Species-specific barriers constrict Orsay virus host range across the Caenorhabditis genus
Herek, D.; Munoz-Sanchez, J. C.; Villena-Gimenez, A.; Castiglioni, V. G.; Elena, S. F.
Show abstract
Predicting the host range and spillover potential of RNA viruses requires understanding how ecological, immunological, and evolutionary factors jointly shape viral life cycles across related hosts. Here we integrate population-level viral load dynamics, single-animal heterogeneity, tissue-level progression, transmission competence, and evolutionary sustainability to map the eco evolutionary barriers that Orsay virus encounters across six Caenorhabditis species. We show that host species identity determines the timing and completeness of the viral life cycle, producing species specific combinations of susceptibility, replication kinetics, RNA2 to RNA1 stoichiometric balance, virion egress, and onward transmission. These phenotypes correspond to distinct host competence phenotypes, ranging from permissive (Caenorhabditis elegans) to restrictive or evolutionarily dead end host species. Alternative host species disrupt viral life cycle synchrony through delayed replication, truncated cycles, or failure to produce lumen localized virus, thereby reducing transmission and preventing viral adaptation upon serial passage. Our results demonstrate how temporal mismatches between viral replication and host physiology create a series of eco evolutionary barriers to emergence, offering a mechanistic framework for predicting viral host range.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Single mosquito metatranscriptomics identifies vectors, emerging pathogens and reservoirs in one assay 94%
- Physiology and ecology together regulate host and vector importance for Ross River virus and other vector-borne diseases 94%
- Resolving the origins of secretory products and anthelmintic responses in a human parasitic nematode at single-cell resolution. 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.