Meta-transcriptomic profiling of Ixodes ricinus and Ixodes persulcatus ticks from Finland reveals diverse RNA viromes, community structuring, and emerging viral lineages

Meta-transcriptomic profiling of Ixodes ricinus and Ixodes persulcatus ticks from Finland reveals diverse RNA viromes, community structuring, and emerging viral lineages | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Meta-transcriptomic profiling of Ixodes ricinus and Ixodes persulcatus ticks from Finland reveals diverse RNA viromes, community structuring, and emerging viral lineages Theophilus Yaw Alale, Jesse Mänttäri, Heidi M. Viitaniemi, Mikhail Ozerov, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9279202/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Tick-borne viruses (TBVs) are a threat to human and animal health in northern Europe, where Ixodes ricinus and Ixodes persulcatus are undergoing range expansion. In Finland, the genetic diversity, population structure, and tick-associated RNA virome of these species remain insufficiently characterized. Characterizing tick-borne viruses in Finland is essential to detect emerging pathogens at the northern edge of tick expansion, where climate-driven ecological change may facilitate the spread of novel viruses with potential risks to human and animal health. We applied single-tick metatranscriptomic approach to profile and compare the RNA viromes of 92 adult I. ricinus and I. persulcatus collected from two locations in Finland. We identified 49 RNA viruses spanning 12 families, with Nairoviridae, Phenuiviridae, Partitiviridae, Flaviviridae, and Rhabdoviridae being the most represented. Viral community composition differed significantly between tick species, with I. persulcatus exhibiting more constrained but bunyaviridae-dominated viromes, whereas I. ricinus showed greater inter-individual variability and enrichment of partiti-like and iflavirus taxa. Phylogenetic reconstruction of conserved viral proteins supported the presence of several divergent viral lineages and confirmed first detection of multiple Eurasian tick-associated viruses in Finland, including Nuomin virus, Gakugsa tick virus, and Onega tick phlebovirus. Several detected viruses clustered within clades that include known or suspected zoonotic agents, highlighting the need for close monitoring of tick populations, especially in zones of sympatric occurrence where hybridization and associated close contact between the tick species has been observed. These findings reveal substantial, species-structured viral diversity in Finnish Ixodes ticks. tick-borne viruses metatranscriptomics Ixodes ricinus Ixodes persulcatus viral ecology phylogenomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Ticks are obligate hematophagous ectoparasites of a wide range of vertebrate hosts, including mammals, birds, and reptiles. They are considered as the second most important vectors of human and animal pathogens worldwide after mosquitoes (Brites-Neto et al., 2015 ; Kumar et al., 2019 ; Shi et al., 2016 ). More than 900 tick species have been described, most belonging to the Ixodidae (hard ticks) and Argasidae (soft ticks) families (Horak et al., 2015 ). Many of these species are of significant veterinary and public health importance due to their role in transmitting a diverse range of pathogens, including bacteria (e.g., Borrelia , Anaplasma ), protozoa (e.g., Babesia ), and numerous viruses (e.g., TBEV). In northern Europe, Ixodes ricinus (the castor bean tick) and I. persulcatus (the taiga tick) are the dominant tick species associated with the transmission of tick-borne pathogens (Laaksonen et al., 2017 ; Lindgren et al., 2000 ). Ixodes ricinus is widespread across Western and Central Europe, extending into parts of northern Europe, including southern and coastal Finland, whereas I. persulcatus is distributed across northern and eastern Europe and in Asia (Jääskeläinen et al., 2010 ). These species overlap geographically in parts of Finland, Estonia and Russia, forming hybrid zones that may create opportunities for cross-species tick-borne pathogen exchange and genetic introgression among tick populations (Alale et al., 2024 ). Both species exhibit broad host ranges infesting humans, livestock, companion animals, and wildlife(Jongejan & Uilenberg, 2004 ; Parola & Raoult, 2001 ) making them central to the ecology of many vector-borne pathogens. Their overlapping habitats in Finland, combined with changing environmental conditions, have been linked to the increased prevalence and northward spread of tick-borne pathogens (Jääskeläinen et al., 2010 ; Zając et al., 2021 ). Tick-borne viruses (TBVs) represent a rapidly expanding group of emerging infectious agents, several of which can cause severe diseases in humans and domestic animals. These viruses include members of several RNA virus families, such as Flaviviridae, Nairoviridae, Phenuiviridae, Rhabdoviridae, Reoviridae, and Orthomyxoviridae (Bente et al., 2013 ; Charrel et al., 2004 ; Shi, Lin, Vasilakis, et al., 2016 ). Notable TBVs of medical and veterinary importance in Europe include tick-borne encephalitis virus (TBEV), Powassan virus (POWV), Louping ill virus, Omsk hemorrhagic fever virus (OHFV), and Crimean-Congo hemorrhagic fever virus (CCHFV) (Charrel et al., 2004 ; Ergunay et al., 2023 ). In Finland, the most significant TBVs reported to date include TBEV (Jääskeläinen et al., 2010 ), POWV, and the recently identified Alongshan virus (ALSV) (Kuivanen et al., 2019 ). These viruses can cause a spectrum of diseases ranging from mild febrile illness to severe neurological disorders and haemorrhagic fevers, underscoring their public health importance. Given Finland’s northern latitude, extensive forest coverage, and seasonal temperature variation, the region provides a unique ecological setting to study the diversity and evolution of TBVs under the influence of climatic and ecological change (Uusitalo et al., 2022 ; Zając et al., 2021 ). The increasing discovery of novel tick-associated viruses across Eurasia underscores the need for continued surveillance of viral diversity in regions undergoing ecological and climatic transition (Shi et al., 2016 ; Zhou et al., 2023). Therefore, understanding the viral composition of I. ricinus and I. persulcatus in Finland is particularly important due to the recent expansion in distributions, where climate change is altering tick abundance, host availability, and species overlap (Sormunen et al., 2023; Estrada-Pena and de la Fuente, 2014; Alkishe et al., 2017). Additionally, sympatric zones may promote viral exchange, reshape viral community structure and potentially influence transmission dynamics (Toorians et al., 2024; Cayol et al., 2018). Without such regional baselines, it may be difficult to detect shifts in viral diversity associated with climate change, range expansion, or ecological disturbance. Single-tick metatranscriptomics enables a fine-scale understanding of the tick virome, diversity and structure, revealing how viral communities differ between species, sexes, and environments (Martyn et al., 2024 ). The application of metatranscriptomics to individual ticks has proven particularly powerful for resolving inter-individual variation in viral carriage, co-infections, and abundance patterns (Pettersson et al., 2017 ; R. Wang et al., 2024 ). Understanding these factors is essential for predicting viral emergence and informing effective surveillance and control measures. Although metatranscriptomic studies of tick-borne viruses have been conducted elsewhere in Europe, they have focused primarily on I. ricinus (Pettersson et al., 2017 ), leaving comparative data for I. persulcatus limited. Comprehensive virome profiling across distinct ecological zones is therefore critical to assess species-specific viral structure, identify potential zoonotic threats, and clarify evolutionary relationships among tick-associated viruses. In this study, we applied a single-tick metatranscriptomic approach to investigate the viral communities of I. ricinus and I. persulcatus collected from two geographically distinct Finnish populations in Ruissalo (Turku) and Hervanta (Tampere). Specifically, we aimed to (1) identify and phylogenetically classify tick-associated RNA viruses; (2) comprehensively characterize and compare the RNA viromes of the two tick species; and (3) quantify species-specific differences in alpha and beta diversity, viral abundance, and community structure. Materials and Methods Sample Collection and RNA Extraction Adult ticks of both species were collected by cloth dragging from two geographically distinct populations: I. ricinus from Ruissalo Island near the city of Turku (60.42211° N, 22.09654° E) and I. persulcatus from Finland’s southernmost known population in Hervanta, Tampere (61.43328° N, 23.83003° E). Collections were done in May-July 2024. Ticks were collected into falcon tubes and kept alive in a chamber at room temperature with > 85% relative humidity (RH) to prevent desiccation. A total of 96 individual ticks (44 males and 52 females) were processed for metatranscriptomic analysis. Prior to RNA extraction, each tick was washed sequentially with 70% ethanol and rinsed with nuclease-free water to remove surface contaminants. Individual ticks were then flash-frozen in liquid nitrogen and homogenized in lysis buffer using sterile plastic pestles following the manufacturer’s protocol for the Zymo Research Quick-RNA™ MiniPrep Kit (catalogue number: D7001). Total RNA was extracted from each tick, eluted in 50 µL of nuclease-free water, and stored at -80°C until library preparation. RNA purity and yield were measured using both NanoDrop spectrophotometer and Qubit 4 Fluorometer (Thermo Fisher Scientific). Ribosomal RNA Depletion To enrich for viral and non-ribosomal transcripts, ribosomal RNA was depleted using the NEBNext® rRNA Depletion Kit v2 (Human/Mouse/Rat) (New England Biolabs, #E7400). The depletion protocol was carried out according to the manufacturer’s guidelines, effectively removing cytoplasmic and mitochondrial rRNA. The resulting rRNA-depleted RNA was purified using Agencourt RNAClean XP beads (Beckman Coulter) and quantified with a Qubit RNA HS Assay Kit (Thermo Fisher Scientific). Library Preparation and Sequencing Metatranscriptomic libraries were prepared from rRNA-depleted RNA using the NEBNext® Ultra II Directional RNA Library Prep Kit (New England Biolabs, #E7760L). RNA fragmentation, first- and second-strand cDNA synthesis, adapter ligation, and PCR enrichment were performed according to the manufacturer’s recommendations, with PCR cycle numbers optimized to minimize amplification bias. Library size distribution was confirmed on an Agilent 2100 Bioanalyzer, and concentrations were measured using a Qubit dsDNA HS Assay Kit. Fragment size selection was performed using E-Gel SizeSelect™ II (Invitrogen) following the manufacturer's recommendations to obtain insert sizes of 300–450 bp. Libraries were pooled equimolarly and sequenced on an Illumina NovaSeq 6000 platform at the Finnish Functional Genomics Centre, University of Turku, generating paired-end 150 bp reads. Sequencing depth was optimized to capture low-abundance viral transcripts. Bioinformatics Workflow All computational analyses were conducted on the CSC Puhti supercomputing cluster (CSC - IT Center for Science, Finland). Data processing, assembly, annotation, and statistical analyses were performed using a combination of custom bash scripts, R scripts, and standardized bioinformatics workflows. 1. Quality Control and Read Filtering Raw reads were quality-checked using FastQC (v0.11.9) (Andrew S., 2010 ) and trimmed with Trimmomatic (v0.39, Java 11) (Bolger et al., 2014) to remove adapters and low-quality bases (SLIDINGWINDOW:4:20, MINLEN:50). Only paired reads were used. Trimmed read quality was reassessed using MultiQC (Ewels et al., 2016 ). 2. Tick Host Read Removal Trimmed reads were mapped to species-specific reference genomes of I. ricinus (GenBank accession numbers PRJNA270959) (Cramaro et al., 2015 )d persulcatus (GenBank accession number PRJNA633311 (Jia et al., 2020 ) using HISAT2 (--rna-strandness FR) (v2.2.1) (Kim et al., 2019 ). Concordantly mapped reads were removed, and unmapped paired-end reads were retained for downstream metatranscriptomic analysis. Mapping statistics were extracted from alignment summary files and aggregated across samples. 3. Taxonomic Classification Unmapped reads were taxonomically classified using Kraken2 (v2.1.3)(Wood et al., 2019 ) which uses a custom k-mer-based NCBI RefSeq database containing viral, bacterial, protozoan, and archaeal genomes (updated April 2024). Classification confidence was set at 0.1. Read counts were refined using Bracken (v2.8) to estimate accurate species-level abundance. Classification reports were processed using Bracken to estimate taxon-level relative abundances at species and genus levels. Kraken2 outputs were parsed using custom Python scripts to generate per-sample taxonomic tables, which were merged into a unified abundance matrix for downstream statistical analyses. 4. Extraction of Viral Reads Virus-assigned reads were extracted following the National Microbiome Data Collaborative (NMDC) ( https://nmdc-edge.org ) (Eloe-Fadrosh et al., 2022 ; Kelliher et al., 2024 ) metagenomic workflow framework. Reads identified with Kraken2 classification outputs were extracted using KrakenTools (extract_kraken_reads.py). Those reads assigned to NCBI TaxID 10239 (Viruses) and to major viral families (including Nairoviridae, Phenuiviridae , Flaviviridae , Partitiviridae , Chuviridae ) were extracted into family-specific paired FASTQ files using automated Bash workflows. This hierarchical extraction strategy improves sensitivity for low-abundance viral taxa and reduces assembly complexity in mixed-community datasets (Shang and Sun, 2021) 5. De Novo Viral Assembly Extracted viral reads were assembled using rnaSPAdes (v3.15.5) (Bushmanova et al., 2019 ) under default parameter for stranded data. Assemblies were conducted both at the individual-sample level and, where appropriate, using pooled family-level viral read sets to enhance recovery of fragmented viral genomes. Only individual level sample assembly data were used for downstream analysis. Contigs shorter than 1000 nucleotides were excluded from further analysis to reduce spurious assemblies and non-informative fragments. Assembled viral contigs were evaluated using CheckV (Nayfach et al., 2021 ) and QUAST v5.2.0 (Gurevich et al., 2013 ), which provided assembly statistics such as N50, contig length distribution, and genome completeness estimates, contamination, and viral host integration signals. Viral contigs were categorized into quality tiers (complete, high-quality, medium-quality, or low-quality) following NMDC viral genome reporting standards. Only contigs meeting minimum completeness and quality thresholds (complete 100% completeness, high-quality ≥ 90% completeness, medium-quality (50–90% completeness, low-quality < 50% completeness) were retained for taxonomic classification and phylogenetic inference (Li et al., 2024). 