2. Academic Validation
  3. Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening

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

  • Nat Biotechnol. 2020 Oct;38(10):1194-1202. doi: 10.1038/s41587-020-0505-4.
Huang Huang # 1,  Chunlin Wang # 1,  Florian Rubelt 1,  Thomas J Scriba 2,  Mark M Davis 3 4 5
Affiliations

Affiliations

  • 1 Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA.
  • 2 South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa.
  • 3 Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA. [email protected].
  • 4 Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA. [email protected].
  • 5 The Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA. [email protected].
  • # Contributed equally.
PMID: 32341563 DOI: 10.1038/s41587-020-0505-4
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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