Abstract

Identifying immunodominant T cell epitopes remains a significant challenge in the context of infectious disease, autoimmunity, and immuno-oncology. To address the challenge of antigen discovery, we developed a quantitative proteomic approach that enabled unbiased identification of major histocompatibility complex class II (MHCII)–associated peptide epitopes and biochemical features of antigenicity. On the basis of these data, we trained a deep neural network model for genome-scale predictions of immunodominant MHCII-restricted epitopes. We named this model bacteria originated T cell antigen (BOTA) predictor. In validation studies, BOTA accurately predicted novel CD4 T cell epitopes derived from the model pathogen Listeria monocytogenes and the commensal microorganism Muribaculum intestinale. To conclusively define immunodominant T cell epitopes predicted by BOTA, we developed a high-throughput approach to screen DNA-encoded peptide–MHCII libraries for functional recognition by T cell receptors identified from single-cell RNA sequencing. Collectively, these studies provide a framework for defining the immunodominance landscape across a broad range of immune pathologies.

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Data availability

Source data are available for Figs. 1, 2, 5, and 7 and can be found in the Supplementary Information. There are no restrictions on source data availability. Data for Fig. 7 can be accessed through GEO accession GSE117166.

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Acknowledgements

We thank H. Vlamakis, T. Reimels, and I. Latorre for scientific input, J. Gracias for technical assistance, and P. Rogers for the FACS work. This work was supported by funding from The Leona M. and Harry B. Helmsley Charitable Trust, National Institutes of Health grants DK043351, AI109725, AT009708, and DK092405, and the Juvenile Diabetes Research Fund to R.J.X.

Author information

Author notes

  1. These authors contributed equally to this work: Daniel B. Graham, Chengwei Luo.

Affiliations

  1. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA

    • Daniel B. Graham
    • , Chengwei Luo
    • , Daniel J. O’Connell
    • , Ariel Lefkovith
    • , Eric M. Brown
    • , Moran Yassour
    • , Mukund Varma
    • , Jennifer G. Abelin
    • , Guadalupe J. Jasso
    • , Caline G. Matar
    • , Steven A. Carr
    •  & Ramnik J. Xavier
  2. Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    • Daniel B. Graham
    • , Chengwei Luo
    • , Kara L. Conway
    •  & Ramnik J. Xavier
  3. Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    • Daniel B. Graham
    •  & Ramnik J. Xavier
  4. Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA

    • Daniel B. Graham
    •  & Ramnik J. Xavier
  5. Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    • Chengwei Luo
    • , Kara L. Conway
    •  & Ramnik J. Xavier
  6. Immunology Program, Harvard Medical School, Boston, MA, USA

    • Guadalupe J. Jasso

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Contributions

D.B.G., C.L., and R.J.X. conceptualized the study. D.B.G., C.L., J.G.A., K.L.C., and S.A.C. constructed the study methodology. C.L. and M.Y. managed the software used in the study. C.L., M.V., and J.G.A. undertook the formal analysis of the data. D.B.G., J.G.A., C.G.M., A.L., G.J.J., E.M.B., D.J.O., and K.L.C. undertook the investigation. S.A.C. managed the resources. D.B.G. wrote the original manuscript draft. D.B.G. and R.J.X. supervised the study. R.J.X. acquired the funding for the study.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Daniel B. Graham or Ramnik J. Xavier.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–5 and Supplementary Table 2

  2. Reporting Summary

  3. Supplementary Table 1

    MHCII peptidomics

  4. Supplementary Table 3

    TCR pairing from Listeria-infected mice

  5. Supplementary Table 4

    Single-cell RNA-seq in T cells from Listeria-infected mice

  6. Supplementary Table 5

    16S rRNA sequencing from SICC-seq

  7. Supplementary Table 6

    TCR-seq and 5ʹ-DGE oligonucleotides

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DOI

https://doi.org/10.1038/s41591-018-0203-7