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Current seasonal influenza virus vaccines induce responses primarily against immunodominant but highly plastic epitopes in the globular head of the hemagglutinin (HA) glycoprotein. Because of viral antigenic drift at these sites, vaccines need to be updated and readministered annually. To increase the breadth of influenza vaccine–mediated protection, we developed an antigenically complex mixture of recombinant HAs designed to redirect immune responses to more conserved domains of the protein. Vaccine-induced antibodies were disproportionally redistributed to the more conserved stalk of the HA without hindering, and in some cases improving, antibody responses against the head domain. These improved responses led to increased protection against homologous and heterologous viral challenges in both mice and ferrets compared with conventional vaccine approaches. Thus, antigenically complex protein mixtures can at least partially overcome HA head domain antigenic immunodominance and may represent a step toward a more universal influenza vaccine.

Betacoronaviruses caused the outbreaks of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome, as well as the current pandemic of SARS coronavirus 2 (SARS-CoV-2)1–4. Vaccines that elicit protective immunity against SARS-CoV-2 and betacoronaviruses that circulate in animals have the potential to prevent future pandemics. Here we show that the immunization of macaques with nanoparticles conjugated with the receptor-binding domain of SARS-CoV-2, and adjuvanted with 3M-052 and alum, elicits cross-neutralizing antibody responses against bat coronaviruses, SARS-CoV and SARS-CoV-2 (including the B.1.1.7, P.1 and B.1.351 variants). Vaccination of macaques with these nanoparticles resulted in a 50% inhibitory reciprocal serum dilution (ID50) neutralization titre of 47,216 (geometric mean) for SARS-CoV-2, as well as in protection against SARS-CoV-2 in the upper and lower respiratory tracts. Nucleoside-modified mRNAs that encode a stabilized transmembrane spike or monomeric receptor-binding domain also induced cross-neutralizing antibody responses against SARS-CoV and bat coronaviruses, albeit at lower titres than achieved with the nanoparticles. These results demonstrate that current mRNA-based vaccines may provide some protection from future outbreaks of zoonotic betacoronaviruses, and provide a multimeric protein platform for the further development of vaccines against multiple (or all) betacoronaviruses.

Typhoid Vi vaccines have been shown to be efficacious in children living in endemic regions; however, a widely accepted correlate of protection remains to be established. We applied a systems serology approach to identify Vi-specific serological correlates of protection using samples obtained from participants enrolled in an experimental controlled human infection study. Participants were vaccinated with Vi-tetanus toxoid conjugate (Vi-TT) or unconjugated Vi-polysaccharide (Vi-PS) vaccines and were subsequently challenged with Salmonella Typhi bacteria. Multivariate analyses identified distinct protective signatures for Vi-TT and Vi-PS vaccines in addition to shared features that predicted protection across both groups. Vi IgA quantity and avidity correlated with protection from S. Typhi infection, whereas higher fold increases in Vi IgG responses were associated with reduced disease severity. Targeted antibody-mediated functional responses, particularly neutrophil phagocytosis, were also identified as important components of the protective signature. These humoral markers could be used to evaluate and develop efficacious Vi-conjugate vaccines and assist with accelerating vaccine availability to typhoid-endemic regions.

Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of biological datasets, but the necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software, SIMON, to facilitate the application of 180+ state-of-the-art machine-learning algorithms to high-dimensional biomedical data. With an easy-to-use graphical user interface, standardized pipelines, and automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.