Understanding the hallmarks of the immune response to SARS-CoV-2 is critical for fighting the COVID-19 pandemic. We assessed antibody and T cell reactivity in convalescent COVID-19 patients and healthy donors sampled both prior to and during the pandemic. Healthy donors examined during the pandemic exhibited increased numbers of SARS-CoV-2-specific T cells, but no humoral response. Their probable exposure to the virus resulted in either asymptomatic infection without antibody secretion, or activation of pre-existing immunity. In convalescent patients, we observed a public and diverse T cell response to SARS-CoV-2 epitopes, revealing T cell receptor (TCR) motifs with germline-encoded features. Bulk CD4 + and CD 8+ T cell responses to the spike glycoprotein were mediated by groups of homologous TCRs, some of them shared across multiple donors. Overall, our results demonstrate that the T cell response to SARS-CoV-2, including the identified set of TCRs, can serve as a useful biomarker for surveying antiviral immunity.

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T cell maturation and activation depend upon T cell receptor (TCR) interactions with a wide variety of antigenic peptides displayed in a given major histocompatibility complex (MHC) context. Complementarity-determining region 3 (CDR3) is the most variable part of the TCRα and -β chains, which govern interactions with peptide–MHC complexes. However, it remains unclear how the CDR3 landscape is shaped by individual MHC context during thymic selection of naïve T cells. We established two mouse strains carrying distinct allelic variants ofH2-Aand analyzed thymic and peripheral production and TCR repertoires of naïve conventional CD4+T (Tconv) and naïve regulatory CD4+T (Treg) cells. Compared with tuberculosis-resistant C57BL/6 (H2-Ab) mice, the tuberculosis-susceptible H2-Ajmice had fewer CD4+T cells of both subsets in the thymus. In the periphery, this deficiency was only apparent for Tconvand was compensated for by peripheral reconstitution for Treg. We show that H2-Ajfavors selection of a narrower and more convergent repertoire with more hydrophobic and strongly interacting amino acid residues in the middle of CDR3α and CDR3β, suggesting more stringent selection against a narrower peptide–MHC-II context. H2-Ajand H2-Abmice have prominent reciprocal differences in CDR3α and CDR3β features, probably reflecting distinct modes of TCR fitting to MHC-II variants. These data reveal the mechanics and extent of how MHC-II shapes the naïve CD4+T cell CDR3 landscape, which essentially defines adaptive response to infections and self-antigens.

Understanding the hallmarks of the adaptive immune response to SARS-CoV-2 is critical for fighting the COVID-19 pandemic. We assessed the antibody and T-cell reactivity in COVID-19 convalescent patients and healthy donors sampled both prior to and during the pandemic. The numbers of SARS- CoV-2-specific T cells were increased in healthy donors examined during COVID-19. Combined with the absence of symptoms and humoral response across that group, this finding suggests that some individuals might be protected by T-cell cross-reactivity. In convalescent patients we observed public and diverse T-cell response to SARS-CoV-2 epitopes, revealing T-cell receptor motifs with germline- encoded features. Bulk CD4+ and CD8+ T-cell responses to Spike glycoprotein were mediated by groups of homologous T-cell receptors, some of them shared across multiple donors. Overall, our results demonstrate that T-cell response to SARS-CoV-2, including the identified set of specific T-cell receptors, can serve as a useful biomarker for surveying viral exposure and immunity.

The PBAF chromatin-remodeling complexes are multi-protein machines, regulating expression of genes involved in proliferation and differentiation. PHF10 is a subunit of the PBAF essential for its association with chromatin. Mammalian PHF10 is expressed as four ubiquitous isoforms, which are alternatively incorporated in the complex and differ by their influence on transcription of target genes. PHF10 have different domain structure and two of them (PHF10-S isoforms) lack C-terminal PHD domains, which enables their phosphorylation by CK-1. Here we have found that PBAF subunits have low turnover rate, except for PHF10 which has much lower half-life, and is degraded by β-TrCP. The β-TrCP knockdown stabilizes PBAF core subunits - BRG1 and BAF155 and specific subunits - PHF10, BAF200, BAF180 and BRD7. PHF10 isoforms contain two non-canonical β-TrCP degrons and are degraded by β-TrCP in a phospho-dependent manner. But phosphorylation of PHF10-S degrons by CK-1, contrary to previously described degrons, prevents their degradation. Targeted molecular docking demonstrated that phosphorylated forms of PHF10 bind to β-TrCP with much lower affinity than non-phosphorylated ones, contrary to previously described degrons. This unorthodox mechanism proposes that phosphorylation of β-TrCP degrons by CK-1 could not only degrade a set of proteins, but also stabilize a different set of targets.

T cell recognition of a cognate peptide–major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T cell receptors would greatly facilitate identification of vaccine targets for both pathogenic diseases and personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the center of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as severe acute respiratory syndrome coronavirus 2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in the training data of the models seems to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen versus cancer peptides. Overall, we demonstrate that accurate and reliable predictions of immunogenic CD8+ T cell targets remain unsolved; thus, we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.

Antigen recognition by T-cells is guided by the T-cell receptor (TCR) heterodimer formed by α and β chains. A huge diversity of TCR sequences should be maintained by the immune system in order to be able to mount an effective response towards foreign pathogens, so, due to cooperative binding of α and β chains to the pathogen, any constraints on chain pairing can have a profound effect on immune repertoire structure, diversity and antigen specificity. By integrating available structural data and paired chain sequencing results we were able to show that there are almost no constraints on pairing in TCRαβ complexes, allowing naive T-cell repertoire to reach the highest possible diversity. Additional analysis reveals that the specific choice of contacting amino acids can still have a profound effect on complex conformation. Moreover, antigen-driven selection can distort the uniform landscape of chain pairing, while small, yet significant, differences in the pairing can be attributed to various specialized T-cell subsets such as MAIT and iNKT T-cells, as well as other TCR sets specific to certain antigens.

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