med. 21, 86–91 (2015).

    Article  CAS  Google Scholar 

  12. McDaniel, J.R., DeKosky, B.J., Tanno, H., Ellington, A.D. & Georgiou, G. Nat. Protoc. 11, 429–442 (2016).

    Article  CAS  Google Scholar 

  13. Spadiut, O., Capone, S., Krainer, F., Glieder, A. & Herwig, C. Trends Biotechnol. 32, 54–60 (2014).

    Article  CAS  Google Scholar 

  14. Bowley, D.R., Labrijn, A.F., Zwick, M.B. & Burton, D.R. Protein Eng. Des. Sel. 20, 81–90 (2007).

    Article  CAS  Google Scholar 

  15. Feldhaus, M.J. et al. Nat. Biotechnol. 21, 163–170 (2003).

    Article  CAS  Google Scholar 

  16. Lee, J. et al. Nat. Med. 22, 1456–1464 (2016).

    Article  CAS  Google Scholar 

  17. Wentz, A.E. & Shusta, E.V. Appl. Environ. Microbiol. 73, 1189–1198 (2007).

    Article  CAS  Google Scholar 

  18. Ojima-Kato, T. et al. Protein Eng. Des. Sel. 29, 149–157 (2016).

    Article  CAS  Google Scholar 

  19. Kong, R. et al. Science 352, 828–833 (2016).

    Article  CAS  Google Scholar 

  20. Stanley, D.A. et al. Nat. Med. 20, 1126–1129 (2014).

    Article  CAS  Google Scholar 

  21. Maruyama, T. et al. J. Virol. 73, 6024–6030 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Tian, M. et al. Cell 166, 1471–1484. e18 (2016).

