Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs.

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

NK cell responses to cytomegalovirus infection lead to stable imprints in the human KIR repertoire and involve activating KIRs.

Human natural killer (NK) cells are functionally regulated by killer cell immunoglobulin-like receptors (KIRs) and their interactions with HLA class I molecules. As KIR expression in a given NK cell is genetically hard-wired, we hypothesized that KIR repertoire perturbations reflect expansions of unique NK-cell subsets and may be used to trace adaptation of the NK-cell compartment to virus infections. By determining the human "KIR-ome" at a single-cell level in more than 200 donors, we were able to analyze the magnitude of NK cell adaptation to virus infections in healthy individuals. Strikingly, infection with human cytomegalovirus (CMV), but not with other common herpesviruses, induced expansion and differentiation of KIR-expressing NK cells, visible as stable imprints in the repertoire. Education by inhibitory KIRs promoted the clonal-like expansion of NK cells, causing a bias for self-specific inhibitory KIRs. Furthermore, our data revealed a unique contribution of activating KIRs (KIR2DS4, KIR2DS2, or KIR3DS1), in addition to NKG2C, in the expansion of human NK cells. These results provide new insight into the diversity of KIR repertoire and its adaptation to virus infection, suggesting a role for both activating and inhibitory KIRs in immunity to CMV infection.

Tolerant and diverse natural killer cell repertoires in the absence of selection.

Natural killer (NK) cells are innate lymphocytes that participate in the early control of viruses and tumors. The function of NK cells is under tight regulation by two complementary inhibitory receptor families that bind to classical and non-classical HLA class I molecules: the CD94/NKG2A receptors and the killer cell immunoglobulin-like receptors (KIRs). In this mini-review, recent data on the structure of human NK cell receptor repertoires and its relation to functional responses and tolerance to self are discussed. We propose that no active selection is required to generate diverse NK cell repertoires characterized by a dominant expression of receptors with specificity for self-HLA class I. Instead, the primary consequence of interactions with HLA class I molecules is a functional tuning of randomly generated NK cell repertoires.

KIR acquisition probabilities are independent of self-HLA class I ligands and increase with cellular KIR expression.

Inhibitory killer cell immunoglobulin-like receptors (KIRs) preserve tolerance to self and shape the functional response of human natural killer (NK) cells. Here, we have evaluated the influence of selection processes in the formation of inhibitory KIR repertoires in a cohort of 44 donors homozygous for the group A KIR haplotype. Coexpression of multiple KIRs was more frequent than expected by the product rule that describes random association of independent events. In line with this observation, the probability of KIR acquisition increased with the cellular expression of KIRs. Three types of KIR repertoires were distinguished that differed in frequencies of KIR- and NKG2A-positive cells but showed no dependency on the number of self-HLA class I ligands. Furthermore, the distribution of self- and nonself-KIRs at the cell surface reflected a random combination of receptors rather than a selection process conferred by cognate HLA class I molecules. Finally, NKG2A was found to buffer overall functional responses in KIR repertoires characterized by low-KIR expression frequencies. The results provide new insights into the formation of inhibitory KIR repertoires on human NK cells and support a model in which variegated KIR repertoires are generated through sequential and random acquisition of KIRs in the absence of selection.