Neutralizing antibody vaccine for pandemic and pre-emergent coronaviruses

Betacoronaviruses (betaCoVs) caused the severe acute respiratory syndrome (SARS) and Middle East Respiratory Syndrome (MERS) outbreaks, and the SARS-CoV-2 pandemic1–4. Vaccines that elicit protective immunity against SARS-CoV-2 and betaCoVs circulating in animals have the potential to prevent future betaCoV pandemics. Here, we show that macaque immunization with a multimeric SARS-CoV-2 receptor binding domain (RBD) nanoparticle adjuvanted with 3M-052/Alum elicited cross-neutralizing antibody (cross-nAb) responses against batCoVs, SARS-CoV-1, SARS-CoV-2, and SARS-CoV-2 variants B.1.1.7, P.1, and B.1.351. Nanoparticle vaccination resulted in a SARS-CoV-2 reciprocal geometric mean neutralization ID50 titer of 47,216, and protection against SARS-CoV-2 in macaque upper and lower respiratory tracts. Importantly, nucleoside-modified mRNA encoding a stabilized transmembrane spike or monomeric RBD also induced SARS-CoV-1 and batCoV cross-nAbs, albeit at lower titers. These results demonstrate current mRNA vaccines may provide some protection from future zoonotic betaCoV outbreaks, and provide a platform for further development of pan-betaCoV vaccines

Analysing extinction risk in parrots using decision trees

Comparative analysis techniques have been successfully applied in a number of recent attempts to identify the species traits associated with a current threat of extinction although less often to predict which species may become threatened in the future. Although prediction of risk is obviously a priority, such analyses are undermined by the fact that there may be non-linear and non-additive relationships between the species traits used. A Decision Tree analysis can accommodate with such relationships and here it is used to explore factors affecting extinction risk in parrots. The results firstly verify that simple biological and biogeographical traits can separate threatened from non-threatened species. It is also possible to predict which species are likely to become threatened in the future. The utility of the method is not in testing evolutionary-based hypotheses to explain extinction risk, rather it is a simple and practical method of confirming and/or predicting levels of risk. For well known taxonomic groups it could be used to confirm current IUCN threat categories and identify which species should receive closest attention when the group is next reviewed. For poorly known groups it could be used to predict categories of threat for unclassified species from small groups of classified ones