Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity.

T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoire composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of host responses to viruses such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we perform a large-scale analysis of over 4.7 billion sequences across 2130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identify and characterize convergent COVID-19-associated CDR3 gene usages, specificity groups, and sequence patterns. Here we show that T cell clonal expansion is associated with the upregulation of T cell effector function, TCR signaling, NF-kB signaling, and interferon-gamma signaling pathways. We also demonstrate that machine learning approaches accurately predict COVID-19 infection based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.

Multiplexed LNP-mRNA vaccination against pathogenic coronavirus species.

Although COVID-19 vaccines have been developed, multiple pathogenic coronavirus species exist, urging on development of multispecies coronavirus vaccines. Here we develop prototype lipid nanoparticle (LNP)-mRNA vaccine candidates against SARS-CoV-2 Delta, SARS-CoV, and MERS-CoV, and we test how multiplexing LNP-mRNAs can induce effective immune responses in animal models. Triplex and duplex LNP-mRNA vaccinations induce antigen-specific antibody responses against SARS-CoV-2, SARS-CoV, and MERS-CoV. Single-cell RNA sequencing profiles the global systemic immune repertoires and respective transcriptome signatures of vaccinated animals, revealing a systemic increase in activated B cells and differential gene expression across major adaptive immune cells. Sequential vaccination shows potent antibody responses against all three species, significantly stronger than simultaneous vaccination in mixture. These data demonstrate the feasibility, antibody responses, and single-cell immune profiles of multispecies coronavirus vaccination. The direct comparison between simultaneous and sequential vaccination offers insights into optimization of vaccination schedules to provide broad and potent antibody immunity against three major pathogenic coronavirus species.

Variant-specific vaccination induces systems immune responses and potent in vivo protection against SARS-CoV-2.

Lipid nanoparticle (LNP)-mRNA vaccines offer protection against COVID-19; however, multiple variant lineages caused widespread breakthrough infections. Here, we generate LNP-mRNAs specifically encoding wild-type (WT), B.1.351, and B.1.617 SARS-CoV-2 spikes, and systematically study their immune responses. All three LNP-mRNAs induced potent antibody and T cell responses in animal models; however, differences in neutralization activity have been observed between variants. All three vaccines offer potent protection against in vivo challenges of authentic viruses of WA-1, Beta, and Delta variants. Single-cell transcriptomics of WT- and variant-specific LNP-mRNA-vaccinated animals reveal a systematic landscape of immune cell populations and global gene expression. Variant-specific vaccination induces a systemic increase of reactive CD8 T cells and altered gene expression programs in B and T lymphocytes. BCR-seq and TCR-seq unveil repertoire diversity and clonal expansions in vaccinated animals. These data provide assessment of efficacy and direct systems immune profiling of variant-specific LNP-mRNA vaccination in vivo.

Metaviromic identification of discriminative genomic features in SARS-CoV-2 using machine learning.

The COVID-19 pandemic caused by SARS-CoV-2 has become a major threat across the globe. Here, we developed machine learning approaches to identify key pathogenic regions in coronavirus genomes. We trained and evaluated 7,562,625 models on 3,665 genomes including SARS-CoV-2, MERS-CoV, SARS-CoV, and other coronaviruses of human and animal origins to return quantitative and biologically interpretable signatures at nucleotide and amino acid resolutions. We identified hotspots across the SARS-CoV-2 genome, including previously unappreciated features in spike, RdRp, and other proteins. Finally, we integrated pathogenicity genomic profiles with B cell and T cell epitope predictions for enrichment of sequence targets to help guide vaccine development. These results provide a systematic map of predicted pathogenicity in SARS-CoV-2 that incorporates sequence, structural, and immunologic features, providing an unbiased collection of genetic elements for functional studies. This metavirome-based framework can also be applied for rapid characterization of new coronavirus strains or emerging pathogenic viruses.

Nonstructural Protein 1 of SARS-CoV-2 Is a Potent Pathogenicity Factor Redirecting Host Protein Synthesis Machinery toward Viral RNA.

The causative virus of the COVID-19 pandemic, SARS-CoV-2, uses its nonstructural protein 1 (Nsp1) to suppress cellular, but not viral, protein synthesis through yet unknown mechanisms. We show here that among all viral proteins, Nsp1 has the largest impact on host viability in the cells of human lung origin. Differential expression analysis of mRNA-seq data revealed that Nsp1 broadly alters the cellular transcriptome. Our cryo-EM structure of the Nsp1-40S ribosome complex shows that Nsp1 inhibits translation by plugging the mRNA entry channel of the 40S. We also determined the structure of the 48S preinitiation complex formed by Nsp1, 40S, and the cricket paralysis virus internal ribosome entry site (IRES) RNA, which shows that it is nonfunctional because of the incorrect position of the mRNA 3' region. Our results elucidate the mechanism of host translation inhibition by SARS-CoV-2 and advance understanding of the impacts from a major pathogenicity factor of SARS-CoV-2.