Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening

Abstract

CD4+ T cells are critical to fighting pathogens, but a comprehensive analysis of human T-cell specificities is hindered by the diversity of HLA alleles (>20,000) and the complexity of many pathogen genomes. We previously described GLIPH, an algorithm to cluster T-cell receptors (TCRs) that recognize the same epitope and to predict their HLA restriction, but this method loses efficiency and accuracy when >10,000 TCRs are analyzed. Here we describe an improved algorithm, GLIPH2, that can process millions of TCR sequences. We used GLIPH2 to analyze 19,044 unique TCRβ sequences from 58 individuals latently infected with Mycobacterium tuberculosis (Mtb) and to group them according to their specificity. To identify the epitopes targeted by clusters of Mtb-specific T cells, we carried out a screen of 3,724 distinct proteins covering 95% of Mtb protein-coding genes using artificial antigen-presenting cells (aAPCs) and reporter T cells. We found that at least five PPE (Pro-Pro-Glu) proteins are targets for T-cell recognition in Mtb.

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Fig. 1: The workflow of Mtb-specific T-cell repertoire and GLIPH2 analysis.
Fig. 2: A reporter system to efficiently screen protein antigen.
Fig. 3: Antigen discovery using proteome screening.
Fig. 4: Antigen discovery for T-cell receptor specificity group III.
Fig. 5: Antigen discovery for T-cell receptor specificity group I.
Fig. 6: The discrepancy between peptide and protein stimulation.

Data availability

The data supporting the findings of this study are available within the paper and in its Supplementary Information files.

Code availability

Two compiled standalone versions of GLIPH2 (Executable for MacOS ≥ 10.14.14 and Linux server Centos 7) are provided as Supplementary Code. Also, a web tool for GLIPH2 analysis is available at http://50.255.35.37:8080/.

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Acknowledgements

We would like to thank the Stanford Human Immune Monitoring Center for their high-throughput sequencing support for this project, M. Mindrinos and co-workers at Sirona Genomics for the HLA typing, S. Xue (Department of Immunology, University College London) for providing the Jurkat 76 T-cell line, J. Li for providing HLA-typed PBMCs, L. Chen and S. Chiou for valuable discussions regarding GLIPH2 optimization, H. Mahomed, W. Hanekom and members of the Adolescent Cohort Study (ACS) group for enrolment and follow-up of the Mtb-infected adolescents, R. DiFazio for help making the schematic overview and Y. Chien for constructive criticism of the manuscript, and J. Pavlovitch-Bedzyk for proofreading. This work was supported by the Bill and Melinda Gates Foundation (grant OPP1113682) and the Howard Hughes Medical Institute.

Author information

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Authors

Contributions

H.H., C.W. and M.M.D. conceptualized the study. H.H. performed the experiments with assistance from F.R. C.W. authored the codebase, upgraded the algorithm and performed its benchmark. T.J.S. provided PBMCs from Mtb-infected adolescents. F.R. provided bulk sequencing and bulk TCR analysis. H.H., C.W. and F.R. performed the analysis. H.H. and M.M.D. wrote the manuscript with input from all authors. M.M.D. supervised the study.

Corresponding author

Correspondence to Mark M. Davis.

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The authors declare no competing interests.

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Supplementary Information

Supplementary Information

Supplementary Figures 1–7

Reporting Summary

Supplementary Table 1

Mtb-specific TCR sequences and summary

Supplementary Table 2

TCR sequences from VDJdb

Supplementary Table 3

TCR specificity groups from GLIPH2 analysis

Supplementary Table 4

Gene list of the whole Mtb ORF clone set

Supplementary Code

Two compiled standalone versions of GLIPH2

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Huang, H., Wang, C., Rubelt, F. et al. Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0505-4

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