Join us on Tuesday 19 November, 12:30 – 13:30, at the Big Data Institute (BDI), for the upcoming seminar in the PSI seminar series. Dr. Dalan Bailey will present his research on predicting the next pandemic pathogen, focusing on how emerging enveloped RNA viruses can adapt to new hosts.
The seminar will take place from 12:30 to 13:30 in the BDI building, seminar rooms, followed by lunch and networking from 13:30 to 14:30.
Abstract:
Zoonotic viruses, like SARS-CoV-2, are a significant cause of pandemics due to their ability, especially in error-prone RNA viruses, to rapidly adapt to new hosts. Factors driving zoonotic spillover are diverse, from macro-scale influences such as human population growth and land use changes to molecular-level mechanisms like viral entry, replication, and immune evasion. Understanding these molecular drivers is critical to improving global preparedness, developing vaccines, and advancing therapeutic strategies. My group examines viral entry mechanisms in emerging enveloped RNA viruses to better classify pandemic potential. The 2024 WHO Pathogen Prioritization project highlighted the importance of a family-based approach to assessing pandemic risk, calling for broader research on RNA virus families beyond the well-known human pathogens. A significant gap exists in understanding emerging virus families, with humans themselves potentially serving as unrecognized reservoirs in anthropogenic spillover events. Moreover, computational prediction of pandemic potential based on viral sequences alone requires more comprehensive datasets. Our group investigates receptor usage conservation across viral families, analyzing whether phylogenetic distance and receptor affinity correlate with host range. Establishing links between viral genotypes and host-range phenotypes will improve pandemic prediction models. By generating rich, comprehensive datasets and integrating them into predictive frameworks, we aim to equip the global community with the tools needed to anticipate and respond to future pandemics more effectively.