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The Orsay Virus as a model for population-wide viral infection dynamics

Laurence Pirenne, View ORCID ProfileMaximilian A. H. Jakobs, David Jordan, View ORCID ProfileKristian Franze, Eric A. Miska
doi: https://doi.org/10.1101/2021.03.02.433572
Laurence Pirenne
1Gurdon Institute, University of Cambridge, Cambridge, CB2 1QN, UK
2Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
3Orofacial Development and Regeneration, Institute of Oral Biology, Centre of Dental Medicine, University of Zurich, Zurich, Switzerland
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Maximilian A. H. Jakobs
5Department of Physiology, Development, and Neuroscience, Downing Site, Cambridge, CB2 3DY, UK
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  • ORCID record for Maximilian A. H. Jakobs
David Jordan
1Gurdon Institute, University of Cambridge, Cambridge, CB2 1QN, UK
2Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
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Kristian Franze
5Department of Physiology, Development, and Neuroscience, Downing Site, Cambridge, CB2 3DY, UK
6Institute of Medical Physics, Friedrich-Alexander University Erlangen-Nuremberg, 91052 Erlangen, Germany
7Max-Planck-Zentrum für Physik und Medizin, 91054 Erlangen, Germany
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Eric A. Miska
1Gurdon Institute, University of Cambridge, Cambridge, CB2 1QN, UK
2Department of Genetics, University of Cambridge, Cambridge, CB2 3EH, UK
4Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
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  • For correspondence: eam29@cam.ac.uk
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Abstract

To this day, epidemics pose a considerable threat to mankind. Experimental models that simulate the spread of infectious diseases are thus crucial to the inception of effective control policies. Current models have had great success incorporating virulence and host immune response but do rarely take host genetics, behavior and host environment into account. Here, we present a full-scale imaging setup that utilizes the infection of the nematode C. elegans with a positive-stranded RNA virus (Orsay Virus) to probe key epidemiological parameters and simulate the spread of infection in a whole population. We demonstrate that our system is able to quantify infection levels and host behavior at a high sampling rate and show that different host genetic backgrounds can influence viral spread, while also highlighting the influence of infection on various host behaviors. Future work will allow the isolation of key behavioral and environmental factors that affect viral spread, potentially enabling novel policies to combat the spread of viral infections.

Significance Statement In the ongoing COVID-19 pandemic, we struggle to find effective control policies that “stop the spread”. While current animal models of virus spread in populations are highly sophisticated, they rarely explore effects of host behavior and its environment. We developed an experimental animal model system that allows us to visualize virus transmission in whole populations of C. elegans while also measuring behaviors. We were able to demonstrate how C. elegans genetics influences the progression of viral infection in a population and how animals adjust their behavior when infected. In the future, we envision that animal model systems like ours are used to test the effects of viral control policies on viral spread before they are applied in real world scenarios.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Classification Biological Sciences, Genetics

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 02, 2021.
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The Orsay Virus as a model for population-wide viral infection dynamics
Laurence Pirenne, Maximilian A. H. Jakobs, David Jordan, Kristian Franze, Eric A. Miska
bioRxiv 2021.03.02.433572; doi: https://doi.org/10.1101/2021.03.02.433572
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The Orsay Virus as a model for population-wide viral infection dynamics
Laurence Pirenne, Maximilian A. H. Jakobs, David Jordan, Kristian Franze, Eric A. Miska
bioRxiv 2021.03.02.433572; doi: https://doi.org/10.1101/2021.03.02.433572

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