Antigen discovery and specification of immunodominance hierarchies for MHCII-restricted epitopes

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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Fig. 1: MHCII peptidomics in primary murine dendritic cells results in more than 3,700 distinct peptide identifications and defines the I-Ab-binding motif.
Fig. 2: Antigen processing pathways and epitope features revealed by MHCII peptidomics.
Fig. 3: Validation of BOTA epitope predictions with MHCII peptidomics.
Fig. 4: BOTA and MHCII peptidomics accurately predict immunodominance in vivo.
Fig. 5: Single-cell RNA-seq integrates T cell phenotype with TCR repertoire in the Listeria response.
Fig. 6: Computational prediction and validation of a dominant commensal antigen.

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.

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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.

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

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 and Supplementary Table 2

Reporting Summary

Supplementary Table 1

MHCII peptidomics

Supplementary Table 3

TCR pairing from Listeria-infected mice

Supplementary Table 4

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

Supplementary Table 5

16S rRNA sequencing from SICC-seq

Supplementary Table 6

TCR-seq and 5ʹ-DGE oligonucleotides

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Graham, D.B., Luo, C., O’Connell, D.J. et al. Antigen discovery and specification of immunodominance hierarchies for MHCII-restricted epitopes. Nat Med 24, 1762–1772 (2018). https://doi.org/10.1038/s41591-018-0203-7

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