Evidence for broad cross-reactivity of the SARS-CoV-2 NSP12-directed CD4+ T-cell response with pre-primed responses directed against common cold coronaviruses.

Introduction: The nonstructural protein 12 (NSP12) of the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has a high sequence identity with common cold coronaviruses (CCC). Methods: Here, we comprehensively assessed the breadth and specificity of the NSP12-specific T-cell response after in vitro T-cell expansion with 185 overlapping 15-mer peptides covering the entire SARS-CoV-2 NSP12 at single-peptide resolution in a cohort of 27 coronavirus disease 2019 (COVID-19) patients. Samples of nine uninfected seronegative individuals, as well as five pre-pandemic controls, were also examined to assess potential cross-reactivity with CCCs. Results: Surprisingly, there was a comparable breadth of individual NSP12 peptide-specific CD4+ T-cell responses between COVID-19 patients (mean: 12.82 responses; range: 0-25) and seronegative controls including pre-pandemic samples (mean: 12.71 responses; range: 0-21). However, the NSP12-specific T-cell responses detected in acute COVID-19 patients were on average of a higher magnitude. The most frequently detected CD4+ T-cell peptide specificities in COVID-19 patients were aa236-250 (37%) and aa246-260 (44%), whereas the peptide specificities aa686-700 (50%) and aa741-755 (36%), were the most frequently detected in seronegative controls. In CCC-specific peptide-expanded T-cell cultures of seronegative individuals, the corresponding SARS-CoV-2 NSP12 peptide specificities also elicited responses in vitro. However, the NSP12 peptide-specific CD4+ T-cell response repertoire only partially overlapped in patients analyzed longitudinally before and after a SARS-CoV-2 infection. Discussion: The results of the current study indicate the presence of pre-primed, cross-reactive CCC-specific T-cell responses targeting conserved regions of SARS-CoV-2, but they also underline the complexity of the analysis and the limited understanding of the role of the SARS-CoV-2 specific T-cell response and cross-reactivity with the CCCs.

Correction: Broadly directed SARS-CoV-2-specific CD4+ T cell response includes frequently detected peptide specificities within the membrane and nucleoprotein in patients with acute and resolved COVID-19.

[This corrects the article DOI: 10.1371/journal.ppat.1009842.].

Cross-reactive and mono-reactive SARS-CoV-2 CD4+ T cells in prepandemic and COVID-19 convalescent individuals.

Class II tetramer reagents for eleven common DR alleles and a DP allele prevalent in the world population were used to identify SARS-CoV-2 CD4+ T cell epitopes. A total of 112, 28 and 42 epitopes specific for Spike, Membrane and Nucleocapsid, respectively, with defined HLA-restriction were identified. Direct ex vivo staining of PBMC with tetramer reagents was used to define immunodominant and subdominant T cell epitopes and estimate the frequencies of these T cells in SARS-CoV-2 exposed and naïve individuals. Majority of SARS-CoV-2 epitopes identified have <67% amino acid sequence identity with endemic coronaviruses and are unlikely to elicit high avidity cross-reactive T cell responses. Four SARS-CoV-2 Spike reactive epitopes, including a DPB1*04:01 restricted epitope, with ≥67% amino acid sequence identity to endemic coronavirus were identified. SARS-CoV-2 T cell lines for three of these epitopes elicited cross-reactive T cell responses to endemic cold viruses. An endemic coronavirus Spike T cell line showed cross-reactivity to the fourth SARS-CoV-2 epitope. Three of the Spike cross-reactive epitopes were subdominant epitopes, while the DPB1*04:01 restricted epitope was a dominant epitope. Frequency analyses showed Spike cross-reactive T cells as detected by tetramers were present at relatively low frequency in unexposed people and only contributed a small proportion of the overall Spike-specific CD4+ T cells in COVID-19 convalescent individuals. In total, these results suggested a very limited number of SARS-CoV-2 T cells as detected by tetramers are capable of recognizing ccCoV with relative high avidity and vice versa. The potentially supportive role of these high avidity cross-reactive T cells in protective immunity against SARS-CoV-2 needs further studies.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

Broadly directed SARS-CoV-2-specific CD4+ T cell response includes frequently detected peptide specificities within the membrane and nucleoprotein in patients with acute and resolved COVID-19.

The aim of this study was to define the breadth and specificity of dominant SARS-CoV-2-specific T cell epitopes using a comprehensive set of 135 overlapping 15-mer peptides covering the SARS-CoV-2 envelope (E), membrane (M) and nucleoprotein (N) in a cohort of 34 individuals with acute (n = 10) and resolved (n = 24) COVID-19. Following short-term virus-specific in vitro cultivation, the single peptide-specific CD4+ T cell response of each patient was screened using enzyme linked immuno spot assay (ELISpot) and confirmed by single-peptide intracellular cytokine staining (ICS) for interferon-γ (IFN-γ) production. 97% (n = 33) of patients elicited one or more N, M or E-specific CD4+ T cell responses and each patient targeted on average 21.7 (range 0-79) peptide specificities. Overall, we identified 10 N, M or E-specific peptides that showed a response frequency of more than 36% and five of them showed high binding affinity to multiple HLA class II binders in subsequent in vitro HLA binding assays. Three peptides elicited CD4+ T cell responses in more than 55% of all patients, namely Mem_P30 (aa146-160), Mem_P36 (aa176-190), both located within the M protein, and Ncl_P18 (aa86-100) located within the N protein. These peptides were further defined in terms of length and HLA restriction. Based on this epitope and restriction data we developed a novel DRB*11 tetramer (Mem_aa145-164) and examined the ex vivo phenotype of SARS-CoV-2-specific CD4+ T cells in one patient. This detailed characterization of single T cell peptide responses demonstrates that SARS-CoV-2 infection universally primes a broad T cell response directed against multiple specificities located within the N, M and E structural protein.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.