Leveraging sequence data to identify mammalian-adaptive mutations and host factors in avian influenza virus

Principal Supervisor:   Dr. Daniel Goldhill (RVC)

Co-Supervisor:   Dr. Damien Tully (LSHTM) and Prof. Damer Blake (RVC)

Project Description

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 known1,2, 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 mutations3. However, these studies rarely test whether identified mutations have the predicted biological effect3. 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 assays4 will be undertaken to assess whether these mutations lead to human adaptation. Furthermore, this work will identify novel host factors behind mutations of unknown mechanism1. 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 principal supervisor.

Key References

Long, J. S. et al. Species difference in ANP32A underlies influenza A virus polymerase host restriction. Nature 529, 101-104 (2016).

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

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).

Goldhill, D. H. et al. The mechanism of resistance to favipiravir in influenza. Proceedings of the National Academy of Sciences 115, 11613-11618 (2018).

Further details about the project may be obtained from

Principal Supervisor:      dgoldhill@rvc.ac.uk

Co-Supervisor:   damien.tully@lshtm.ac.uk

How to apply

To apply for this position, please follow the link below. Please use your personal statement to tell us why this project excites you, the sort of science that you are most interested in, and to demonstrate any previous skills or experience relevant to the project. For more information on the application process and English Language requirements at RVC see How to Apply.

Interviews will take place March 2023.

We welcome informal enquiries – these should be directed to the Lead Supervisor: dgoldhill@rvc.ac.uk 

Closing date for applications is: 
13th February 2023