COVID-19 vaccines have been authorized in multiple countries, and more are under rapid development. Careful design of a vaccine prioritization strategy across sociodemographic groups is a crucial public policy challenge given that 1) vaccine supply will be constrained for the first several months of the vaccination campaign, 2) there are stark differences in transmission and severity of impacts from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across groups, and 3) SARS-CoV-2 differs markedly from previous pandemic viruses. We assess the optimal allocation of a limited vaccine supply in the United States across groups differentiated by age and essential worker status, which constrains opportunities for social distancing. We model transmission dynamics using a compartmental model parameterized to capture current understanding of the epidemiological characteristics of COVID-19, including key sources of group heterogeneity (susceptibility, severity, and contact rates). We investigate three alternative policy objectives (minimizing infections, years of life lost, or deaths) and model a dynamic strategy that evolves with the population epidemiological status. We find that this temporal flexibility contributes substantially to public health goals. Older essential workers are typically targeted first. However, depending on the objective, younger essential workers are prioritized to control spread or seniors to directly control mortality. When the objective is minimizing deaths, relative to an untargeted approach, prioritization averts deaths on a range between 20,000 (when nonpharmaceutical interventions are strong) and 300,000 (when these interventions are weak). We illustrate how optimal prioritization is sensitive to several factors, most notably, vaccine effectiveness and supply, rate of transmission, and the magnitude of initial infections.

The death toll and economic loss resulting from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic are stark reminders that we are vulnerable to zoonotic viral threats. Strategies are needed to identify and characterize animal viruses that pose the greatest risk of spillover and spread in humans and inform public health interventions. Using expert opinion and scientific evidence, we identified host, viral, and environmental risk factors contributing to zoonotic virus spillover and spread in humans. We then developed a risk ranking framework and interactive web tool, SpillOver, that estimates a risk score for wildlife-origin viruses, creating a comparative risk assessment of viruses with uncharacterized zoonotic spillover potential alongside those already known to be zoonotic. Using data from testing 509,721 samples from 74,635 animals as part of a virus discovery project and public records of virus detections around the world, we ranked the spillover potential of 887 wildlife viruses. Validating the risk assessment, the top 12 were known zoonotic viruses, including SARS-CoV-2. Several newly detected wildlife viruses ranked higher than known zoonotic viruses. Using a scientifically informed process, we capitalized on the recent wealth of virus discovery data to systematically identify and prioritize targets for investigation. The publicly accessible SpillOver platform can be used by policy makers and health scientists to inform research and public health interventions for prevention and rapid control of disease outbreaks. SpillOver is a living, interactive database that can be refined over time to continue to improve the quality and public availability of information on viral threats to human health.

Acetogenic bacteria use cellular redox energy to convert CO₂ to acetate using the Wood–Ljungdahl (WL) pathway. Such redox energy can be derived from electrons generated from H₂ as well as from inorganic materials, such as photoresponsive semiconductors. We have developed a nanoparticle-microbe hybrid system in which chemically synthesized cadmium sulfide nanoparticles (CdS-NPs) are displayed on the cell surface of the industrial acetogen Clostridium autoethanogenum. The hybrid system converts CO₂ into acetate without the need for additional energy sources, such as H₂, and uses only light-induced electrons from CdS-NPs. To elucidate the underlying mechanism by which C. autoethanogenum uses electrons generated from external energy sources to reduce CO₂, we performed transcriptional analysis. Our results indicate that genes encoding the metal ion or flavin-binding proteins were highly up-regulated under CdS-driven autotrophic conditions along with the activation of genes associated with the WL pathway and energy conservation system. Furthermore, the addition of these cofactors increased the CO₂ fixation rate under light-exposure conditions. Our results demonstrate the potential to improve the efficiency of artificial photosynthesis systems based on acetogenic bacteria integrated with photoresponsive nanoparticles.

Cullin-RING (really intersting new gene) E3 ubiquitin ligases (CRLs) are the largest E3 family and direct numerous protein substrates for proteasomal degradation, thereby impacting a myriad of physiological and pathological processes including cancer. To date, there are no reported small-molecule inhibitors of the catalytic activity of CRLs. Here, we describe high-throughput screening and medicinal chemistry optimization efforts that led to the identification of two compounds, 33-11 and KH-4-43, which inhibit E3 CRL4 and exhibit antitumor potential. These compounds bind to CRL4’s core catalytic complex, inhibit CRL4-mediated ubiquitination, and cause stabilization of CRL4’s substrate CDT1 in cells. Treatment with 33-11 or KH-4-43 in a panel of 36 tumor cell lines revealed cytotoxicity. The antitumor activity was validated by the ability of the compounds to suppress the growth of human tumor xenografts in mice. Mechanistically, the compounds’ cytotoxicity was linked to aberrant accumulation of CDT1 that is known to trigger apoptosis. Moreover, a subset of tumor cells was found to express cullin4 proteins at levels as much as 70-fold lower than those in other tumor lines. The low-cullin4–expressing tumor cells appeared to exhibit increased sensitivity to 33-11/KH-4-43, raising a provocative hypothesis for the role of low E3 abundance as a cancer vulnerability.