6. Viral Gene Prediction and Functional Annotation Genome annotation and functional characterization of viral contigs were performed using the standardized NMDC metagenomic annotation pipeline ( https://nmdc-edge.org/home ) (Eloe-Fadrosh et al., 2022 ; Kelliher et al., 2024 ). Open reading frames (ORFs) were predicted using Prodigal in metagenomic mode. Predicted proteins were annotated using BLASTp (Altschul et al., 1990) searches against the NCBI RefSeq viral protein database, HMMER (Finn et al., 2011) searches against Pfam and viral protein family HMM profiles, and eggNOG-mapper (v2) (Cantalapiedra et al., 2021) for functional classification and ortholog assignment. Taxonomic assignment of viral contigs was refined using combined evidence from BLAST using a minimum of 70% identity and less than 1 x 10 − 5 similarity threshold, conserved domain architecture, and phylogenetic placement. Viral family classification followed the International Committee on Taxonomy of Viruses (ICTV) (Lefkowitz et al., 2018 ) consistent taxonomy used within the NMDC framework. 7. Taxonomic Confirmation and Novel Virus Assessment Taxonomic assignments were confirmed using reciprocal best-hit BLAST strategies and phylogenetic placement of conserved proteins, particularly RNA-dependent RNA polymerase (RdRp). Viral sequences showing < 75% amino acid identity to known references and forming monophyletic clades distinct from recognized species were classified as divergent or putative novel viral lineages, consistent with ICTV sequence demarcation guidelines and NMDC viral genome reporting criteria. Final viral classifications were cross validated against GenBank and recent tick virome datasets. Statistical Analyses and Visualization All statistical analyses and plotting were conducted in R (v4.3.2) (R Core Team, 2022 ) using a combination of base functions and Bioconductor packages. Viral abundance matrices generated from the Kraken2 and Bracken outputs were imported into R and normalized prior to analysis. Data manipulation and summarization were performed using the tidyverse package (Wickham et al., 2019 ). To assess within-sample diversity, alpha diversity indices including Shannon, Simpson, and species richness were calculated using the vegan R package (v2.8-0) (Oksanen et al., 2025). These indices provided insights into viral community complexity and evenness within each tick sample. Viral Community and Diversity Analyses Viral abundance tables derived from Kraken2 output and viral families based on taxonomic classification were merged into a unified abundance matrix. Alpha-diversity metrics (Shannon and Simpson indices) were calculated using the vegan R package (Oksanen et al., 2025). Beta-diversity was assessed using Bray-Curtis dissimilarity, and differences in community composition were tested using PERMANOVA with 999 permutations (Anderson & Walsh, 2013 ). Non-metric multidimensional scaling (NMDS) ordination was used to visualize inter-sample relationships. Heatmaps of viral relative abundance were generated using hierarchical clustering based on Bray-Curtis distances. Differential Abundance Analysis Differential viral abundance between tick species was assessed using DESeq2 (v1.42.0) (Love et al., 2014 ) on raw count matrices derived from classified viral reads. Counts with at least 1000 viral reads were normalized using the median-of-ratios method. Viruses with adjusted p-values (Benjamini-Hochberg) below 0.05 were considered significantly differentially abundant. Log2 fold changes were used to quantify the magnitude and direction of species-associated differences in viral abundance. Phylogenetic Analysis To infer the evolutionary relationships of viral sequences detected in tick samples, phylogenetic analyses were conducted using RNA-dependent RNA polymerase (RdRp) amino acid sequences, which represent the most conserved genomic region across RNA viruses and are widely used for virus classification. Viral contigs containing putative RdRp domains were identified from de novo assemblies based on DIAMOND BLASTp similarity searches against the NCBI non-redundant protein database with curated viral reference datasets. Corresponding reference sequences representing major viral clades and closely related taxa were retrieved from GenBank to provide phylogenetic context. Protein sequences were aligned using MAFFT v7.520 (Katoh & Standley, 2013 ) with the L-INS-i algorithm, which is optimized for accuracy in datasets with variable sequence length and moderate divergence. Alignments were manually inspected and curated using AliView v1.28 (Larsson, 2014 ) to verify reading frame consistency, identify poorly aligned regions, and confirm that conserved RdRp motifs were properly aligned. To reduce phylogenetic noise arising from ambiguously aligned or highly variable regions, alignments were trimmed using ClipKIT v1.3.0 (Steenwyk et al., 2020 ) with the “smart-gap” strategy, which removes alignment columns with excessive gaps while retaining phylogenetically informative sites. Maximum likelihood phylogenetic trees were reconstructed using IQ-TREE v2.2.2.6 (Nguyen et al., 2015 ). The best-fitting amino acid substitution model for each alignment was selected automatically using ModelFinder implemented in IQ-TREE based on Bayesian Information Criterion (BIC). Branch support was assessed using 1,000 ultrafast bootstrap replicates combined with SH-like approximate likelihood ratio tests (SH-aLRT), providing robust measures of node confidence. Nodes with ultrafast bootstrap support ≥ 95% and SH-aLRT support ≥ 80% were considered strongly supported. For the Chuviridae dataset, the best-fit model was LG + F+R6, with a RapidNJ starting tree log-likelihood of -127,568.089 and a final optimized likelihood score of -127,382.018. For the Nairoviridae dataset, the optimal model was Q.yeast + F+R6, with a RapidNJ log-likelihood of -204,087.415 and a final likelihood of -203,064.648. For Partitiviridae, ModelFinder selected LG + I+G4, yielding a RapidNJ log-likelihood of -19,180.027 and a final likelihood of -19,112.164. For Phenuiviridae, the best-fit model was Q.yeast + F+R7, with a RapidNJ log-likelihood of -181,883.814 and a final optimized likelihood of -181,478.618. Trees were inferred as either unrooted or rooted depending on the availability of appropriate outgroup sequences. When suitable distant homologs were available, trees were rooted using outgroup taxa belonging to related viral families; otherwise, midpoint rooting was applied. Tree topologies were visualized and annotated using Interactive Tree Of Life (iTOL) v6 (Letunic & Bork, 2024 ), allowing integration of metadata such as tick species, geographic origin, and viral family assignments. Branches corresponding to Finnish sequences were highlighted to facilitate comparison with previously described viral lineages. Phylogenetic placement of Finnish viral sequences was used to assess taxonomic relationships, identify clustering with known tick-associated virus clades, and evaluate genetic divergence relative to established species. Sequences that formed well-supported monophyletic groups distinct from described taxa were considered putative novel or divergent viral lineages, pending formal taxonomic classification following ICTV guidelines. Results Read Processing and Quality Control Metatranscriptomic sequencing was completed for 92 individual adult ticks, comprising 46 I. ricinus (23 females, 23 males) and 46 I. persulcatus (23 females, 23 males). Libraries passed all platform quality metrics and no samples were excluded. Mean Phred quality scores exceeded 34 across all libraries, indicating high base-calling accuracy. After adapter removal and quality trimming, reads were filtered using stringent Phred score thresholds and processed through the NMDC metatranscriptomic workflow. Tick read removal using reference-guided mapping resulted in mean tick-aligned fractions of 89.4% ± 3.2% for I. ricinus and 91.1% ± 2.6% for I. persulcatus (Supplementary Figs. S1-S2). The final retained non-tick reads that did not align to tick reference genome averaged 3.2 × 10⁶ ± 0.4 × 10⁶ for I. ricinus samples and 1.01 × 10⁶ ± 0.2 × 10⁶ for I. persulcatus samples, were used for downstream analysis. The average microbial component of total non-tick read after quality control for I. ricinus for example was 1.37 x 10 6 ± 0.1 × 10⁶. The summary composition report of non-tick reads is shown in Supplementary Fig. S3 Overview of Viral Diversity Across Sampled Ixodes Ticks Across all samples, 49 distinct RNA viruses spanning 12 viral families were identified (Supplementary, Table S1 ). Stacked relative abundance profiles showed consistent but species-dependent viral community structure (Fig. 1 ), and mean family and species-level abundance summaries further highlighted dominant taxa (Supplementary Fig.S4). Each sample had at least one virus (Supplementary Fig. S5). The most represented families were Nairoviridae (11 taxa), Phenuiviridae (9), Partitiviridae (7), Flaviviridae (4), and Orthomyxoviridae (3). Individual ticks harboured an average of 10.9 ± 3.2 viral taxa, indicating substantial viral diversity at the single-tick level (Fig. 1 ). A small subset of viruses accounted for a large fraction of total viral reads, whereas many taxa were present at low abundance and low prevalence. Prevalence and Abundance Structure of Dominant Viruses Prevalence-abundance analysis revealed a skewed distribution dominated by a small number of highly prevalent viruses (Fig. 2 ). Nine taxa were detected in more than 20% of ticks. Beiji nairovirus was nearly ubiquitous (96% prevalence), followed by Jilin partiti-like virus 1 (89%), Sara tick phlebovirus (85%), and Gakugsa tick virus (82%). Five taxa accounted for the largest share of total viral reads across the dataset: Beiji nairovirus (26.5%), Sara tick phlebovirus (18.7%), Onega tick phlebovirus (12.4%), Gakugsa tick virus (11.6%), and Jilin partiti-like virus 1 (15.3%) (Figs. 1 – 3 ). Low-prevalence viruses, including Hepacivirus hominis, Kizhi virus, and SARS-related coronavirus-like fragments (SARSC), were detected in at least 10% of ticks, but at very low coverage, typically represented by short contigs and sparse read support (Figs. 2 & 3 ). These detections were retained only when supported by protein-domain matches and phylogenetic placement. Species-Specific Viral Signatures and Differential Abundance Hierarchical clustering demonstrated strong species-level segregation of viral abundance profiles (Supplementary Fig. S5). Samples clustered primarily by tick species rather than sex. Ixodes persulcatus viromes were enriched in Nairoviridae (34.8% ± 9.7) and Phenuiviridae (22.5% ± 7.6), whereas I. ricinus viromes were enriched in Partitiviridae (21.4% ± 5.9) and Iflaviridae (18.6% ± 4.2). The I. persulcatus virome was dominated by Beiji nairovirus, Sara tick phlebovirus, and Onega tick phlebovirus, together accounting for 57.6% of viral reads. In contrast, I. ricinus showed a more even distribution across several taxa. The caveat in our analysis was that one tick species was collected in one location, and another tick species in another location. Thus, the comparisons between tick species are also comparisons between locations. Differential abundance testing using DESeq2 identified 15 viruses with higher interspecies differences after multiple-testing correction (Benjamini-Hochberg adjusted p < 0.05; Supplementary, Table S2). Viruses enriched in I. persulcatus included Kizhi virus, Jilin partiti-like virus 1, Gakugsa tick virus, Onega tick phlebovirus, and Sara tick phlebovirus. Viruses enriched in I. ricinus included Norwavirus grotenhoutense, Bronnoya virus, Leuven phlebovirus, Betaricinrhavirus chimay, and Norway partiti-like virus 1. Beiji nairovirus and Pustyn virus showed no significant species bias, suggesting broader tick associations (Fig. 3 ). Viral Load, Richness, Alpha Diversity, and Community Structure Total viral read abundance and viral richness varied across tick species and sex categories (Fig. 4 A & B). Boxplot comparisons indicated broader dispersion of viral signal and richness values in I. ricinus compared with I. persulcatus , consistent with greater inter-individual variability in viral composition observed in this species. Although both species harbored diverse viral assemblages, species-level differences were more pronounced than sex-level differences (Supplementary Table 3). Alpha diversity analysis based on the Shannon index showed significantly higher viral diversity in I. ricinus (H = 2.84 ± 0.52) than in I. persulcatus (H = 2.36 ± 0.41; Welch’s t = 2.41, p = 0.018) (Fig. 4 C). In contrast, Simpson’s diversity index did not differ significantly between species (p = 0.145), indicating that the observed difference was driven primarily by richness and evenness rather than dominance by a single viral taxon. Together, these metrics support a more even and taxonomically diverse virome in I. ricinus . Sex-associated effects on alpha diversity were limited and species-dependent. Female I. persulcatus harboured a higher number of viral taxa than males (12.4 ± 2.8 vs. 9.7 ± 2.2; p = 0.046), whereas no significant sex-based difference was detected in I. ricinus (p = 0.273) (Fig. 4 C). Across all diversity metrics, sex explained substantially less variation than species identity (Supplement, Table 3). Community-level analyses demonstrated that tick species identity was the primary determinant of viral composition (PERMANOVA p = 0.001). The differences were further supported by non-metric multidimensional scaling (NMDS) ordination of Bray-Curtis dissimilarities (stress = 0.183), which demonstrated species-associated clustering of viral communities (Fig. 4 D). Samples from I. ricinus and I. persulcatus formed partially separated clusters with limited overlap, whereas male and female ticks overlapped extensively within species clusters. This pattern indicates that tick species identity, rather than sex, is the dominant determinant of viral community composition. The ordination results are consistent with PERMANOVA testing showing a significant species effect and weaker or non-significant sex effects on overall virome structure (Supplementary Table 3A-C). Phylogenetic Placement Confirms Arthropod-Associated Lineages Maximum-likelihood phylogenetic reconstruction of RdRp amino acid sequences placed all Finnish viral sequences within established arthropod-associated viral clades (Figs. 5 – 8 ). Tree topologies were stable across model selection and bootstrap resampling, and alignments were manually inspected to remove poorly aligned regions prior to inference. Importantly, none of the Finnish sequences clustered within well-characterized vertebrate-adapted virus clades, supporting their classification as components of the tick-associated virome rather than misassigned vertebrate pathogens. Finnish nairovirus sequences grouped within tick-associated orthonairovirus clades (Fig. 5 ). Fin_nairovirus_Rdrp showed 98.01% amino acid identity with Beiji orthonairovirus, 97.87% with Gakugsa tick virus, and 87.95% with Yichun nairovirus, supporting assignment to a Eurasian tick-associated nairovirus lineage. Two distinct phlebovirus lineages were resolved (Fig. 6 ). Fin_Phlebovirus_Rdrp-A was nearly identical to Onega tick phlebovirus (99.63% identity, accession number: XCO66288.1), whereas Fin_Phlebovirus-B clustered with Sara tick phlebovirus (99.47% identity) and