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Cited by 484vaccines and immunoinformatics approachesTuberculosis Research and EpidemiologyT-cell and B-cell Immunology

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

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Huang, Huang, Wang, Chunlin, Rubelt, Florian, Scriba, Thomas J., Davis, Mark M.·2020·Nature Biotechnology
DOI 10.1038/s41587-020-0505-4PMC PMC7541396
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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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Authors
Huang, H., Wang, C., Rubelt, F., Scriba, T. J., & Davis, M. M.
Affiliations
  • Stanford University (US)
  • University of Cape Town (ZA)
  • Howard Hughes Medical Institute (US)
Corresponding author
Davis, Mark M.
ORCID
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2020-04-27
Volume
38
Issue
10
Pages
1194-1202
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
DOI
10.1038/s41587-020-0505-4
PMC
PMC7541396
OpenAlex
W3020331344
Cited by
484
References
5
Citations by year
2026: 65 · 2025: 88 · 2024: 107 · 2023: 90 · 2022: 54 · 2021: 63 · 2020: 17
FWCI
18.33
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Biotechnology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Bill and Melinda Gates Foundation (OPP1113682)
  • Howard Hughes Medical Institute
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
  • Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
  • Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
Keywords
Mycobacterium tuberculosis, T cell epitopes, HLA restriction, latent tuberculosis infection, PPE proteins, antigen-presenting cells, T cell immunity
MeSH terms
Alleles, Amino Acid Sequence, HLA Antigens, Humans, Mycobacterium tuberculosis, Receptors, Antigen, T-Cell, Software, CD4-Positive T-Lymphocytes, Cluster Analysis, Epitopes, T-Lymphocyte

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