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SARS-CoV-2 infected cells present HLA-I peptides from canonical and out-of-frame ORFs

ABSTRACT: T cell-mediated immunity seems to play a critical role against SARS-CoV-2 infection and establishing protective memory. Yet the repertoire of viral epitopes responsible for activation of T cell responses remains mostly unknown. Identification and characterization of viral peptides presented on class I human leukocyte antigen (HLA-I) will reveal the viral signatures as seen by cytotoxic T cells that can then be harnessed for the development of effective vaccines. Here, we report the first HLA-I immunopeptidome of SARS-CoV-2 in two human cell lines at different times post-infection using mass spectrometry. We found HLA-I peptides derived not only from canonical ORFs, but also from internal out-of-frame ORFs in Spike and Nucleoprotein not currently captured by vaccine approaches. Whole proteome analysis revealed that expression of ubiquitination pathway proteins, and the proteasome maturation protein, POMP, were significantly altered in infected cells. Retrospective analysis of computational predictions highlighted shortcomings of in silico-only approaches in recovering the observed viral epitopes. Finally, given the experimental evidence for endogenous processing and presentation of the viral peptides we detected, we estimated that at least one HLA-A, -B, or -C allele is covered by at least one peptide for 99% of the population. These biological insights and the list of naturally presented SARS-CoV-2 peptides will facilitate data-driven selection of peptides for immune monitoring and vaccine development.

ORGANISM(S): Severe acute respiratory syndrome coronavirus 2 Homo sapiens

PROVIDER: GSE159191 | GEO | 2020/10/08

REPOSITORIES: GEO

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Project description:Next-generation T-cell-directed vaccines for COVID-19 aim to induce durable T-cell immunity against circulating and future hypermutated SARS-CoV-2 variants. Mass Spectrometry (MS)based immunopeptidomics holds promise for guiding vaccine design, but computational challenges impede the precise and unbiased identification of conserved T-cell epitopes crucial for vaccines against rapidly mutating viruses. We introduce a computational framework and analysis platform integrating a novel machine learning algorithm, immunopeptidomics, intra-host data, epitope immunogenicity, and geo-temporal CD8+ T-cell epitope conservation analyses. Central to our approach is MHCvalidator, a novel artificial neural network algorithm enhancing MS-based immunopeptidomics sensitivity by modeling antigen presentation and sequence features. MHCvalidator identified a novel nonconventional SARS-CoV-2 T-cell epitope presented by B7 supertype molecules, originating from a +1-frameshift in a truncated Spike (S) antigen, supported by ribo-seq data. Intra-host analysis of SARS-CoV-2 proteomes from ~100,000 COVID-19 patients revealed a prevalent S antigen truncation in ~51% of cases, exposing a rich source of frameshifted viral antigens. Our framework includes EpiTrack, a new computational pipeline tracking global mutational dynamics of MHCvalidator-identified SARS-CoV-2 CD8+ epitopes from vaccine BNT162b4. While most vaccine-encoded CD8+ epitopes exhibit global conservation from January 2020 to October 2023, a highly immunodominant A*01-associated epitope, especially in hospitalized patients, undergoes substantial mutations in Delta and Omicron variants. Our approach unveils unprecedented SARS-CoV-2 T-cell epitopes, elucidates novel antigenic features, and underscores mutational dynamics of vaccine-relevant epitopes. The analysis platform is applicable to any viruses, and underscores the need for continual vigilance in T-cell vaccine development against the evolving landscape of hypermutating SARS-CoV-2 variants.

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SARS-CoV-2 infected cells present HLA-I peptides from canonical and out-of-frame ORFs

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