showed lower similarity to Fairhair virus (89.29%) and Shoal Cavern virus (78.20%). Partiti-like viruses formed a strongly supported tick-associated clade distinct from plant and fungal partitiviruses (Fig. 7 ). The Finnish partiti-like sequence showed 100% identity to Jilin partiti-like virus 1 and 94.42% identity to Norway partiti-like virus 1. A Finnish chuvirus-related sequence clustered within tick-associated Chuviridae lineages (Fig. 8 ). The RdRp segment showed 99.36% and 99.34% identity to Nuomin virus (accession number: UKS70436.1) and Lesnoe virus (accession number: WAS28088.1), respectively, and substantially lower identity (77.24%) to Deer tick mononegavirales-like virus (accession number: AIE42676.2), supporting placement within a Nuomin-like lineage. Discussion This study provides a high-resolution, single-tick metatranscriptomic comparison of RNA viromes associated with Ixodes ricinus and Ixodes persulcatus in Finland and establishes a detailed baseline for tick-associated viral diversity in the boreal region. Across 92 individually sequenced adult ticks, we identified 49 RNA viruses spanning 12 viral families, including multiple divergent lineages supported by conserved domain architecture, read remapping, and model-tested phylogenetic placement (Figs. 1 – 8 ). The consistent detection of diverse RNA viruses across both tick species reinforces the view that Ixodes ticks maintain high levels of RNA virus diversity in northern ecosystems, in agreement with tick-borne viral surveys from Europe and Asia (Bratuleanu et al., 2023; Pettersson et al., 2017 ; Shi et al., 2016 ; Wang et al., 2023 ) This finding is consistent with previous evidence that tick species identity is a dominant determinant of viral community assembly (Meng et al., 2019 ; Pettersson et al., 2017 ), whereas tick sex had only minor independent effects with a weak interaction component (Supplement, Table 1A-C; Figs. 2 – 4 ). We found a strong, statistically supported divergence in virome composition between I. ricinus and I. persulcatus sampled at different geographical locations. Community-level analyses demonstrated that tick species identity was the primary determinant of viral composition. NMDS ordination showed species-associated clustering with partial but not complete separation and acceptable ordination stress, indicating robust but not absolute community differentiation (Fig. 4 D). Ixodes persulcatus displayed lower overall viral diversity but stronger clustering within β -diversity analyses, suggesting specialized viral associations or narrower ecological interactions. Similar species-driven structuring has been observed in Russian and Chinese tick populations, where I. persulcatus harbours a homogeneous virome dominated by bunyaviruses and phleboviruses (Kholodilov et al., 2022 ; R. Wang et al., 2024 ). Ixodes persulcatus samples formed a more compact cluster, whereas I. ricinus samples were more widely dispersed, consistent with greater inter-individual virome variability. Alpha diversity metrics further supported this contrast: I. ricinus exhibited significantly higher Shannon diversity especially in males, while Simpson indices did not differ significantly, indicating that between-species differences were driven primarily by richness and evenness rather than dominance by single taxa (Fig. 4 A & B). These patterns remained stable after normalization, arguing against sequencing depth as a confounder. The taxonomic structure of the viromes differed markedly between species. Ixodes persulcatus was strongly dominated by Nairoviridae and Phenuiviridae, whereas I. ricinus showed higher relative representation of Partitiviridae and Iflaviridae (Figs. 1 & 3 ). A small number of viruses dominated total abundance and prevalence, particularly Beiji nairovirus, Sara tick phlebovirus, Onega tick phlebovirus, Gakugsa tick virus, and Jilin partiti-like virus 1. Several of these viruses showed high prevalence across individuals, with Beiji nairovirus detected in nearly all ticks, while others exhibited significant species enrichment patterns in differential abundance testing. The strong enrichment of Nairoviridae and Phenuiviridae members in I. persulcatus underscores its zoonotic importance across Eurasia. The frequent detection of Beiji nairovirus and Sara tick phlebovirus previously described in northeastern China, Japan and Siberia (Kholodilov et al., 2022 ; Kishimoto et al., 2024 ; Li et al., 2015 ; Tokarz et al., 2018 ) may suggests a wider distribution facilitated by migratory vertebrate hosts and or overlapping vector populations. These viruses are phylogenetically related to high-consequence human pathogens such as Crimean-Congo hemorrhagic fever virus (CCHFV) and severe fever with thrombocytopenia syndrome virus (SFTSV), which are maintained by ticks of a different genus (Ma et al., 2021 ; Shimada et al., 2016 ). Their detection in Finnish I. persulcatus populations suggests either ancient viral persistence in northern ecosystems or recent introductions linked to expanding tick distributions. At the same time, some highly prevalent viruses did not show strong species bias, suggesting broader tick compatibility within sampled tick populations. Conversely, the more taxonomically diverse but lower-abundance virome of I. ricinus dominated by Jilin partiti-like virus 1 and Gakugsa tick virus could represent long-term tick-virus coadaptation without known vertebrate pathogenicity (Gould et al., 2003). Phylogenomic reconstruction based on RdRp amino acid sequences confirmed that Finnish viral sequences fall within established arthropod-associated clades across Nairoviridae, Phenuiviridae, Partitiviridae, Rhabdoviridae (Suppplementary material Fig. 4 ) and Chuviridae and do not cluster with recognized vertebrate-common virus lineages (Figs. 5 – 8 ). These findings reflect the ongoing evolution of tick-associated RNA viruses, driven by frequent reassortment, viral host shifts, and environmental pressures (Shi et al., 2016 ). Notably, Kizhi virus, Gakugsa tick virus, and Onega tick phlebovirus are reported here for the first time in Finland, extending their known ranges from Russia, Eastern Europe and East Asia. These findings mirror recent discoveries of related viruses across Eurasia, supporting a model of transcontinental viral exchange mediated by migratory birds, large mammals, and climate-driven vector range expansion (Bratuleanu et al., 2023; Kholodilov et al., 2022 ; Ma et al., 2021 ; Tokarz et al., 2018 ).. From a zoonotic risk perspective, several detected viruses warrant careful but conservative interpretation. Beiji nairovirus and Yichun nairovirus were common and phylogenetically nested within tick-associated nairovirus diversity (Fig. 5 ). Although these sequences cluster within arthropod-associated branches rather than known high-pathogenicity clades, nairoviruses as a broader group include important human and livestock pathogens with reported incidence of human febrile illness in China (Wang et al., 2021 ). Their high prevalence increases opportunities for evolutionary diversification, even if current evidence does not indicate pathogenicity. The Nuomin-like chuvirus similarly represents a lineage with documented vertebrate exposure but unresolved clinical significance (Fig. 8 ). These findings highlight evolutionary proximity and surveillance relevance rather than demonstrated public health threat. Other vertebrate-associated virus signals were detected only at very low abundance and with fragmented genome coverage. Hepacivirus hominis -like reads most plausibly reflect residual viral RNA from vertebrate blood meals rather than productive tick infection. Likewise, sporadic reads assigned to SARS-related coronavirus-like sequences and Alphainfluenzavirus influenzae lacked genome-wide coverage, replication signatures, and stable phylogenetic placement. While there was no evidence of active replication, their presence underscores the remarkable sensitivity of metatranscriptomic methods for detecting low-level or transient viral fragments. Similar incidental findings of coronavirus-like sequences have been reported in global arthropod virome surveys (Nekoei et al., 2022 ; Shi et al., 2016 ). These are generally attributed to environmental contamination or ingestion of tick host blood containing vertebrate viral RNA rather than true tick infection. Although this result does not suggest that Ixodes ticks serve as coronavirus vectors, it highlights the necessity of stringent data interpretation in metavirome studies and the potential of such surveillance to detect unexpected viral lineages at the human-wildlife interface. Ecological differences between I. ricinus and I. persulcatus likely contribute to the observed virome divergence. Ixodes persulcatus occupies more colder continental habitats and is associated with relatively stable wildlife host communities (Bugmyrin et al., 2011 ; Cotes-Perdomo et al., 2025 ; Wang et al., 2023 ), conditions that may favour persistence of specialized viral assemblages dominated by bunyavirus-related taxa. Similar patterns of specialization have been observed in Haemaphysalis longicornis and Dermacentor silvarum , where stable viral consortia persist across vast geographic ranges (Jia et al., 2020 ; S. Wang et al., 2020 ). Meanwhile, I. ricinus , occupying more heterogeneous habitats and feeding on a wider variety of hosts, maintains a more variable virome reflecting opportunistic viral acquisition (Estrada-Peña & de la Fuente, 2017 ; Vanmechelen et al., 2021 ). Despite this finding, we note that the between species differences may likely driven by two separate sampling spots and thus ecological differentiation is also hard to decipher from the results (and differences between sexes). The higher viral richness in female I. persulcatus may result from previous blood-feeding events and prolonged exposure to diverse hosts or environments, consistent with observations from other Eurasia tick populations (Li et al., 2015 ; Pettersson et al., 2017 ; Reuben Kaufman, 2007 ). Several viral genomes showed less than 75% nucleotide similarity to available references and formed distinct, well-supported phylogenetic branches (Figs. 5 – 8 ). We conservatively describe these as putatively novel or highly divergent tick-associated viruses pending full genome resolution and biological validation. Multi-region coverage, conserved domains, and consistent phylogenetic placement support their authenticity and argue against assembly artefacts. Conclusion Finnish Ixodes ticks harbor a diverse and strongly species-structured RNA virome composed predominantly of arthropod-associated viral lineages, including several with evolutionary links to zoonotic virus groups. The presence of high-prevalence nairovirus lineages and a Nuomin-like chuvirus, together with ongoing tick range expansion under climate change, reinforces the need for continuous surveillance. Longitudinal integration of viromics, vector ecology, and tick host data will be essential for anticipating shifts in the tick-associated viral risk landscape. Although this study provides a high-resolution snapshot of the RNA viromes of I. ricinus and I. persulcatus in Finland, metatranscriptomic detection alone does not demonstrate viral replication, transmissibility, or pathogenic potential. Some low-abundance viral signals may reflect residual blood-meal RNA or environmental contamination rather than active tick infection. In addition, sampling was restricted to two geographic locations and a single life stage, limiting broader ecological inference. Declarations Author Contribution All authors Conceptualised the idea. T.Y.A., J.M. & H.M.V. processed the samples and carried out all laboratory work. T.Y.A. processed and analysed the data, and wrote the main manuscript, M.O & M.B-S. contributed to data analysis and manuscript revision, J.J.S. & E.J.V. supervised the project, provided funding and reviewed the manuscript. Data Availability Raw sequencing data have been deposited in the NCBI Sequence Read Archive under the BioProject accession number PRJNA1444502: https://www.ncbi.nlm.nih.gov/sra/PRJNA1444502. Analysis scripts and workflow documentation are available at GitHub repository: https://github.com/theoalal/Bioinformatics_23.git. Acknowledgement We would like to thank members of the university of Turku tick research group for their immense support in helping us get this work done. Many thanks to Dr. Tero Klemola of the department of biology, University of Turku, for providing guidance and giving comments during the preparatory stages of the research. We would also like to thank the Turku University Foundation for providing a research grant to get this work through. References Alale, T. Y., Sormunen, J. J., Vesterinen, E. J., Klemola, T., Knott, K. E., & Baltazar-Soares, M. (2024). Genomic signatures of hybridization between Ixodes ricinus and Ixodes persulcatus in natural populations. Ecology and Evolution , 14 (5). https://doi.org/10.1002/ece3.11415 Alburkat, H. A. T., Pulkkinen, E., Virtanen, J., Vapalahti, O., Sironen, T., & Jääskeläinen, A. J. (2024). Serological and molecular screening of arenaviruses in suspected tick-borne encephalitis cases in Finland. Epidemiology and Infection , 152 . https://doi.org/10.1017/S0950268824000128 Anderson, M. J., & Walsh, D. C. I. (2013). PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? Ecological Monographs , 83 (4). https://doi.org/10.1890/12-2010.1 Andrew S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data . http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Bente, D. A., Forrester, N. L., Watts, D. M., McAuley, A. J., Whitehouse, C. A., & Bray, M. (2013). Crimean-Congo hemorrhagic fever: History, epidemiology, pathogenesis, clinical syndrome and genetic diversity. In Antiviral Research (Vol. 100, Number 1). https://doi.org/10.1016/j.antiviral.2013.07.006 Brites-Neto, J., Duarte, K. M. R., & Martins, T. F. (2015). Tick-borne infections in human and animal population worldwide. Veterinary World , 8 (3). https://doi.org/10.14202/vetworld.2015.301-315 Bugmyrin, S., Hokkanen, T. J., Romanova, L., Bespyatova, L., Fyodorov, F., Burenkova, L., Yakimova, A., & Ieshko, E. (2011). Ixodes persulcatus [Schulze 1930] (Acari: Ixodidae) in eastern Finland. Entomologica Fennica , 22 (4). https://doi.org/10.33338/ef.84556 Bushmanova, E., Antipov, D., Lapidus, A., & Prjibelski, A. D. (2019). RnaSPAdes: A de novo transcriptome assembler and its application to RNA-Seq data. GigaScience , 8 (9). https://doi.org/10.1093/gigascience/giz100 Charrel, R. N., Attoui, H., Butenko, A. M., Clegg, J. C., Deubel, V., Frolova, T. V., Gould, E. A., Gritsun, T. S., Heinz, F. X., Labuda, M., Lashkevich, V. A., Loktev, V., Lundkvist, A., Lvov, D. V., Mandl, C. W., Niedrig, M., Papa, A., Petrov, V. S., Plyusnin, A., … de Lamballerie, X. (2004). Tick-borne virus diseases of human interest in Europe. In Clinical Microbiology and Infection (Vol. 10, Number 12). https://doi.org/10.1111/j.1469-0691.2004.01022.x Cotes-Perdomo, A., Soleng, A., Alfsnes, K., & Andreassen, Å. (2025). First report of the taiga tick Ixodes persulcatus in Norway. Ticks and Tick-Borne Diseases , 16 (4). https://doi.org/10.1016/j.ttbdis.2025.102508 Cramaro, W. J., Revets, D., Hunewald, O. E., Sinner, R., Reye, A. L., & Muller, C. P. (2015). Integration