Phylogeographic analysis of Tula hantavirus highlights a single introduction to central Europe

Abstract

Orthohantaviruses are zoonotic pathogens of humans, unique among the bunyaviruses in not being transmitted by an arthropod vector. Tula orthohantavirus (TULV) is an old-world hantavirus, of yet unclear human pathogenicity, with few reported cases of clinically relevant human infection. So far, phylogeographic studies exploring global pathways of hantaviral migration are scarce and generally do not focus on a specific hantavirus species. The aim of the present study was to reconstruct the dispersal history of TULV lineages across Eurasia, based on S segment sequences sampled from different geographic areas. Maximum likelihood and Bayesian inference methods were used to perform the phylogenetic analysis and phylogeographic reconstructions. Sampling time and trapping localities were obtained for the total of 735 TULV S segment sequences available in public databases at the time of the study. The estimated substitution rate of the analyzed partial S segment alignment was 2.26 x 10-3 substitution/site/year (95% HPD interval: 1.79 x 10-3 – 2.75 x 10-3). Continuous phylogeography of TULV S segment sequences placed the potential root and origin of TULV spread in the Black Sea region. In our study, we detect a single lineage introduction of TULV to Europe, followed by local viral circulation further on.

Genome-wide support for incipient Tula hantavirus species within a single rodent host lineage

“…2013 ). A recent study suggested a potential origin of TULV in the Black Sea region based on partial S-segment sequence data ( Cirkovic et al. 2023 ).…”

“…2023 ). The vast distribution range of TULV is only very sparsely sampled in the east ( Cirkovic et al. 2023 ), but this would indicate that the Western and Central European TULV clades originated from a single ancestral strain from Eastern Europe similar to the deeper evolutionary history of M. arvalis ( Heckel et al.…”

Deep Sequencing to Reveal Phylo-Geographic Relationships of Juquitiba Virus in Paraguay

“…We have seen this specificity with the modes employed by different hantaviruses for entry [ 91 , 92 ] and the ability of certain hantaviruses to encode non-structural proteins [ 93 ]. Studies exploring the migration patterns of rodent reservoirs with their specific hantaviruses are greatly needed, since the diversification of each hantavirus is largely a result of the migration patterns of viruses and their hosts [ 94 ]. Our study employed a continuous phylogeographical model because of issues such as the unrealistic subdivision of sampling sites, the effect of ancestor restriction to sampled sites on the inferred dispersal history, and inadequate estimates due to over- or under-sampling seen with discrete models, although the latter can also affect continuous models [ 57 , 95 , 96 , 97 , 98 ].…”

“…This represented a “bottom-up” approach to understanding hantavirus evolution as compared to the use of informative priors, which could bias inference related to the spatiotemporal dynamics of specific hantaviruses. Some studies, however, have explored the spatiotemporal dynamics of a group of hantaviruses using a discrete phylogeographic model [ 99 ], as well as a unique hantavirus, Tula virus ( Orthohantavirus tulaense , TULV), employing a continuous phylogeographic model [ 94 ].…”

How fast are viruses spreading in the wild?

“…Figure 3: comparison of dispersal metrics estimated for different genomic datasets of viruses spreading in animal populations.Specifically, we here report posterior estimates obtained for two metrics estimated from trees sampled from the posterior distribution of a Bayesian continuous phylogeographic inference: the weighted diffusion coefficient and the isolation-by-distance (IBD) signal estimated by the Pearson correlation coefficient (rP) between the patristic and log-transformed great-circle geographic distances computed for each pair of virus samples. For both metrics, we report the posterior distribution of both metrics estimated through continuous phylogeographic inference for the following datasets: West Nile virus in North America[39], Lumpy skin disease virus[11], Porcine deltacoronavirus in China[40], Getah virus in China[41], avian influenza virus (AIV) in the Mekong region[42] and H3N1 in Belgium[30], rabies virus (dogs) in Iran[37], rabies virus (bats) in Peru[36], rabies virus (dogs) in northern Africa[35,44], rabies virus (bats) in Argentina[34], rabies virus (skunks) in the USA[33], rabies virus (raccoons) in the USA[32], Tula virus in central Europe[43], rabies virus (bats) in eastern Brazil[31], Powassan virus in the USA[29], Lassa virus in Africa[28], Puumala virus in Belgium[27], and Nova virus in Belgium[26]. (*) Estimates based on the analysis of the wildtype strains (see[11] for further detail); (**) estimates based on the combined analysis of lineages L1 and L3.…”