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.

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SummarySalmonella enterica serovar Typhimurium ( S. Typhimurium) and several mutant derivatives were able to enter efficiently murine bone marrow-derived dendritic cells using mechanisms predominantly independent of the Salmonella pathogenicity island 1 type III secretion system. The levels of intracellular bacteria did not increase significantly over many hours after invasion. Using fluid endocytic tracers and other markers, S. Typhimurium-containing vacuoles (SCVs) were physically distinguishable from early endocytic compartments. Fifty to eighty per cent of SCVs harbouring wild-type S. Typhimurium or aroA , invH and ssaV mutant derivatives were associated with late endosome markers. In contrast, S. Typhimurium sifA was shown to escape the SCVs into the cytosol of infected dendritic cells. S. Typhimurium aroC sifA was more efficient than S. Typhimurium aroC in delivering a eukaryotic promoter-driven green fluorescent protein reporter gene for expression in dendritic cells. In contrast, S. Typhimurium aroC sifA did not detectably increase the efficiency of MHC class I presentation of the model antigen ovalbumin to T cells compared to a similar aroC derivative. Mice infected with the S. Typhimurium aroC sifA expressing ovalbumin did not develop detectably enhanced levels of cytotoxic T cell or interferon-g g g g production compared to S. Typhimurium aroC derivatives.

Novel candidate live oral vaccines based on a Salmonella enterica serovar Typhi ZH9 (Ty2 ⌬aroC ⌬ssaV) derivative that directed the expression of either the B subunit of Escherichia coli heat-labile toxin or hepatitis B virus core antigen from the bacterial chromosome using the in vivo inducible ssaG promoter were constructed. The levels of attenuation of the two S. enterica serovar Typhi ZH9 derivatives were similar to that of the parent as assessed by measuring the replication of bacteria within human macrophage-like U937 cells. The expression of heterologous antigen in the respective S. enterica serovar Typhi ZH9 derivatives was up-regulated significantly within U937 cells compared to similar S. enterica serovar Typhi ZH9 derivative bacteria grown in modified Luria-Bertani broth supplemented with aromatic amino acids. Immunization of mice with these S. enterica serovar Typhi ZH9 derivatives stimulated potent antigen-specific serum immunoglobulin G responses to the heterologous antigens.

Background: The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue, especially, tumor genetic intra-heterogeneity coupled with the problem of sequencing and alignment artifacts, makes somatic variant calling a challenging task. Current variant filtering strategies, such as rule-based filtering and consensus voting of different algorithms, have previously helped to increase specificity, although comes at the cost of sensitivity. Methods: In light of this, we have developed the NeoMutate framework which incorporates 7 supervised machine learning (ML) algorithms to exploit the strengths of multiple variant callers, using a non-redundant set of biological and sequence features. We benchmarked NeoMutate by simulating more than 10,000 bona fide cancer-related mutations into three well-characterized Genome in a Bottle (GIAB) reference samples.Results: A robust and exhaustive evaluation of NeoMutate's performance based on 5-fold cross validation experiments, in addition to 3 independent tests, demonstrated a substantially improved variant detection accuracy compared to any of its individual composite variant callers and consensus calling of multiple tools. Conclusions:We show here that integrating multiple tools in an ensemble ML layer optimizes somatic variant detection rates, leading to a potentially improved variant selection framework for the diagnosis and treatment of cancer.