Warwick:Pirbright collaboration: understanding host-virus interactions in co-infection

Project Outline

Simultaneous or sequential infection with multiple virus strains is the norm in natural populations. The presence of a second strain often has important consequences for both disease spread and host health. For example, a strain’s success on both ecological and evolutionary timescales is influenced by how quickly it can replicate within the host, its persistence within the host, and its dominance compared to other strains.

This project will investigate the mechanisms and dynamics of viral co-infections using one of the most contagious groups of pathogens, foot-and-mouth disease viruses (FMDVs). FMDVs can infect numerous cloven-hoofed animals including domestic livestock, where they cause substantial losses in production of meat and dairy. For example, the 2001 FMDV outbreak in the U.K. cost the economy an estimated £8 billion and led to the culling of approximately 7 million livestock. In countries where foot-and-mouth disease (FMD) circulates regularly, multiple strains and serotypes of FMDV circulate, often sequentially, lending importance to research on how co-infecting viruses interact to influence infection outcomes.

Our previous research explored the co-infection dynamics of three distinct Southern Africa Territories (SAT) FMDV serotypes (SAT1, 2 and 3) in African buffalo. In experiments based on simultaneous infection with all serotypes, SAT1 persisted for longer in lymphoid tissues, surpassing the competitive persistence of SAT2 and SAT3. These findings correlated with a higher killing capacity and persistence in vitro.

We expect certain viruses to exhibit superior transmission, persistence, and dominance compared to their counterparts. Our research team at The Pirbright Institute (Eva Perez-Martin, Simon Gubbins) and University of Warwick (Erin Gorsich) has expertise in quantifying the outcomes of co-infection, characterising the mechanisms responsible, and understanding the disease dynamic consequences of co-infection. The ideal candidate will be interested in engaging in this highly interdisciplinary project, taking advantage of state-of-the-art methods to study host-virus interactions.

Techniques

A range of techniques will be applied for this project, with some choice dependent the applicants preference and project direction. They include:

• Handling viruses requiring the highest levels of biosecurity (SAPO4) virus in high biocontainment facilities.
• Experiments to investigate viral interactions, which include virological methods such as killing assays, time-course infections, viral titrations.
• Cell cultures of different cell lines (porcine, bovine, caprine) and primary cells (buffalo kidney cells).
• There may be opportunities to participate in experimental challenge studies using live animals.
• Monitoring of FMDV co-infections will be achieved through advanced bioimaging techniques, such as high-resolution flow cytometry, confocal microscopy and Incucyte Live-cell analysis. Quantification of viral RNA will be conducted using specific RT-qPCR. Long-term viral competition across
• passages in cell culture will be monitored by means of serotype-specific sandwich ELISAs.
• Mathematical and statistical modelling will be used to relate experimental data to disease spread.

Wildlife disease

Project Outline

Mathematical and statistical models are important tools in wildlife biology. For wildlife disease, they are particularly useful in evaluating vaccination or population management strategies in endangered species (e.g. Robinson et al. 2018), for investigating spill-over in between wildlife and livestock/domestic animal species (e.g. Gilbertson et al. 2022), and to understand the mechanisms driving transmission in data-poor settings (e.g. Jolles, Gorsich et al. 2021). Our team at the Zeeman Institute at University of Warwick has experience integrating models and field data to understand how wildlife disease spread and persist on ecological and evolutionary timescales.

We are looking to recruit a PhD student in the areas of disease ecology, mathematical and evolutionary ecology. Topics available include: (i) ecological and evolutionary dynamics of infectious diseases in natural bighorn sheep populations (Dugovich et al. 2021); (ii) multi-strain dynamics of foot-and-mouth disease virus in African buffalo (Jolles, Gorsich et al. 2021); (iii) the spread and evolution of avian influenza in wild waterfowl in the U.K. While the questions addressed are flexible, engagement with at least one of the model systems (e.g. bighorn sheep, African buffalo, avian influenza) will ensure your project is embedded in an international team of investigators and based on ongoing field-based data collection. The ideal candidate will be interested in developing data-driven models for real-world wildlife disease systems.

References

• Dugovich, B., Beechler, B.R., Dolan, B. et al. 2023. Population connectivity patterns of genetic diversity, immune responses, and exposure to infectious pneumonia in a metapopulation of desert bighorn sheep. Journal of Animal Ecology. 92, 1456–1469.
• Gilbertson, M.L.J., Onorato, D., Cunningham, M., VandeWoude, S., Craft, M.E. 2022. Paradoxes and synergies: Optimizing management of a deadly virus in an endangered carnivore. Journal of Applied Ecology. 59. 1548-1558.
• Jolles, A*, Gorsich, E.*, Gubbins, S., Beechler, B., Buss, P., Juleff, N., de Klerk-Lorist, L., Maree, F., Perez-Martin, E., van Schalkwyk, O.L. Scott, K. Zhang, F., Medlock, J.*, Charleston, B.* 2021. Endemic persistence of a highly contagious pathogen: foot-and-mouth disease in its wildlife host. Science. 374. 104-109.
• Robinson, S.J., Barbieri, M.M., Murphy, S., Baker, J.D., Harting, A.L., Craft, M.E., Littnan, C.L., 2018. Model recommendations meet management reality: Implementation and evaluation of a network-informed vaccination effort for endangered Hawaiian monk seals. Proceedings of the Royal Society B. 285. 20171899.

Techniques

A range of techniques will be applied for this project, with some choice dependent on the project chosen. They include statistical analysis of geospatial and epidemiological data across space and time. Analysis will culminate in the development of mechanistic models at a range of spatial and temporal scales. They will be analysed to investigate disease spread on ecological time scales and host-pathogen co-evolution at longer time scales.