The diversity of circulating influenza viruses in birds means that there is a constant risk that a novel virus could emerge and cause a future pandemic. However, most avian influenza infections in humans do not spread beyond a single individual as avian viruses are poorly adapted to human host factors. While the mechanisms of certain adaptive mutations are known (1,2) we still cannot predict which avian viruses have a greater propensity to infect human cells. A better understanding of which mutations lead to human adaptation would aid in pandemic preparedness by highlighting which avian influenza viruses are likeliest to emerge.

Several computational studies have compared avian and mammalian influenza sequences to identify mammalian-adaptive mutations (3). However, these studies rarely test whether identified mutations have the predicted biological effect (3). Furthermore, as biological mechanisms are mostly unknown, it is impossible to establish which mutations are associated with particular host factors or to easily predict the effect of novel mutations.

This PhD project will implement a large-scale bioinformatic analysis of sequences of avian influenza infection from mammals to identify human adaptive mutations. Structural models will then be used to classify which mutations likely share a mechanism allowing for the discovery of mutations with unknown mechanism which could be interacting with novel host factors. Focusing on mutations in the polymerase, laboratory-based analyses using minigenome assays (4) will be undertaken to assess whether these mutations lead to human adaptation. Furthermore, this work will identify novel host factors behind mutations of unknown mechanism (1). Finally, a model encompassing the mutational repertoire across all genes will be constructed to predict the likelihood of human emergence for current and future circulating strains of avian influenza.The student will benefit from a highly multidisciplinary supervisor team as you will be trained in complementary skills in bioinformatics, molecular virology and structural biology. This diverse skill set will equip you for a multitude of potential career paths. This project would suit a candidate with a background or experience in laboratory techniques/molecular biology and/or computational biology. Experience and prior knowledge of influenza may be advantageous but is not essential. We are supportive of diverse career paths and we welcome applicants with a diversity of backgrounds, experience and ideas and we encourage applications from those with non-traditional academic backgrounds as well as those who are not looking for a career in academia. Informal enquiries are welcome and may be addressed to the supervisors.

This studentship will be held jointly between the lab of Dr Daniel Goldhill, based at the beautiful Hawkshead Campus of the RVC, and Dr Damien Tully at the London School of Hygiene and Tropical Medicine in London.

References:

1. Long, J. S. et al. Species difference in ANP32A underlies influenza A virus polymerase host restriction. Nature 529, 101-104 (2016).
2. Pinto, R. M. et al. Zoonotic avian influenza viruses evade human BTN3A3 restriction. bioRxiv, 2022.2006.2014.496196 (2022). https://doi.org:10.1101/2022.06.14.496196
3. Borkenhagen, L. K., Allen, M. W. & Runstadler, J. A. Influenza virus genotype to phenotype predictions through machine learning: a systematic review: Computational Prediction of Influenza Phenotype. Emerging microbes & infections 10, 1896-1907 (2021).
4. Goldhill, D. H. et al. The mechanism of resistance to favipiravir in influenza. Proceedings of the National Academy of Sciences 115, 11613-11618 (2018).