of Ixodes ricinus genome sequencing with transcriptome and proteome annotation of the naïve midgut. BMC Genomics , 16 (1). https://doi.org/10.1186/s12864-015-1981-7 Eloe-Fadrosh, E. A., Ahmed, F., Anubhav, Babinski, M., Baumes, J., Borkum, M., Bramer, L., Canon, S., Christianson, D. S., Corilo, Y. E., Davenport, K. W., Davis, B., Drake, M., Duncan, W. D., Flynn, M. C., Hays, D., Hu, B., Huntemann, M., Kelliher, J., … Fagnan, K. (2022). The National Microbiome Data Collaborative Data Portal: An integrated multi-omics microbiome data resource. Nucleic Acids Research , 50 (D1). https://doi.org/10.1093/nar/gkab990 Ergunay, K., Bourke, B. P., Reinbold-Wasson, D. D., Nikolich, M. P., Nelson, S. P., Caicedo-Quiroga, L., Vaydayko, N., Kirkitadze, G., Chunashvili, T., Long, L. S., Blackburn, J. K., Cleary, N. G., Tucker, C. L., & Linton, Y. M. (2023). The expanding range of emerging tick-borne viruses in Eastern Europe and the Black Sea Region. Scientific Reports , 13 (1). https://doi.org/10.1038/s41598-023-46879-2 Estrada-Peña, A., & de la Fuente, J. (2017). Host distribution does not limit the range of the tick ixodes ricinus but impacts the circulation of transmitted pathogens. Frontiers in Cellular and Infection Microbiology , 7 (OCT). https://doi.org/10.3389/fcimb.2017.00405 Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics , 32 (19), 3047–3048. https://doi.org/10.1093/bioinformatics/btw354 Gurevich, A., Saveliev, V., Vyahhi, N., & Tesler, G. (2013). QUAST: Quality assessment tool for genome assemblies. Bioinformatics , 29 (8). https://doi.org/10.1093/bioinformatics/btt086 Horak, I. G., Jordaan, A. J., Nel, P. J., van Heerden, J., Heyne, H., & van Dalen, E. M. (2015). Distribution of endemic and introduced tick species in Free State Province, South Africa. Journal of the South African Veterinary Association , 86 (1). https://doi.org/10.4102/jsava.v86i1.1255 Jääskeläinen, A. E., Sironen, T., Murueva, G. B., Subbotina, N., Alekseev, A. N., Castrén, J., Alitalo, I., Vaheri, A., & Vapalahti, O. (2010). Tick-borne encephalitis virus in ticks in Finland, Russian Karelia and Buryatia. Journal of General Virology , 91 (11). https://doi.org/10.1099/vir.0.023663-0 Jia, N., Wang, J., Shi, W., Du, L., Sun, Y., Zhan, W., Jiang, J. F., Wang, Q., Zhang, B., Ji, P., Bell-Sakyi, L., Cui, X. M., Yuan, T. T., Jiang, B. G., Yang, W. F., Lam, T. T. Y., Chang, Q. C., Ding, S. J., Wang, X. J., … Cao, W. C. (2020). Large-Scale Comparative Analyses of Tick Genomes Elucidate Their Genetic Diversity and Vector Capacities. Cell , 182 (5). https://doi.org/10.1016/j.cell.2020.07.023 Jongejan, F., & Uilenberg, G. (2004). The global importance of ticks. In Parasitology (Vol. 129, Number SUPPL.). https://doi.org/10.1017/S0031182004005967 Katoh, K., & Standley, D. M. (2013). MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Molecular Biology and Evolution , 30 (4). https://doi.org/10.1093/molbev/mst010 Kelliher, J. M., Xu, Y., Flynn, M. C., Babinski, M., Canon, S., Cavanna, E., Clum, A., Corilo, Y. E., Fujimoto, G., Giberson, C., Johnson, L. Y. D., Li, K. J., Li, P. E., Li, V., Lo, C. C., Lynch, W., Piehowski, P., Prime, K., Purvine, S., … Chain, P. S. G. (2024). Standardized and accessible multi-omics bioinformatics workflows through the NMDC EDGE resource. Computational and Structural Biotechnology Journal , 23 . https://doi.org/10.1016/j.csbj.2024.09.018 Kholodilov, I. S., Belova, O. A., Ivannikova, A. Y., Gadzhikurbanov, M. N., Makenov, M. T., Yakovlev, A. S., Polienko, A. E., Dereventsova, A. V., Litov, A. G., Gmyl, L. V., Okhezin, E. V., Luchinina, S. V., Klimentov, A. S., & Karganova, G. G. (2022). Distribution and Characterisation of Tick-Borne Flavi-, Flavi-like, and Phenuiviruses in the Chelyabinsk Region of Russia. Viruses , 14 (12). https://doi.org/10.3390/v14122699 Kim, D., Paggi, J. M., Park, C., Bennett, C., & Salzberg, S. L. (2019). Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology , 37 (8), 907–915. https://doi.org/10.1038/s41587-019-0201-4 Kishimoto, M., Itakura, Y., Tabata, K., Komagome, R., Yamaguchi, H., Ogasawara, K., Nakao, R., Qiu, Y., Sato, K., Kawabata, H., Kajihara, M., Monma, N., Seto, J., Shigeno, A., Horie, M., Sasaki, M., Hall, W. W., Sawa, H., Orba, Y., & Matsuno, K. (2024). A wide distribution of Beiji nairoviruses and related viruses in Ixodes ticks in Japan. Ticks and Tick-Borne Diseases , 15 (6). https://doi.org/10.1016/j.ttbdis.2024.102380 Kuivanen, S., Levanov, L., Kareinen, L., Sironen, T., Jääskeläinen, A. J., Plyusnin, I., Zakham, F., Emmerich, P., Schmidt-Chanasit, J., Hepojoki, J., Smura, T., & Vapalahti, O. (2019). Detection of novel tick-borne pathogen, alongshan virus, in Ixodes ricinus ticks, south-eastern Finland, 2019. Eurosurveillance , 24 (27). https://doi.org/10.2807/1560-7917.ES.2019.24.27.1900394 Kumar, M., Sharma, A., & Grover, P. (2019). Triple Tick Attack. Cureus . https://doi.org/10.7759/cureus.4064 Laaksonen, M., Sajanti, E., Sormunen, J. J., Penttinen, R., Hänninen, J., Ruohomäki, K., Sääksjärvi, I., Vesterinen, E. J., Vuorinen, I., Hytönen, J., & Klemola, T. (2017). Crowdsourcing-based nationwide tick collection reveals the distribution of Ixodes ricinus and I. persulcatus and associated pathogens in Finland. Emerging Microbes and Infections , 6 (5). https://doi.org/10.1038/emi.2017.17 Larsson, A. (2014). AliView: A fast and lightweight alignment viewer and editor for large datasets. Bioinformatics , 30 (22). https://doi.org/10.1093/bioinformatics/btu531 Lefkowitz, E. J., Dempsey, D. M., Hendrickson, R. C., Orton, R. J., Siddell, S. G., & Smith, D. B. (2018). Virus taxonomy: The database of the International Committee on Taxonomy of Viruses (ICTV). Nucleic Acids Research , 46 (D1). https://doi.org/10.1093/nar/gkx932 Letunic, I., & Bork, P. (2024). Interactive Tree of Life (iTOL) v6: Recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Research , 52 (W1). https://doi.org/10.1093/nar/gkae268 Li, C. X., Shi, M., Tian, J. H., Lin, X. D., Kang, Y. J., Chen, L. J., Qin, X. C., Xu, J., Holmes, E. C., & Zhang, Y. Z. (2015). Unprecedented genomic diversity of RNA viruses in arthropods reveals the ancestry of negative-sense RNA viruses. ELife , 2015 (4). https://doi.org/10.7554/eLife.05378 Lindgren, E., Tälleklint, L., & Polfeldt, T. (2000). Impact of climatic change on the northern latitude limit and population density of the disease-transmitting European tick Ixodes ricinus. Environmental Health Perspectives , 108 (2). https://doi.org/10.2307/3454509 Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology , 15 (12). https://doi.org/10.1186/s13059-014-0550-8 Ma, J., Lv, X. L., Zhang, X., Han, S. Z., Wang, Z. D., Li, L., Sun, H. T., Ma, L. X., Cheng, Z. L., Shao, J. W., Chen, C., Zhao, Y. H., Sui, L., Liu, L. N., Qian, J., Wang, W., & Liu, Q. (2021). Identification of a new orthonairovirus associated with human febrile illness in China. Nature Medicine , 27 (3). https://doi.org/10.1038/s41591-020-01228-y Martyn, C., Hayes, B. M., Lauko, D., Midthun, E., Castaneda, G., Bosco-Lauth, A., Salkeld, D. J., Kistler, A., Pollard, K. S., & Chou, S. (2024). Metatranscriptomic investigation of single Ixodes pacificus ticks reveals diverse microbes, viruses, and novel mRNA-like endogenous viral elements . MSystems , 9 (6). https://doi.org/10.1128/msystems.00321-24 Meng, F., Ding, M., Tan, Z., Zhao, Z., Xu, L., Wu, J., He, B., & Tu, C. (2019). Virome analysis of tick-borne viruses in Heilongjiang Province, China. Ticks and Tick-Borne Diseases , 10 (2). https://doi.org/10.1016/j.ttbdis.2018.12.002 Namina, A., Kazarina, A., Lazovska, M., Akopjana, S., Ulanova, V., Kivrane, A., Freimane, L., Sadovska, D., Kimsis, J., Bormane, A., Capligina, V., & Ranka, R. (2023). Comparative Microbiome Analysis of Three Epidemiologically Important Tick Species in Latvia. Microorganisms , 11 (8). https://doi.org/10.3390/microorganisms11081970 Nayfach, S., Camargo, A. P., Schulz, F., Eloe-Fadrosh, E., Roux, S., & Kyrpides, N. C. (2021). CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nature Biotechnology , 39 (5). https://doi.org/10.1038/s41587-020-00774-7 Nekoei, S., Khamesipour, F., Benchimol, M., Bueno-Marí, R., & Ommi, D. (2022). SARS-CoV-2 Transmission by Arthropod Vectors: A Scoping Review. In BioMed Research International (Vol. 2022). https://doi.org/10.1155/2022/4329423 Nguyen, L. T., Schmidt, H. A., Von Haeseler, A., & Minh, B. Q. (2015). IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Molecular Biology and Evolution , 32 (1). https://doi.org/10.1093/molbev/msu300 Parola, P., & Raoult, D. (2001). Tick-borne bacterial diseases emerging in Europe. Clinical Microbiology and Infection , 7 (2). https://doi.org/10.1046/j.1469-0691.2001.00200.x Pettersson, J. H. O., Shi, M., Bohlin, J., Eldholm, V., Brynildsrud, O. B., Paulsen, K. M., Andreassen, Å., & Holmes, E. C. (2017). Characterizing the virome of Ixodes ricinus ticks from northern Europe. Scientific Reports , 7 (1). https://doi.org/10.1038/s41598-017-11439-y R Core Team. (2022). R Core Team 2021 R: A language and environment for statistical computing. R foundation for statistical computing. https://www.R-project.org/. R Foundation for Statistical Computing , 2 . Reuben Kaufman, W. (2007). Gluttony and sex in female ixodid ticks: How do they compare to other blood-sucking arthropods? Journal of Insect Physiology , 53 (3). https://doi.org/10.1016/j.jinsphys.2006.10.004 Shi, M., Lin, X. D., Tian, J. H., Chen, L. J., Chen, X., Li, C. X., Qin, X. C., Li, J., Cao, J. P., Eden, J. S., Buchmann, J., Wang, W., Xu, J., Holmes, E. C., & Zhang, Y. Z. (2016). Redefining the invertebrate RNA virosphere. Nature , 540 (7634). https://doi.org/10.1038/nature20167 Shi, M., Lin, X.-D., Vasilakis, N., Tian, J.-H., Li, C.-X., Chen, L.-J., Eastwood, G., Diao, X.-N., Chen, M.-H., Chen, X., Qin, X.-C., Widen, S. G., Wood, T. G., Tesh, R. B., Xu, J., Holmes, E. C., & Zhang, Y.-Z. (2016). Divergent Viruses Discovered in Arthropods and Vertebrates Revise the Evolutionary History of the Flaviviridae and Related Viruses. Journal of Virology , 90 (2). https://doi.org/10.1128/jvi.02036-15 Shimada, S., Aoki, K., Nabeshima, T., Fuxun, Y., Kurosaki, Y., Shiogama, K., Onouchi, T., Sakaguchi, M., Fuchigami, T., Ono, H., Nishi, K., Posadas-Herrera, G., Uchida, L., Takamatsu, Y., Yasuda, J., Tsutsumi, Y., Fujita, H., Morita, K., & Hayasaka, D. (2016). Tofla virus: A newly identified Nairovirus of the Crimean-Congo hemorrhagic fever group isolated from ticks in Japan. Scientific Reports , 6 . https://doi.org/10.1038/srep20213 Steenwyk, J. L., Buida, T. J., Li, Y., Shen, X. X., & Rokas, A. (2020). ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference. PLoS Biology , 18 (12). https://doi.org/10.1371/journal.pbio.3001007 Tokarz, R., Sameroff, S., Tagliafierro, T., Jain, K., Williams, S. H., Cucura, D. M., Rochlin, I., Monzon, J., Carpi, G., Tufts, D., Diuk-Wasser, M., Brinkerhoff, J., & Lipkin, W. I. (2018). Identification of Novel Viruses in Amblyomma americanum , Dermacentor variabilis, and Ixodes scapularis Ticks . MSphere , 3 (2). https://doi.org/10.1128/msphere.00614-17 Uusitalo, R., Siljander, M., Lindén, A., Sormunen, J. J., Aalto, J., Hendrickx, G., Kallio, E., Vajda, A., Gregow, H., Henttonen, H., Marsboom, C., Korhonen, E. M., Sironen, T., Pellikka, P., & Vapalahti, O. (2022). Predicting habitat suitability for Ixodes ricinus and Ixodes persulcatus ticks in Finland. Parasites and Vectors , 15 (1). https://doi.org/10.1186/s13071-022-05410-8 Vanmechelen, B., Merino, M., Vergote, V., Laenen, L., Thijssen, M., Martí-Carreras, J., Claerebout, E., & Maes, P. (2021). Exploration of the Ixodes ricinus virosphere unveils an extensive virus diversity including novel coltiviruses and other reoviruses. Virus Evolution , 7 (2). https://doi.org/10.1093/ve/veab066 Wang, J., Wang, J., Kuang, G., Wu, W., Yang, L., Yang, W., Pan, H., Han, X., Yang, T., Shi, M., & Feng, Y. (2023). Meta-transcriptomics for the diversity of tick-borne virus in Nujiang, Yunnan Province. Frontiers in Cellular and Infection Microbiology , 13 . https://doi.org/10.3389/fcimb.2023.1283019 Wang, R., Liu, S., Sun, H., Xu, C., Wen, Y., Wu, X., Zhang, W., Nie, K., Li, F., Fu, S., Yin, Q., He, Y., Xu, S., Liang, G., Deng, L., Wei, Q., & Wang, H. (2024). Metatranscriptomics Reveals the RNA Virome of Ixodes Persulcatus in the China–North Korea Border, 2017. Viruses , 16 (1). https://doi.org/10.3390/v16010062 Wang, S. S., Liu, J. Y., Wang, B. Y., Wang, W. J., Cui, X. M., Jiang, J. F., Sun, Y., Guo, W. Bin, Pan, Y. S., Zhou, Y. H., Lin, Z. T., Jiang, B. G., Zhao, L., & Cao, W. C. (2023). Geographical distribution of Ixodes persulcatus and associated pathogens: Analysis of integrated data from a China field survey and global published data. One Health , 16 . https://doi.org/10.1016/j.onehlt.2023.100508 Wang, S., Zhao, T., Yu, X., Lin, Z., Hua, X., & Cui, L. (2020). Characterization of tick viromes collected from dogs in China. Biosafety and Health , 2 (2). https://doi.org/10.1016/j.bsheal.2020.03.002 Wang, Y. C., Wei, Z., Lv, X., Han, S., Wang, Z., Fan, C., Zhang, X., Shao, J., Zhao, Y. H., Sui, L., Chen, C., Liao, M., Wang, B., Jin, N., Li, C., Ma, J., Hou, Z. J., Yang, Z., Han, Z., … Liu, Q. (2021). A new nairo-like virus associated with human febrile illness in China. Emerging Microbes and Infections , 10 (1). https://doi.org/10.1080/22221751.2021.1936197 Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software , 4 (43). https://doi.org/10.21105/joss.01686 Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biology , 20 (1). https://doi.org/10.1186/s13059-019-1891-0 Zając, Z., Kulisz, J., Bartosik, K., Woźniak, A., Dzierżak, M., & Khan, A. (2021). Environmental determinants of the occurrence and activity of Ixodes ricinus ticks and the prevalence of tick-borne diseases in eastern Poland. Scientific Reports , 11 (1). https://doi.org/10.1038/s41598-021-95079-3 Zakham, F., Albalawi, A. E., Alanazi, A. D., Nguyen, P. T., Alouffi, A. S., Alaoui, A., Sironen, T., Smura, T., & Vapalahti, O. (2021). Viral RNA metagenomics of Hyalomma ticks collected from dromedary camels in Makkah province, Saudi Arabia. Viruses , 13 (7). https://doi.org/10.3390/v13071396 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTBVFile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9279202","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618478603,"identity":"e21b54ff-2133-4281-b116-787102b390a3","order_by":0,"name":"Theophilus Yaw Alale","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYDACCSBmbJCQgXItGPgYmA+AWPxsOHTwQLXwwE1gY2BLYABqkmzDr4UBWQuPAVhLAw4t9tLNBx8X7rDgMTh/gE2CoUZCno2955v0xzYGCT5ctsgcSzaeeUaCx+BGAlDLMQnDNp6z2yQOArXg9kuOmTRvG0gLA1ALmwRjm0QuWEsdbi353yBawA77J2HfJv/mGSFb2CBaDiSArUgEstnwa7mRZmw8E6hM8kZis0Vin0RyG0+ascWZcxI4tbDPSH74uLCtTo7v/OGDNz58s7HtZz/88EZFmY2EfAMOPUDADKEYWyQSEIISuNUjtDAwf8CrbBSMglEwCkYsAAC910qJ653juQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Turku","correspondingAuthor":true,"prefix":"","firstName":"Theophilus","middleName":"Yaw","lastName":"Alale","suffix":""},{"id":618478604,"identity":"c363c504-018e-4fcd-a94a-9929b161a9e6","order_by":1,"name":"Jesse Mänttäri","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Jesse","middleName":"","lastName":"Mänttäri","suffix":""},{"id":618478605,"identity":"a183b41d-2f63-4bc6-afb1-414784d3f8a6","order_by":2,"name":"Heidi M. Viitaniemi","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Heidi","middleName":"M.","lastName":"Viitaniemi","suffix":""},{"id":618478607,"identity":"2e30fb51-626a-4bcc-b5c4-2a3e175f2e6c","order_by":3,"name":"Mikhail Ozerov","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Mikhail","middleName":"","lastName":"Ozerov","suffix":""},{"id":618478619,"identity":"6623a98b-ca4c-4819-a109-eab330d10e90","order_by":4,"name":"Miguel Baltazar-Soares","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Baltazar-Soares","suffix":""},{"id":618478621,"identity":"0df5dc39-79ca-409b-9443-cff0ced12eab","order_by":5,"name":"Jani J. Sormunen","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Jani","middleName":"J.","lastName":"Sormunen","suffix":""},{"id":618478625,"identity":"4aed8709-3bd5-4dc2-9ed6-aa5342697130","order_by":6,"name":"Eero J. Vesterinen","email":"","orcid":"","institution":"University of Turku","correspondingAuthor":false,"prefix":"","firstName":"Eero","middleName":"J.","lastName":"Vesterinen","suffix":""}],"badges":[],"createdAt":"2026-03-31 11:39:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9279202/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9279202/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106552063,"identity":"8a746f7c-8ba4-4aad-b22c-0e8fb78cb1ad","added_by":"auto","created_at":"2026-04-09 18:36:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164872,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eViral community composition across all tick samples.