Journal
NATURE BIOTECHNOLOGY
Volume 38, Issue 10, Pages 1194-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41587-020-0505-4
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Funding
- Stanford Human Immune Monitoring Center
- Bill and Melinda Gates Foundation [OPP1113682]
- Howard Hughes Medical Institute
- Bill and Melinda Gates Foundation [OPP1113682] Funding Source: Bill and Melinda Gates Foundation
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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 TCR beta 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. The T-cell response to tuberculosis is examined by clustering T-cell receptor sequences to identify shared specificities, along with whole-genome antigen screening.
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