    Article  CAS  Google Scholar 

  23. Joyce, M.G. et al. Cell 166, 609–623 (2016).

    Article  CAS  Google Scholar 

  24. Moody, M.A. et al. PLoS One 6, e25797 (2011).

    Article  CAS  Google Scholar 

  25. Pinna, D., Corti, D., Jarrossay, D., Sallusto, F. & Lanzavecchia, A. Eur. J. Immunol. 39, 1260–1270 (2009).

    Article  CAS  Google Scholar 

  26. Whittle, J.R. et al. J. Virol. 88, 4047–4057 (2014).

    Article  Google Scholar 

  27. Kanekiyo, M. et al. Cell 162, 1090–1100 (2015).

    Article  CAS  Google Scholar 

  28. Tillotson, B.J., Cho, Y.K. & Shusta, E.V. Methods 60, 27–37 (2013).

    Article  CAS  Google Scholar 

  29. Wang, X.X., Cho, Y.K. & Shusta, E.V. Nat. Methods 4, 143–145 (2007).

    Article  CAS  Google Scholar 

  30. Fang, Y., Chu, T.H., Ackerman, M.E. & Griswold, K.E. MAbs 9, 1253–1261 (2017).

    Article  CAS  Google Scholar 

  31. Côté, M. et al. Nature 477, 344–348 (2011).

    Article  Google Scholar 

  32. Misasi, J. et al. Science 351, 1343–1346 (2016).

    Article  CAS  Google Scholar 

  33. Whittle, J.R.R. et al. Proc. Natl. Acad. Sci. USA 108, 14216–14221 (2011).

    Article  CAS  Google Scholar 

  34. Lee, J.E. et al. Nature 454, 177–182 (2008).

    Article  CAS  Google Scholar 

  35. Olinger, G.G. Jr. et al. Proc. Natl. Acad. Sci. USA 109, 18030–18035 (2012).

    Article  CAS  Google Scholar 

  36. Lavinder, J.J. et al. Proc. Natl. Acad. Sci. USA 111, 2259–2264 (2014).

    Article  CAS  Google Scholar 

  37. Doria-Rose, N.A. et al. J. Virol. 83, 188–199 (2009).

    Article  CAS  Google Scholar 

  38. Doria-Rose, N.A. et al. J. Virol. 84, 1631–1636 (2010).

    Article  CAS  Google Scholar 

  39. Benatuil, L., Perez, J.M., Belk, J. & Hsieh, C.M. Protein Eng. Des. Sel. 23, 155–159 (2010).

    Article  CAS  Google Scholar 

  40. Reich, L.L., Dutta, S. & Keating, A.E. J. Mol. Biol. 427, 2135–2150 (2015).

    Article  CAS  Google Scholar 

  41. Wang, B. et al. MAbs 8, 1035–1044 (2016).

    Article  CAS  Google Scholar 

  42. DeKosky, B.J. et al. Proc. Natl. Acad. Sci. USA 113, E2636–E2645 (2016).

    Article  CAS  Google Scholar 

  43. Cale, E.M. et al. Immunity 46, 777–791. e10 (2017).

    Article  CAS  Google Scholar 

  44. Whitehead, T.A. et al. Nat. Biotechnol. 30, 543–548 (2012).

    Article  CAS  Google Scholar 

  45. DeKosky, B.J. et al. Nat. Biotechnol. 31, 166–169 (2013).

    Article  CAS  Google Scholar 

  46. Wang, B. et al. Sci. Rep. 5, 13926 (2015).

    Article  Google Scholar 

  47. Wu, X. et al. Science 329, 856–861 (2010).

    Article  CAS  Google Scholar 

  48. Sullivan, N.J. et al. PLoS Med. 3, 0865–0873 (2006).

    CAS  Google Scholar 

  49. Yang, Z.Y. et al. J. Virol. 78, 4029–4036 (2004).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge B. Hartman for assistance with figures, N. Doria-Rose, G. Ippolito, and M. Kanekiyo for advice and guidance, S. Lucas, and K. Zhou for help with experiments, S. Darko for assistance with data processing, and E. Shusta and S. Harrison for kindly sharing reagents and research tools. This work was funded in part by the intramural research program of the Vaccine Research Center, NIAID, NIH, NIH grant DP5OD023118-01 to B.J.D., NIH grant 5R21CA191239-01 to A.D.E., Leidos Biomedical Research Inc. contract 15X219, DTRA contract HDTRA1-12-C-0105, and NIH grant 1R56AI106006 to G.G.

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Authors and Affiliations

Authors

Contributions

B.W., B.J.D., J.R. Mascola, and G.G. conceived the study and designed the experiments. B.W. and B.J.D. conducted the experiments with help from M.R.T., J.L., E.N., J.M., R.K., J.R. McDaniel, G.D., K.E.L., T.N., C.W.C., E.G.V., A.F., A.C., A.P., K.L., E.S.Y., W.-P.K., W.N.V., A.G.S., M.A.M., D.R.A., A.R.H., F.L., J.E.L., B.S.G., M.C., and D.C.D. N.J.S, A.D.E., J.R. Mascola, and G.G. supervised the study. B.W., B.J.D., J.R. Mascola, and G.G. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to John R Mascola or George Georgiou.

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Competing interests

Competing financial interests statement G.G., B.J.D., and A.D.E. declare competing financial interests in the form of a patent filed by the University of Texas at Austin.

Integrated supplementary information

Supplementary Figure 1 Map of pCT-VHVL-K1 native VH:VL display vector.

Natively-paired VH:VL sequences are cloned en masse into this vector for human antibody repertoire mining, and their corresponding Fabs are expressed on the yeast cell surface via galactose induction.

Supplementary Figure 2 Flow cytometry analysis of a panel of human anti-HA antibodies before and after display optimization, and of the 2 anti-EBOV antibodies and 1 anti-HIV-1 bNAb in the optimized system.