\u003c/strong\u003e Stacked barplot of the relative abundance (%) of the top 10 viral taxa per sample (samples grouped by species). Each bar represents an individual tick sample, ordered by total viral abundance, while colours correspond to distinct viral taxa.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9279202/v1/57d88c57362a79e71911b262.png"},{"id":106725097,"identity":"d9956d42-5fe5-44da-ae13-71c5b96637bd","added_by":"auto","created_at":"2026-04-12 18:31:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence and relative abundance of dominant viral taxa.\u003c/strong\u003eBubble plot showing prevalence (percentage of ticks infected) and mean relative abundance of viral taxa detected across samples. Bubble size corresponds to prevalence and colour correspond to virus family. Nine viruses were only detected in less than 20% of ticks, while Beiji nairovirus was present in almost all (96%) individuals.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9279202/v1/efb67ba576ec920720c665ed.png"},{"id":106552066,"identity":"29f63e39-db13-4186-a247-836ceba55cb0","added_by":"auto","created_at":"2026-04-09 18:36:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":239494,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of differentially abundant tick-associated RNA viruses between species.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe heatmap displays the 20 viruses identified as significantly differentially abundant by DESeq2 analysis of normalized viruses read counts. Rows represent viruses and columns represent individual tick samples. Colour intensity corresponds to log2-transformed normalized abundance values (scaled as indicated in the legend). Differential abundance was determined using the Wald test with Benjamini-Hochberg false discovery rate (FDR) correction (adjusted p \u0026lt; 0.05). Patterns illustrate clear species-associated clustering of viral abundance profiles.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9279202/v1/21756a09098cdbdf71523707.png"},{"id":106725235,"identity":"2dec07e8-7361-4d01-994f-ed532955e243","added_by":"auto","created_at":"2026-04-12 18:31:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":99948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlpha diversity and community structure of tick-associated RNA viromes\u003c/strong\u003e. (A\u0026amp;B) Total number of ticks by species based on viral read count and viral richness (number of detected viral taxa per tick). (C) Shannon alpha diversity of viral communities in \u003cem\u003eIxodes persulcatus\u003c/em\u003e and \u003cem\u003eIxodes ricinus\u003c/em\u003e, stratified by sex. Boxes represent interquartile ranges, center lines indicate medians, and whiskers denote range. (D) Non-metric multidimensional scaling (NMDS) ordination of Bray-Curtis dissimilarities among viral communities from individual ticks (stress = 0.183). Points represent individual ticks, coloured by species and shaped by sex. Ellipses indicate 95% confidence intervals for species centroids.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9279202/v1/abedf5fc888a770978e7307f.png"},{"id":106994009,"identity":"4fac398e-d52d-4f72-aaa1-632b84f5781c","added_by":"auto","created_at":"2026-04-15 15:02:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":154188,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaximum-likelihood phylogeny of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eNairoviridae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eRdRp sequences.\u003c/strong\u003e\u003cbr\u003e\nMaximum-likelihood tree inferred from RdRp amino acid sequences using IQ-TREE under the best-fit model (Q.yeast+F+R6). Finnish sequences (red) cluster within tick-associated orthonairovirus clades and show high amino acid identity to Beiji orthonairovirus and Gakugsa tick virus. Branch support values are shown at key nodes.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9279202/v1/5b2fa832a448fbda5669f492.png"},{"id":106552069,"identity":"b76dcdb8-2b59-4187-ae6e-6eb454c15495","added_by":"auto","created_at":"2026-04-09 18:36:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":202712,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaximum-likelihood phylogeny of Phenuiviridae RdRp sequences.\u003c/strong\u003e\u003cbr\u003e\nPhylogenetic reconstruction of phlebovirus-related RdRp sequences using IQ-TREE (model Q.yeast+F+R7). Finnish sequences (red) group with Onega tick phlebovirus and Sara tick phlebovirus lineages with high amino acid identity.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9279202/v1/9c1832f38298c44c0bf46261.png"},{"id":106552070,"identity":"2543a78b-539b-4c5b-a036-696f313e0fca","added_by":"auto","created_at":"2026-04-09 18:36:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":207488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaximum-likelihood phylogeny of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePartitiviridae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-like RdRp sequences.\u003c/strong\u003e\u003cbr\u003e\nMaximum-likelihood tree of partiti-like RdRp sequences inferred under model LG+I+G4. Finnish sequences (red) cluster with Jilin and Norway partiti-like viruses in a tick-associated clade distinct from plant and fungal partitiviruses. Branch structure supports a host-associated radiation of tick-linked partiti-like viruses.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9279202/v1/33f3beaf851038e75276a2d8.png"},{"id":106725286,"identity":"b8cd4f7f-09f2-4e64-b1d8-d54b2f38dfff","added_by":"auto","created_at":"2026-04-12 18:32:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":223801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaximum-likelihood phylogeny of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eChuviridae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-related RdRp sequences.\u003c/strong\u003e\u003cbr\u003e\nPhylogenetic tree inferred using model LG+F+R6 showing placement of the Finnish chuvirus sequence within a tick-associated Chuviridae clade. The Finnish sequence clusters tightly with Nuomin virus and Lesnoe virus (\u0026gt;99% amino acid identity) and is clearly separated from more distant mononegavirales-like viruses.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9279202/v1/6b15848aeb49315020a88e76.png"},{"id":108707643,"identity":"52152e4c-0e51-467f-a41e-b21294fbc368","added_by":"auto","created_at":"2026-05-07 13:41:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1599079,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9279202/v1/70c07bec-2bc6-4a7c-adb1-01d628cc279f.pdf"},{"id":106726860,"identity":"d6cb2828-f409-40da-8d38-902e124e0aba","added_by":"auto","created_at":"2026-04-12 18:37:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":699097,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTBVFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-9279202/v1/2ff257cfd0dd81d88621cef9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Meta-transcriptomic profiling of Ixodes ricinus and Ixodes persulcatus ticks from Finland reveals diverse RNA viromes, community structuring, and emerging viral lineages","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTicks are obligate hematophagous ectoparasites of a wide range of vertebrate hosts, including mammals, birds, and reptiles. They are considered as the second most important vectors of human and animal pathogens worldwide after mosquitoes (Brites-Neto et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). More than 900 tick species have been described, most belonging to the \u003cem\u003eIxodidae\u003c/em\u003e (hard ticks) and \u003cem\u003eArgasidae\u003c/em\u003e (soft ticks) families (Horak et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Many of these species are of significant veterinary and public health importance due to their role in transmitting a diverse range of pathogens, including bacteria (e.g., \u003cem\u003eBorrelia\u003c/em\u003e, \u003cem\u003eAnaplasma\u003c/em\u003e), protozoa (e.g., \u003cem\u003eBabesia\u003c/em\u003e), and numerous viruses (e.g., TBEV).\u003c/p\u003e \u003cp\u003eIn northern Europe, \u003cem\u003eIxodes ricinus\u003c/em\u003e (the castor bean tick) and \u003cem\u003eI. persulcatus\u003c/em\u003e (the taiga tick) are the dominant tick species associated with the transmission of tick-borne pathogens (Laaksonen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lindgren et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). \u003cem\u003eIxodes ricinus\u003c/em\u003e is widespread across Western and Central Europe, extending into parts of northern Europe, including southern and coastal Finland, whereas \u003cem\u003eI. persulcatus\u003c/em\u003e is distributed across northern and eastern Europe and in Asia (J\u0026auml;\u0026auml;skel\u0026auml;inen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These species overlap geographically in parts of Finland, Estonia and Russia, forming hybrid zones that may create opportunities for cross-species tick-borne pathogen exchange and genetic introgression among tick populations (Alale et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Both species exhibit broad host ranges infesting humans, livestock, companion animals, and wildlife(Jongejan \u0026amp; Uilenberg, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Parola \u0026amp; Raoult, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) making them central to the ecology of many vector-borne pathogens. Their overlapping habitats in Finland, combined with changing environmental conditions, have been linked to the increased prevalence and northward spread of tick-borne pathogens (J\u0026auml;\u0026auml;skel\u0026auml;inen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zając et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTick-borne viruses (TBVs) represent a rapidly expanding group of emerging infectious agents, several of which can cause severe diseases in humans and domestic animals. These viruses include members of several RNA virus families, such as Flaviviridae, Nairoviridae, Phenuiviridae, Rhabdoviridae, Reoviridae, and Orthomyxoviridae (Bente et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Charrel et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Shi, Lin, Vasilakis, et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Notable TBVs of medical and veterinary importance in Europe include tick-borne encephalitis virus (TBEV), Powassan virus (POWV), Louping ill virus, Omsk hemorrhagic fever virus (OHFV), and Crimean-Congo hemorrhagic fever virus (CCHFV) (Charrel et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ergunay et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Finland, the most significant TBVs reported to date include TBEV (J\u0026auml;\u0026auml;skel\u0026auml;inen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), POWV, and the recently identified Alongshan virus (ALSV) (Kuivanen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These viruses can cause a spectrum of diseases ranging from mild febrile illness to severe neurological disorders and haemorrhagic fevers, underscoring their public health importance. Given Finland\u0026rsquo;s northern latitude, extensive forest coverage, and seasonal temperature variation, the region provides a unique ecological setting to study the diversity and evolution of TBVs under the influence of climatic and ecological change (Uusitalo et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zając et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe increasing discovery of novel tick-associated viruses across Eurasia underscores the need for continued surveillance of viral diversity in regions undergoing ecological and climatic transition (Shi et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhou et al., 2023). Therefore, understanding the viral composition of \u003cem\u003eI. ricinus\u003c/em\u003e and \u003cem\u003eI. persulcatus\u003c/em\u003e in Finland is particularly important due to the recent expansion in distributions, where climate change is altering tick abundance, host availability, and species overlap (Sormunen et al., 2023; Estrada-Pena and de la Fuente, 2014; Alkishe et al., 2017). Additionally, sympatric zones may promote viral exchange, reshape viral community structure and potentially influence transmission dynamics (Toorians et al., 2024; Cayol et al., 2018). Without such regional baselines, it may be difficult to detect shifts in viral diversity associated with climate change, range expansion, or ecological disturbance.\u003c/p\u003e \u003cp\u003eSingle-tick metatranscriptomics enables a fine-scale understanding of the tick virome, diversity and structure, revealing how viral communities differ between species, sexes, and environments (Martyn et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The application of metatranscriptomics to individual ticks has proven particularly powerful for resolving inter-individual variation in viral carriage, co-infections, and abundance patterns (Pettersson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; R. Wang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Understanding these factors is essential for predicting viral emergence and informing effective surveillance and control measures. Although metatranscriptomic studies of tick-borne viruses have been conducted elsewhere in Europe, they have focused primarily on \u003cem\u003eI. ricinus\u003c/em\u003e (Pettersson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), leaving comparative data for \u003cem\u003eI. persulcatus\u003c/em\u003e limited. Comprehensive virome profiling across distinct ecological zones is therefore critical to assess species-specific viral structure, identify potential zoonotic threats, and clarify evolutionary relationships among tick-associated viruses.