EventsViruses 2026 – New Horizons in Virology
Published
This submission belongs to the session S5. Virus-Host Interactions of the event Viruses 2026 – New Horizons in Virology
Published date
09 Mar, 2026
Academic Editor
author-avatarEric Freed
Citation
Stéphane Blanc, What we can learn in virology from multipartite viruses, in Proceedings of Viruses 2026 – New Horizons in Virology, Barcelona, 11 March–13 March 2026, MDPI: Basel, Switzerland
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What we can learn in virology from multipartite viruses

1. UMR PHIM, Univ Montpellier, INRAE, CIRAD, IRD, Institut Agro, Montpellier, France, France
Abstract

Multipartite viruses, with genomes split across multiple segments each encapsidated in separate particles, deviate sharply from classical viral architectures. In these systems, full genetic information is recovered only at the population level. Theoretical models predict that such viruses—especially those with more than three or four segments—should be nonviable due to high loss risk during transmission. Yet, they comprise ~17% of all known viral species. Using a nanovirus with eight genomic segments, we have experimentally challenged prevailing assumptions in virology. We showed that spatial co-localization of all segments in single host cells is not essential: distinct genes act cooperatively at a supracellular scale. Similarly, segments’ transfer between hosts by aphid vectors does not require co-transmission. They can be separately acquired from different infected plants by one or multiple vectors and reassembled into a complete genome. Furthermore, we uncovered that segment abundance varies with the host plant, suggesting a mutation-free mechanism for tuning gene expression through gene dosing. This flexibility may confer a benefit in fluctuating environments. These findings prompt a broader question: are such collective, flexible strategies unique to multipartite viruses, or do they reveal a more general, overlooked mode of viral organization?

Keywords
Multipartite viruses
pluricellularity
gene copy number variation
gene network
population genetics
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