(a) Display of the six anti-HA antibodies listed in Figure 1c that did not functionally display in EBY100 (upper) and in AWY101 with LZ-forced Fab dimerization (lower). (b) Anti-EBOV antibodies c13c6 and KZ52 and anti-HIV-1 bNAb VRC34.01 displayed in the optimized system. For anti-HA antibodies, 100nM recombinant A/California/07/2009 HA was used to stain D1 H1-2, D1 H1-3/H3-3, D1 H1-53, D1 H1-12, and D1 H1-17/H3-14, and 100nM recombinant B/Brisbane/60/2008 HA was used to stain D1 Vic-8/Yama-20. 23 nM GPΔmuc-APC was used to stain c13c6 and KZ52; 50nM VRC34-epitope scaffold-FP-APC was used to stain VRC34.01. A representative profile from 5 (a) or 3 (b) independent experiments for each antibody is shown.

Supplementary Figure 3 Representative FACS gating strategy for EBOV library sorts.

Yeast cells were stained with 2 μg/ml anti-FLAG-FITC and 23 nM GPΔmuc-APC.

Supplementary Figure 4 Flow cytometry antigen binding profiles of monoclonal yeast populations expressing EBOV.YD.09-EBOV.YD.11, which were identified by single colony picking.

Yeast cells were stained with 2 μg/ml anti-FLAG-FITC and 23 nM GPΔmuc-APC.

Supplementary Figure 5 Biolayer interferometry response curves for human anti-EBOV antibodies from the plasmablast cognate VH:VL repertoire.

Binding was assessed against GPΔmuc. Global analyses were carried out using nonlinear least-squares fitting allowing a single set of binding parameters to be obtained simultaneously for all concentrations used in each experiment.

Supplementary Figure 6 Neutralization of EBOV GP pseudotype infection by human anti-EBOV antibodies.

% Infection is shown relative to the negative control antibody VRC01. Data are reported as average ± standard deviation for three technical replicates.

Supplementary Figure 7 Representative FACS gating strategy for HIV-1 library sorts.

Yeast cells were stained with 2 μg/ml anti-FLAG-FITC, 50 nM VRC34-epitope scaffold-FP-APC, and 50 nM VRC34-epitope scaffold-KO-PE for the isolation of HIV-1 fusion peptide-specific antibodies.

Supplementary Figure 8 Biolayer interferometry response curves for HIV-1 FP-specific antibodies from the B cell repertoire of an HIV-1 slow progressor.

The FP-scaffold protein was immobilized on the biosensor chip. Global analyses were carried out using nonlinear least-squares fitting allowing a single set of binding parameters to be obtained simultaneously for all concentrations used in each experiment.

Supplementary Figure 9 HIV-1 neutralization IC50 potency for natively paired VRC34 family antibodies discovered via yeast display.

Neutralization was determined against a panel of 22 viruses.

Supplementary Figure 10 Sequence alignments of HIV-1 FP-specific antibodies from the peripheral B cell repertoire of donor N123.

Mutations are colored in red.

Supplementary Figure 11 Representative FACS gating strategy for flu library screening.

Yeast cells were stained with 2 μg/ml anti-FLAG-FITC, and either 40nM A/Solomon Islands/3/2006 H1 HA (upper panel) or 40nM A/Wisconsin/67/2005 H3 HA (lower panel) for the isolation of H1 and H3-specific antibodies, respectively.

Supplementary Figure 12 Surface plasmon resonance binding curves for anti-HA antibodies from the B cell cognate VH:VL repertoire.

Representative binding curves from 3 independent experiments for each antibody are shown.

Supplementary Figure 13 Neutralization profiles of anti-HA antibodies isolated via yeast display.

CR9114 and CR6261 were included as positive and negative controls, respectively. Data are reported as average ± standard deviation from three technical replicates.

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Wang, B., DeKosky, B., Timm, M. et al. Functional interrogation and mining of natively paired human VH:VL antibody repertoires. Nat Biotechnol 36, 152–155 (2018). https://doi.org/10.1038/nbt.4052

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