\u003c/p\u003e \u003cp\u003eIn this study, we applied a single-tick metatranscriptomic approach to investigate the viral communities of \u003cem\u003eI. ricinus\u003c/em\u003e and \u003cem\u003eI. persulcatus\u003c/em\u003e collected from two geographically distinct Finnish populations in Ruissalo (Turku) and Hervanta (Tampere). Specifically, we aimed to (1) identify and phylogenetically classify tick-associated RNA viruses; (2) comprehensively characterize and compare the RNA viromes of the two tick species; and (3) quantify species-specific differences in alpha and beta diversity, viral abundance, and community structure.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample Collection and RNA Extraction\u003c/h2\u003e \u003cp\u003eAdult ticks of both species were collected by cloth dragging from two geographically distinct populations: \u003cem\u003eI. ricinus\u003c/em\u003e from Ruissalo Island near the city of Turku (60.42211\u0026deg; N, 22.09654\u0026deg; E) and \u003cem\u003eI. persulcatus\u003c/em\u003e from Finland\u0026rsquo;s southernmost known population in Hervanta, Tampere (61.43328\u0026deg; N, 23.83003\u0026deg; E). Collections were done in May-July 2024. Ticks were collected into falcon tubes and kept alive in a chamber at room temperature with \u0026gt;\u0026thinsp;85% relative humidity (RH) to prevent desiccation. A total of 96 individual ticks (44 males and 52 females) were processed for metatranscriptomic analysis. Prior to RNA extraction, each tick was washed sequentially with 70% ethanol and rinsed with nuclease-free water to remove surface contaminants. Individual ticks were then flash-frozen in liquid nitrogen and homogenized in lysis buffer using sterile plastic pestles following the manufacturer\u0026rsquo;s protocol for the Zymo Research Quick-RNA\u0026trade; MiniPrep Kit (catalogue number: D7001). Total RNA was extracted from each tick, eluted in 50 \u0026micro;L of nuclease-free water, and stored at -80\u0026deg;C until library preparation. RNA purity and yield were measured using both NanoDrop spectrophotometer and Qubit 4 Fluorometer (Thermo Fisher Scientific).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRibosomal RNA Depletion\u003c/h3\u003e\n\u003cp\u003eTo enrich for viral and non-ribosomal transcripts, ribosomal RNA was depleted using the NEBNext\u0026reg; rRNA Depletion Kit v2 (Human/Mouse/Rat) (New England Biolabs, #E7400). The depletion protocol was carried out according to the manufacturer\u0026rsquo;s guidelines, effectively removing cytoplasmic and mitochondrial rRNA. The resulting rRNA-depleted RNA was purified using Agencourt RNAClean XP beads (Beckman Coulter) and quantified with a Qubit RNA HS Assay Kit (Thermo Fisher Scientific).\u003c/p\u003e\n\u003ch3\u003eLibrary Preparation and Sequencing\u003c/h3\u003e\n\u003cp\u003eMetatranscriptomic libraries were prepared from rRNA-depleted RNA using the NEBNext\u0026reg; Ultra II Directional RNA Library Prep Kit (New England Biolabs, #E7760L). RNA fragmentation, first- and second-strand cDNA synthesis, adapter ligation, and PCR enrichment were performed according to the manufacturer\u0026rsquo;s recommendations, with PCR cycle numbers optimized to minimize amplification bias. Library size distribution was confirmed on an Agilent 2100 Bioanalyzer, and concentrations were measured using a Qubit dsDNA HS Assay Kit. Fragment size selection was performed using E-Gel SizeSelect\u0026trade; II (Invitrogen) following the manufacturer's recommendations to obtain insert sizes of 300\u0026ndash;450 bp.\u003c/p\u003e \u003cp\u003eLibraries were pooled equimolarly and sequenced on an Illumina NovaSeq 6000 platform at the Finnish Functional Genomics Centre, University of Turku, generating paired-end 150 bp reads. Sequencing depth was optimized to capture low-abundance viral transcripts.\u003c/p\u003e\n\u003ch3\u003eBioinformatics Workflow\u003c/h3\u003e\n\u003cp\u003eAll computational analyses were conducted on the CSC Puhti supercomputing cluster (CSC - IT Center for Science, Finland). Data processing, assembly, annotation, and statistical analyses were performed using a combination of custom bash scripts, R scripts, and standardized bioinformatics workflows.\u003c/p\u003e \u003cp\u003e \u003cem\u003e1. Quality Control and Read Filtering\u003c/em\u003e \u003c/p\u003e \u003cp\u003eRaw reads were quality-checked using FastQC (v0.11.9) (Andrew S., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and trimmed with Trimmomatic (v0.39, Java 11) (Bolger et al., 2014) to remove adapters and low-quality bases (SLIDINGWINDOW:4:20, MINLEN:50). Only paired reads were used. Trimmed read quality was reassessed using MultiQC (Ewels et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003e2. Tick Host Read Removal\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTrimmed reads were mapped to species-specific reference genomes of \u003cem\u003eI. ricinus\u003c/em\u003e (GenBank accession numbers PRJNA270959) (Cramaro et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)d \u003cem\u003epersulcatus\u003c/em\u003e (GenBank accession number PRJNA633311 (Jia et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) using HISAT2 (--rna-strandness FR) (v2.2.1) (Kim et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Concordantly mapped reads were removed, and unmapped paired-end reads were retained for downstream metatranscriptomic analysis. Mapping statistics were extracted from alignment summary files and aggregated across samples.\u003c/p\u003e \u003cp\u003e \u003cem\u003e3. Taxonomic Classification\u003c/em\u003e \u003c/p\u003e \u003cp\u003eUnmapped reads were taxonomically classified using Kraken2 (v2.1.3)(Wood et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) which uses a custom k-mer-based NCBI RefSeq database containing viral, bacterial, protozoan, and archaeal genomes (updated April 2024). Classification confidence was set at 0.1. Read counts were refined using Bracken (v2.8) to estimate accurate species-level abundance. Classification reports were processed using Bracken to estimate taxon-level relative abundances at species and genus levels. Kraken2 outputs were parsed using custom Python scripts to generate per-sample taxonomic tables, which were merged into a unified abundance matrix for downstream statistical analyses.\u003c/p\u003e \u003cp\u003e \u003cem\u003e4. Extraction of Viral Reads\u003c/em\u003e \u003c/p\u003e \u003cp\u003eVirus-assigned reads were extracted following the National Microbiome Data Collaborative (NMDC) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nmdc-edge.org\u003c/span\u003e\u003cspan address=\"https://nmdc-edge.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Eloe-Fadrosh et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kelliher et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) metagenomic workflow framework. Reads identified with Kraken2 classification outputs were extracted using KrakenTools (extract_kraken_reads.py). Those reads assigned to NCBI TaxID 10239 (Viruses) and to major viral families (including \u003cem\u003eNairoviridae, Phenuiviridae\u003c/em\u003e, \u003cem\u003eFlaviviridae\u003c/em\u003e, \u003cem\u003ePartitiviridae\u003c/em\u003e, \u003cem\u003eChuviridae\u003c/em\u003e) were extracted into family-specific paired FASTQ files using automated Bash workflows. This hierarchical extraction strategy improves sensitivity for low-abundance viral taxa and reduces assembly complexity in mixed-community datasets (Shang and Sun, 2021)\u003c/p\u003e \u003cp\u003e \u003cem\u003e5. De Novo Viral Assembly\u003c/em\u003e \u003c/p\u003e \u003cp\u003eExtracted viral reads were assembled using rnaSPAdes (v3.15.5) (Bushmanova et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) under default parameter for stranded data. Assemblies were conducted both at the individual-sample level and, where appropriate, using pooled family-level viral read sets to enhance recovery of fragmented viral genomes. Only individual level sample assembly data were used for downstream analysis. Contigs shorter than 1000 nucleotides were excluded from further analysis to reduce spurious assemblies and non-informative fragments.\u003c/p\u003e \u003cp\u003eAssembled viral contigs were evaluated using CheckV (Nayfach et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and QUAST v5.2.0 (Gurevich et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which provided assembly statistics such as N50, contig length distribution, and genome completeness estimates, contamination, and viral host integration signals. Viral contigs were categorized into quality tiers (complete, high-quality, medium-quality, or low-quality) following NMDC viral genome reporting standards. Only contigs meeting minimum completeness and quality thresholds (complete 100% completeness, high-quality\u0026thinsp;\u0026ge;\u0026thinsp;90% completeness, medium-quality (50\u0026ndash;90% completeness, low-quality\u0026thinsp;\u0026lt;\u0026thinsp;50% completeness) were retained for taxonomic classification and phylogenetic inference (Li et al., 2024).\u003c/p\u003e \u003cp\u003e \u003cem\u003e6. Viral Gene Prediction and Functional Annotation\u003c/em\u003e \u003c/p\u003e \u003cp\u003eGenome annotation and functional characterization of viral contigs were performed using the standardized NMDC metagenomic annotation pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nmdc-edge.org/home\u003c/span\u003e\u003cspan address=\"https://nmdc-edge.org/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Eloe-Fadrosh et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kelliher et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Open reading frames (ORFs) were predicted using Prodigal in metagenomic mode. Predicted proteins were annotated using BLASTp (Altschul et al., 1990) searches against the NCBI RefSeq viral protein database, HMMER (Finn et al., 2011) searches against Pfam and viral protein family HMM profiles, and eggNOG-mapper (v2) (Cantalapiedra et al., 2021) for functional classification and ortholog assignment.\u003c/p\u003e \u003cp\u003eTaxonomic assignment of viral contigs was refined using combined evidence from BLAST using a minimum of 70% identity and less than 1 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e similarity threshold, conserved domain architecture, and phylogenetic placement. Viral family classification followed the International Committee on Taxonomy of Viruses (ICTV) (Lefkowitz et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) consistent taxonomy used within the NMDC framework.\u003c/p\u003e \u003cp\u003e \u003cem\u003e7. Taxonomic Confirmation and Novel Virus Assessment\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTaxonomic assignments were confirmed using reciprocal best-hit BLAST strategies and phylogenetic placement of conserved proteins, particularly RNA-dependent RNA polymerase (RdRp). Viral sequences showing\u0026thinsp;\u0026lt;\u0026thinsp;75% amino acid identity to known references and forming monophyletic clades distinct from recognized species were classified as divergent or putative novel viral lineages, consistent with ICTV sequence demarcation guidelines and NMDC viral genome reporting criteria. Final viral classifications were cross validated against GenBank and recent tick virome datasets.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses and Visualization\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses and plotting were conducted in R (v4.3.2) (R Core Team, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) using a combination of base functions and Bioconductor packages. Viral abundance matrices generated from the Kraken2 and Bracken outputs were imported into R and normalized prior to analysis. Data manipulation and summarization were performed using the tidyverse package (Wickham et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To assess within-sample diversity, alpha diversity indices including Shannon, Simpson, and species richness were calculated using the vegan R package (v2.8-0) (Oksanen et al., 2025). These indices provided insights into viral community complexity and evenness within each tick sample.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eViral Community and Diversity Analyses\u003c/h2\u003e \u003cp\u003eViral abundance tables derived from Kraken2 output and viral families based on taxonomic classification were merged into a unified abundance matrix. Alpha-diversity metrics (Shannon and Simpson indices) were calculated using the vegan R package (Oksanen et al., 2025). Beta-diversity was assessed using Bray-Curtis dissimilarity, and differences in community composition were tested using PERMANOVA with 999 permutations (Anderson \u0026amp; Walsh, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Non-metric multidimensional scaling (NMDS) ordination was used to visualize inter-sample relationships. Heatmaps of viral relative abundance were generated using hierarchical clustering based on Bray-Curtis distances.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifferential Abundance Analysis\u003c/h3\u003e\n\u003cp\u003eDifferential viral abundance between tick species was assessed using DESeq2 (v1.42.0) (Love et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) on raw count matrices derived from classified viral reads. Counts with at least 1000 viral reads were normalized using the median-of-ratios method. Viruses with adjusted p-values (Benjamini-Hochberg) below 0.05 were considered significantly differentially abundant. Log2 fold changes were used to quantify the magnitude and direction of species-associated differences in viral abundance.\u003c/p\u003e\n\u003ch3\u003ePhylogenetic Analysis\u003c/h3\u003e\n\u003cp\u003eTo infer the evolutionary relationships of viral sequences detected in tick samples, phylogenetic analyses were conducted using RNA-dependent RNA polymerase (RdRp) amino acid sequences, which represent the most conserved genomic region across RNA viruses and are widely used for virus classification. Viral contigs containing putative RdRp domains were identified from de novo assemblies based on DIAMOND BLASTp similarity searches against the NCBI non-redundant protein database with curated viral reference datasets. Corresponding reference sequences representing major viral clades and closely related taxa were retrieved from GenBank to provide phylogenetic context.\u003c/p\u003e \u003cp\u003eProtein sequences were aligned using MAFFT v7.520 (Katoh \u0026amp; Standley, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) with the L-INS-i algorithm, which is optimized for accuracy in datasets with variable sequence length and moderate divergence. Alignments were manually inspected and curated using AliView v1.28 (Larsson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) to verify reading frame consistency, identify poorly aligned regions, and confirm that conserved RdRp motifs were properly aligned. To reduce phylogenetic noise arising from ambiguously aligned or highly variable regions, alignments were trimmed using ClipKIT v1.3.0 (Steenwyk et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with the \u0026ldquo;smart-gap\u0026rdquo; strategy, which removes alignment columns with excessive gaps while retaining phylogenetically informative sites.