Co-circulating TBEV lineages and evidence for vertical transmission in wild bank voles from Poland

Authors

Krupinska, M., Smura, T., Rablaski, L., Tolkacz, K., Biernat, B., Dwuznik-Szarek, D., Baranowicz, K., Maki, S., Krejmer-Rabalska, M., Baranska, K., Kant, R., Sironen, T., Vapalahti, O., Behnke, J. M., Bajer, A., Grzybek, M.

Abstract

Tick-borne encephalitis virus (TBEV) is a zoonotic flavivirus maintained in complex enzootic cycles involving ticks and vertebrate hosts. While vector-mediated transmission has been extensively studied, host-associated mechanisms, including vertical transmission and prolonged viral carriage in natural reservoir populations, remain poorly understood. Here, we investigated TBEV circulation, genetic diversity, and evidence for vertical transmission in bank voles (Clethrionomys glareolus) from a highly endemic region of North-eastern Poland. A total of 258 wild rodents were screened using molecular and serological approaches. TBEV RNA was detected in 14.3% of individuals, whereas seroprevalence was substantially lower (6.6%), revealing limited concordance between viral presence and humoral response under natural conditions. Near-complete genome sequences were obtained from 10 TBEV-positive individuals, representing the first near-complete TBEV genomes generated from wild rodents in Poland. All isolates belonged to the European subtype (TBEV-Eu), and phylogenetic analysis revealed two genetically distinct viral clades co-circulating within a narrow spatiotemporal window. Importantly, TBEV RNA and/or envelope protein were detected in embryos from naturally infected females, providing evidence consistent with vertical transmission in a wild reservoir host. Together, these findings suggest that vertical transmission and complex host infection dynamics may be underappreciated components of TBEV maintenance in natural reservoir populations. Our results highlight the need to integrate vertebrate host dynamics into models of TBEV ecology and support expanded wildlife-based surveillance to better understand and predict zoonotic risk.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 30 Jul 2026.

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