B-S.B.36: A Mixed-Effects Framework for Small-N Intra-Host Viral Evolution Reveals Tissue-Specific Constraints in an Understudied Arbovirus
Felipe Campelo
University of Bristol
Naomi Forrester-Soto
Pirbright Institute
Keywords
VEEV, Small-N, Intra-Host Evolution, Mosquito, Spatiotemporal Data
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Understanding RNA virus evolution remains challenging due to their high mutation rates, compact genomes, and often scarce datasets that limit robust inference. This is particularly true for understudied arboviruses such as the Venezuelan equine encephalitis virus (VEEV), where intra-host evolutionary dynamics over time are not well understood. Investigating how host environment, temporal progression, and genomic context shape viral diversity requires approaches operating effectively under scarce data conditions. We deployed a mixed-effects modelling framework for investigating spatiotemporal mutational patterns in VEEV-infected culex mosquitoes. Intra-host diversity was modelled as a function of time since infection, tissue compartment, and protein identity, while accounting for host-specific variability. We observed substantial heterogeneity in viral diversity across hosts, indicating pronounced variation in viral population structure. Gene length and protein identity were associated with differences in diversity, suggesting heterogeneous evolutionary constraints across the viral genome. Comparisons across tissue compartments suggest differences between non-disseminated and disseminated samples, although these effects may be partially confounded by sampling time. Our results highlight the interplay between host-specific effects, genomic context, and mutation-level dynamics in shaping intra-host viral evolution. Given the inherent data scarcity associated with emerging or understudied viruses, we propose that a hybrid analytical framework combining classical statistical modeling with machine learning approaches may enable us to better capture complex patterns in such small, noisy datasets. This work showcases a foundation for developing such frameworks studying viral evolutionary dynamics for small-N viral genomic data.
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