\u003c/p\u003e \u003cp\u003eMaximum likelihood phylogenetic trees were reconstructed using IQ-TREE v2.2.2.6 (Nguyen et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The best-fitting amino acid substitution model for each alignment was selected automatically using ModelFinder implemented in IQ-TREE based on Bayesian Information Criterion (BIC). Branch support was assessed using 1,000 ultrafast bootstrap replicates combined with SH-like approximate likelihood ratio tests (SH-aLRT), providing robust measures of node confidence. Nodes with ultrafast bootstrap support\u0026thinsp;\u0026ge;\u0026thinsp;95% and SH-aLRT support\u0026thinsp;\u0026ge;\u0026thinsp;80% were considered strongly supported. For the Chuviridae dataset, the best-fit model was LG\u0026thinsp;+\u0026thinsp;F+R6, with a RapidNJ starting tree log-likelihood of -127,568.089 and a final optimized likelihood score of -127,382.018. For the Nairoviridae dataset, the optimal model was Q.yeast\u0026thinsp;+\u0026thinsp;F+R6, with a RapidNJ log-likelihood of -204,087.415 and a final likelihood of -203,064.648. For Partitiviridae, ModelFinder selected LG\u0026thinsp;+\u0026thinsp;I+G4, yielding a RapidNJ log-likelihood of -19,180.027 and a final likelihood of -19,112.164. For Phenuiviridae, the best-fit model was Q.yeast\u0026thinsp;+\u0026thinsp;F+R7, with a RapidNJ log-likelihood of -181,883.814 and a final optimized likelihood of -181,478.618.\u003c/p\u003e \u003cp\u003eTrees were inferred as either unrooted or rooted depending on the availability of appropriate outgroup sequences. When suitable distant homologs were available, trees were rooted using outgroup taxa belonging to related viral families; otherwise, midpoint rooting was applied. Tree topologies were visualized and annotated using Interactive Tree Of Life (iTOL) v6 (Letunic \u0026amp; Bork, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), allowing integration of metadata such as tick species, geographic origin, and viral family assignments. Branches corresponding to Finnish sequences were highlighted to facilitate comparison with previously described viral lineages.\u003c/p\u003e \u003cp\u003ePhylogenetic placement of Finnish viral sequences was used to assess taxonomic relationships, identify clustering with known tick-associated virus clades, and evaluate genetic divergence relative to established species. Sequences that formed well-supported monophyletic groups distinct from described taxa were considered putative novel or divergent viral lineages, pending formal taxonomic classification following ICTV guidelines.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRead Processing and Quality Control\u003c/h2\u003e \u003cp\u003eMetatranscriptomic sequencing was completed for 92 individual adult ticks, comprising 46 \u003cem\u003eI. ricinus\u003c/em\u003e (23 females, 23 males) and 46 \u003cem\u003eI. persulcatus\u003c/em\u003e (23 females, 23 males). Libraries passed all platform quality metrics and no samples were excluded. Mean Phred quality scores exceeded 34 across all libraries, indicating high base-calling accuracy. After adapter removal and quality trimming, reads were filtered using stringent Phred score thresholds and processed through the NMDC metatranscriptomic workflow.\u003c/p\u003e \u003cp\u003eTick read removal using reference-guided mapping resulted in mean tick-aligned fractions of 89.4% \u0026plusmn; 3.2% for \u003cem\u003eI. ricinus\u003c/em\u003e and 91.1% \u0026plusmn; 2.6% for \u003cem\u003eI. persulcatus\u003c/em\u003e (Supplementary Figs. S1-S2). The final retained non-tick reads that did not align to tick reference genome averaged 3.2 \u0026times; 10⁶ \u0026plusmn; 0.4 \u0026times; 10⁶ for \u003cem\u003eI. ricinus\u003c/em\u003e samples and 1.01 \u0026times; 10⁶ \u0026plusmn; 0.2 \u0026times; 10⁶ for \u003cem\u003eI. persulcatus\u003c/em\u003e samples, were used for downstream analysis. The average microbial component of total non-tick read after quality control for \u003cem\u003eI. ricinus\u003c/em\u003e for example was 1.37 x 10\u003csup\u003e6\u003c/sup\u003e \u0026plusmn; 0.1 \u0026times; 10⁶. The summary composition report of non-tick reads is shown in Supplementary Fig. S3\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOverview of Viral Diversity Across Sampled Ixodes Ticks\u003c/h2\u003e \u003cp\u003eAcross all samples, 49 distinct RNA viruses spanning 12 viral families were identified (Supplementary, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Stacked relative abundance profiles showed consistent but species-dependent viral community structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and mean family and species-level abundance summaries further highlighted dominant taxa (Supplementary Fig.S4). Each sample had at least one virus (Supplementary Fig. S5). The most represented families were Nairoviridae (11 taxa), Phenuiviridae (9), Partitiviridae (7), Flaviviridae (4), and Orthomyxoviridae (3). Individual ticks harboured an average of 10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 viral taxa, indicating substantial viral diversity at the single-tick level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A small subset of viruses accounted for a large fraction of total viral reads, whereas many taxa were present at low abundance and low prevalence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence and Abundance Structure of Dominant Viruses\u003c/h2\u003e \u003cp\u003ePrevalence-abundance analysis revealed a skewed distribution dominated by a small number of highly prevalent viruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nine taxa were detected in more than 20% of ticks. Beiji nairovirus was nearly ubiquitous (96% prevalence), followed by Jilin partiti-like virus 1 (89%), Sara tick phlebovirus (85%), and Gakugsa tick virus (82%). Five taxa accounted for the largest share of total viral reads across the dataset: Beiji nairovirus (26.5%), Sara tick phlebovirus (18.7%), Onega tick phlebovirus (12.4%), Gakugsa tick virus (11.6%), and Jilin partiti-like virus 1 (15.3%) (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLow-prevalence viruses, including Hepacivirus hominis, Kizhi virus, and SARS-related coronavirus-like fragments (SARSC), were detected in at least 10% of ticks, but at very low coverage, typically represented by short contigs and sparse read support (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These detections were retained only when supported by protein-domain matches and phylogenetic placement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSpecies-Specific Viral Signatures and Differential Abundance\u003c/h2\u003e \u003cp\u003eHierarchical clustering demonstrated strong species-level segregation of viral abundance profiles (Supplementary Fig. S5). Samples clustered primarily by tick species rather than sex. \u003cem\u003eIxodes persulcatus\u003c/em\u003e viromes were enriched in Nairoviridae (34.8% \u0026plusmn; 9.7) and Phenuiviridae (22.5% \u0026plusmn; 7.6), whereas \u003cem\u003eI. ricinus\u003c/em\u003e viromes were enriched in Partitiviridae (21.4% \u0026plusmn; 5.9) and Iflaviridae (18.6% \u0026plusmn; 4.2). The \u003cem\u003eI. persulcatus\u003c/em\u003e virome was dominated by Beiji nairovirus, Sara tick phlebovirus, and Onega tick phlebovirus, together accounting for 57.6% of viral reads. In contrast, \u003cem\u003eI. ricinus\u003c/em\u003e showed a more even distribution across several taxa. The caveat in our analysis was that one tick species was collected in one location, and another tick species in another location. Thus, the comparisons between tick species are also comparisons between locations.\u003c/p\u003e \u003cp\u003eDifferential abundance testing using DESeq2 identified 15 viruses with higher interspecies differences after multiple-testing correction (Benjamini-Hochberg adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplementary, Table S2). Viruses enriched in \u003cem\u003eI. persulcatus\u003c/em\u003e included Kizhi virus, Jilin partiti-like virus 1, Gakugsa tick virus, Onega tick phlebovirus, and Sara tick phlebovirus. Viruses enriched in \u003cem\u003eI. ricinus\u003c/em\u003e included Norwavirus grotenhoutense, Bronnoya virus, Leuven phlebovirus, Betaricinrhavirus chimay, and Norway partiti-like virus 1. Beiji nairovirus and Pustyn virus showed no significant species bias, suggesting broader tick associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eViral Load, Richness, Alpha Diversity, and Community Structure\u003c/h2\u003e \u003cp\u003eTotal viral read abundance and viral richness varied across tick species and sex categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA \u0026amp; B). Boxplot comparisons indicated broader dispersion of viral signal and richness values in \u003cem\u003eI. ricinus\u003c/em\u003e compared with \u003cem\u003eI. persulcatus\u003c/em\u003e, consistent with greater inter-individual variability in viral composition observed in this species. Although both species harbored diverse viral assemblages, species-level differences were more pronounced than sex-level differences (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlpha diversity analysis based on the Shannon index showed significantly higher viral diversity in \u003cem\u003eI. ricinus\u003c/em\u003e (H\u0026thinsp;=\u0026thinsp;2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52) than in \u003cem\u003eI. persulcatus\u003c/em\u003e (H\u0026thinsp;=\u0026thinsp;2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41; Welch\u0026rsquo;s t\u0026thinsp;=\u0026thinsp;2.41, p\u0026thinsp;=\u0026thinsp;0.018) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In contrast, Simpson\u0026rsquo;s diversity index did not differ significantly between species (p\u0026thinsp;=\u0026thinsp;0.145), indicating that the observed difference was driven primarily by richness and evenness rather than dominance by a single viral taxon. Together, these metrics support a more even and taxonomically diverse virome in \u003cem\u003eI. ricinus\u003c/em\u003e. Sex-associated effects on alpha diversity were limited and species-dependent. Female \u003cem\u003eI. persulcatus\u003c/em\u003e harboured a higher number of viral taxa than males (12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8 vs. 9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2; p\u0026thinsp;=\u0026thinsp;0.046), whereas no significant sex-based difference was detected in \u003cem\u003eI. ricinus\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.273) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Across all diversity metrics, sex explained substantially less variation than species identity (Supplement, Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eCommunity-level analyses demonstrated that tick species identity was the primary determinant of viral composition (PERMANOVA p\u0026thinsp;=\u0026thinsp;0.001). The differences were further supported by non-metric multidimensional scaling (NMDS) ordination of Bray-Curtis dissimilarities (stress\u0026thinsp;=\u0026thinsp;0.183), which demonstrated species-associated clustering of viral communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Samples from \u003cem\u003eI. ricinus\u003c/em\u003e and \u003cem\u003eI. persulcatus\u003c/em\u003e formed partially separated clusters with limited overlap, whereas male and female ticks overlapped extensively within species clusters. This pattern indicates that tick species identity, rather than sex, is the dominant determinant of viral community composition. The ordination results are consistent with PERMANOVA testing showing a significant species effect and weaker or non-significant sex effects on overall virome structure (Supplementary Table\u0026nbsp;3A-C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic Placement Confirms Arthropod-Associated Lineages\u003c/h2\u003e \u003cp\u003eMaximum-likelihood phylogenetic reconstruction of RdRp amino acid sequences placed all Finnish viral sequences within established arthropod-associated viral clades (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Tree topologies were stable across model selection and bootstrap resampling, and alignments were manually inspected to remove poorly aligned regions prior to inference. Importantly, none of the Finnish sequences clustered within well-characterized vertebrate-adapted virus clades, supporting their classification as components of the tick-associated virome rather than misassigned vertebrate pathogens.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinnish nairovirus sequences grouped within tick-associated orthonairovirus clades (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Fin_nairovirus_Rdrp showed 98.01% amino acid identity with Beiji orthonairovirus, 97.87% with Gakugsa tick virus, and 87.95% with Yichun nairovirus, supporting assignment to a Eurasian tick-associated nairovirus lineage.\u003c/p\u003e \u003cp\u003eTwo distinct phlebovirus lineages were resolved (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Fin_Phlebovirus_Rdrp-A was nearly identical to Onega tick phlebovirus (99.63% identity, accession number: XCO66288.1), whereas Fin_Phlebovirus-B clustered with Sara tick phlebovirus (99.47% identity) and showed lower similarity to Fairhair virus (89.29%) and Shoal Cavern virus (78.20%). Partiti-like viruses formed a strongly supported tick-associated clade distinct from plant and fungal partitiviruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The Finnish partiti-like sequence showed 100% identity to Jilin partiti-like virus 1 and 94.42% identity to Norway partiti-like virus 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA Finnish chuvirus-related sequence clustered within tick-associated Chuviridae lineages (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The RdRp segment showed 99.36% and 99.34% identity to Nuomin virus (accession number: UKS70436.1) and Lesnoe virus (accession number: WAS28088.1), respectively, and substantially lower identity (77.24%) to Deer tick mononegavirales-like virus (accession number: AIE42676.2), supporting placement within a Nuomin-like lineage.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a high-resolution, single-tick metatranscriptomic comparison of RNA viromes associated with \u003cem\u003eIxodes ricinus\u003c/em\u003e and \u003cem\u003eIxodes persulcatus\u003c/em\u003e in Finland and establishes a detailed baseline for tick-associated viral diversity in the boreal region. Across 92 individually sequenced adult ticks, we identified 49 RNA viruses spanning 12 viral families, including multiple divergent lineages supported by conserved domain architecture, read remapping, and model-tested phylogenetic placement (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The consistent detection of diverse RNA viruses across both tick species reinforces the view that \u003cem\u003eIxodes\u003c/em\u003e ticks maintain high levels of RNA virus diversity in northern ecosystems, in agreement with tick-borne viral surveys from Europe and Asia (Bratuleanu et al., 2023; Pettersson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis finding is consistent with previous evidence that tick species identity is a dominant determinant of viral community assembly (Meng et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pettersson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), whereas tick sex had only minor independent effects with a weak interaction component (Supplement, Table\u0026nbsp;1A-C; Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We found a strong, statistically supported divergence in virome composition between \u003cem\u003eI. ricinus\u003c/em\u003e and \u003cem\u003eI. persulcatus\u003c/em\u003e sampled at different geographical locations. Community-level analyses demonstrated that tick species identity was the primary determinant of viral composition. NMDS ordination showed species-associated clustering with partial but not complete separation and acceptable ordination stress, indicating robust but not absolute community differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). \u003cem\u003eIxodes persulcatus\u003c/em\u003e displayed lower overall viral diversity but stronger clustering within \u003cem\u003eβ\u003c/em\u003e-diversity analyses, suggesting specialized viral associations or narrower ecological interactions. Similar species-driven structuring has been observed in Russian and Chinese tick populations, where \u003cem\u003eI. persulcatus\u003c/em\u003e harbours a homogeneous virome dominated by bunyaviruses and phleboviruses (Kholodilov et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; R. Wang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003eIxodes persulcatus\u003c/em\u003e samples formed a more compact cluster, whereas \u003cem\u003eI. ricinus\u003c/em\u003e samples were more widely dispersed, consistent with greater inter-individual virome variability. Alpha diversity metrics further supported this contrast: \u003cem\u003eI. ricinus\u003c/em\u003e exhibited significantly higher Shannon diversity especially in males, while Simpson indices did not differ significantly, indicating that between-species differences were driven primarily by richness and evenness rather than dominance by single taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA \u0026amp; B). These patterns remained stable after normalization, arguing against sequencing depth as a confounder.\u003c/p\u003e \u003cp\u003eThe taxonomic structure of the viromes differed markedly between species. \u003cem\u003eIxodes persulcatus\u003c/em\u003e was strongly dominated by Nairoviridae and Phenuiviridae, whereas \u003cem\u003eI. ricinus\u003c/em\u003e showed higher relative representation of Partitiviridae and Iflaviridae (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A small number of viruses dominated total abundance and prevalence, particularly Beiji nairovirus, Sara tick phlebovirus, Onega tick phlebovirus, Gakugsa tick virus, and Jilin partiti-like virus 1. Several of these viruses showed high prevalence across individuals, with Beiji nairovirus detected in nearly all ticks, while others exhibited significant species enrichment patterns in differential abundance testing. The strong enrichment of Nairoviridae and Phenuiviridae members in \u003cem\u003eI. persulcatus\u003c/em\u003e underscores its zoonotic importance across Eurasia. The frequent detection of Beiji nairovirus and Sara tick phlebovirus previously described in northeastern China, Japan and Siberia (Kholodilov et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kishimoto et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tokarz et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) may suggests a wider distribution facilitated by migratory vertebrate hosts and or overlapping vector populations. These viruses are phylogenetically related to high-consequence human pathogens such as Crimean-Congo hemorrhagic fever virus (CCHFV) and severe fever with thrombocytopenia syndrome virus (SFTSV), which are maintained by ticks of a different genus (Ma et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shimada et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Their detection in Finnish \u003cem\u003eI. persulcatus\u003c/em\u003e populations suggests either ancient viral persistence in northern ecosystems or recent introductions linked to expanding tick distributions.\u003c/p\u003e \u003cp\u003eAt the same time, some highly prevalent viruses did not show strong species bias, suggesting broader tick compatibility within sampled tick populations. Conversely, the more taxonomically diverse but lower-abundance virome of \u003cem\u003eI. ricinus\u003c/em\u003e dominated by Jilin partiti-like virus 1 and Gakugsa tick virus could represent long-term tick-virus coadaptation without known vertebrate pathogenicity (Gould et al., 2003).\u003c/p\u003e \u003cp\u003ePhylogenomic reconstruction based on RdRp amino acid sequences confirmed that Finnish viral sequences fall within established arthropod-associated clades across Nairoviridae, Phenuiviridae, Partitiviridae, Rhabdoviridae (Suppplementary material Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and Chuviridae and do not cluster with recognized vertebrate-common virus lineages (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These findings reflect the ongoing evolution of tick-associated RNA viruses, driven by frequent reassortment, viral host shifts, and environmental pressures (Shi et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Notably, Kizhi virus, Gakugsa tick virus, and Onega tick phlebovirus are reported here for the first time in Finland, extending their known ranges from Russia, Eastern Europe and East Asia. These findings mirror recent discoveries of related viruses across Eurasia, supporting a model of transcontinental viral exchange mediated by migratory birds, large mammals, and climate-driven vector range expansion (Bratuleanu et al., 2023; Kholodilov et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tokarz et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)..\u003c/p\u003e \u003cp\u003eFrom a zoonotic risk perspective, several detected viruses warrant careful but conservative interpretation. Beiji nairovirus and Yichun nairovirus were common and phylogenetically nested within tick-associated nairovirus diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Although these sequences cluster within arthropod-associated branches rather than known high-pathogenicity clades, nairoviruses as a broader group include important human and livestock pathogens with reported incidence of human febrile illness in China (Wang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Their high prevalence increases opportunities for evolutionary diversification, even if current evidence does not indicate pathogenicity. The Nuomin-like chuvirus similarly represents a lineage with documented vertebrate exposure but unresolved clinical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These findings highlight evolutionary proximity and surveillance relevance rather than demonstrated public health threat.\u003c/p\u003e \u003cp\u003eOther vertebrate-associated virus signals were detected only at very low abundance and with fragmented genome coverage. \u003cem\u003eHepacivirus hominis\u003c/em\u003e-like reads most plausibly reflect residual viral RNA from vertebrate blood meals rather than productive tick infection. Likewise, sporadic reads assigned to SARS-related coronavirus-like sequences and \u003cem\u003eAlphainfluenzavirus influenzae\u003c/em\u003e lacked genome-wide coverage, replication signatures, and stable phylogenetic placement. While there was no evidence of active replication, their presence underscores the remarkable sensitivity of metatranscriptomic methods for detecting low-level or transient viral fragments. Similar incidental findings of coronavirus-like sequences have been reported in global arthropod virome surveys (Nekoei et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These are generally attributed to environmental contamination or ingestion of tick host blood containing vertebrate viral RNA rather than true tick infection. Although this result does not suggest that \u003cem\u003eIxodes\u003c/em\u003e ticks serve as coronavirus vectors, it highlights the necessity of stringent data interpretation in metavirome studies and the potential of such surveillance to detect unexpected viral lineages at the human-wildlife interface.\u003c/p\u003e \u003cp\u003eEcological differences between \u003cem\u003eI. ricinus\u003c/em\u003e and \u003cem\u003eI. persulcatus\u003c/em\u003e likely contribute to the observed virome divergence. \u003cem\u003eIxodes persulcatus\u003c/em\u003e occupies more colder continental habitats and is associated with relatively stable wildlife host communities (Bugmyrin et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cotes-Perdomo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), conditions that may favour persistence of specialized viral assemblages dominated by bunyavirus-related taxa. Similar patterns of specialization have been observed in \u003cem\u003eHaemaphysalis longicornis\u003c/em\u003e and \u003cem\u003eDermacentor silvarum\u003c/em\u003e, where stable viral consortia persist across vast geographic ranges (Jia et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; S. Wang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Meanwhile, \u003cem\u003eI. ricinus\u003c/em\u003e, occupying more heterogeneous habitats and feeding on a wider variety of hosts, maintains a more variable virome reflecting opportunistic viral acquisition (Estrada-Pe\u0026ntilde;a \u0026amp; de la Fuente, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vanmechelen et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite this finding, we note that the between species differences may likely driven by two separate sampling spots and thus ecological differentiation is also hard to decipher from the results (and differences between sexes).\u003c/p\u003e \u003cp\u003eThe higher viral richness in female \u003cem\u003eI. persulcatus\u003c/em\u003e may result from previous blood-feeding events and prolonged exposure to diverse hosts or environments, consistent with observations from other Eurasia tick populations (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pettersson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Reuben Kaufman, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Several viral genomes showed less than 75% nucleotide similarity to available references and formed distinct, well-supported phylogenetic branches (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e). We conservatively describe these as putatively novel or highly divergent tick-associated viruses pending full genome resolution and biological validation. Multi-region coverage, conserved domains, and consistent phylogenetic placement support their authenticity and argue against assembly artefacts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFinnish \u003cem\u003eIxodes\u003c/em\u003e ticks harbor a diverse and strongly species-structured RNA virome composed predominantly of arthropod-associated viral lineages, including several with evolutionary links to zoonotic virus groups. The presence of high-prevalence nairovirus lineages and a Nuomin-like chuvirus, together with ongoing tick range expansion under climate change, reinforces the need for continuous surveillance. Longitudinal integration of viromics, vector ecology, and tick host data will be essential for anticipating shifts in the tick-associated viral risk landscape. Although this study provides a high-resolution snapshot of the RNA viromes of \u003cem\u003eI. ricinus\u003c/em\u003e and \u003cem\u003eI. persulcatus\u003c/em\u003e in Finland, metatranscriptomic detection alone does not demonstrate viral replication, transmissibility, or pathogenic potential. Some low-abundance viral signals may reflect residual blood-meal RNA or environmental contamination rather than active tick infection. In addition, sampling was restricted to two geographic locations and a single life stage, limiting broader ecological inference.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors Conceptualised the idea. T.Y.A., J.M. \u0026amp; H.M.V. processed the samples and carried out all laboratory work. T.Y.A. processed and analysed the data, and wrote the main manuscript, M.O \u0026amp; M.B-S. contributed to data analysis and manuscript revision, J.J.S. \u0026amp; E.J.V. supervised the project, provided funding and reviewed the manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequencing data have been deposited in the NCBI Sequence Read Archive under the BioProject accession number PRJNA1444502: https://www.ncbi.nlm.nih.gov/sra/PRJNA1444502. Analysis scripts and workflow documentation are available at GitHub repository: https://github.com/theoalal/Bioinformatics_23.git.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank members of the university of Turku tick research group for their immense support in helping us get this work done. Many thanks to Dr. Tero Klemola of the department of biology, University of Turku, for providing guidance and giving comments during the preparatory stages of the research. We would also like to thank the Turku University Foundation for providing a research grant to get this work through.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlale, T. Y., Sormunen, J. J., Vesterinen, E. J., Klemola, T., Knott, K. E., \u0026amp; Baltazar-Soares, M. (2024). Genomic signatures of hybridization between Ixodes ricinus and Ixodes persulcatus in natural populations. \u003cem\u003eEcology and Evolution\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(5). https://doi.org/10.1002/ece3.11415\u003c/li\u003e\n\u003cli\u003eAlburkat, H. A. T., Pulkkinen, E., Virtanen, J., Vapalahti, O., Sironen, T., \u0026amp; J\u0026auml;\u0026auml;skel\u0026auml;inen, A. J. (2024). Serological and molecular screening of arenaviruses in suspected tick-borne encephalitis cases in Finland. \u003cem\u003eEpidemiology and Infection\u003c/em\u003e, \u003cem\u003e152\u003c/em\u003e. https://doi.org/10.1017/S0950268824000128\u003c/li\u003e\n\u003cli\u003eAnderson, M. J., \u0026amp; Walsh, D. C. I. (2013). PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? \u003cem\u003eEcological Monographs\u003c/em\u003e, \u003cem\u003e83\u003c/em\u003e(4). https://doi.org/10.1890/12-2010.1\u003c/li\u003e\n\u003cli\u003eAndrew S. (2010). \u003cem\u003eFastQC: A Quality Control Tool for High Throughput Sequence Data\u003c/em\u003e. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/.\u003c/li\u003e\n\u003cli\u003eBente, D. A., Forrester, N. L., Watts, D. M., McAuley, A. J., Whitehouse, C. A., \u0026amp; Bray, M. (2013). Crimean-Congo hemorrhagic fever: History, epidemiology, pathogenesis, clinical syndrome and genetic diversity. In \u003cem\u003eAntiviral Research\u003c/em\u003e (Vol. 100, Number 1). https://doi.org/10.1016/j.antiviral.2013.07.006\u003c/li\u003e\n\u003cli\u003eBrites-Neto, J., Duarte, K. M. R., \u0026amp; Martins, T. F. (2015). Tick-borne infections in human and animal population worldwide. \u003cem\u003eVeterinary World\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(3). https://doi.org/10.14202/vetworld.2015.301-315\u003c/li\u003e\n\u003cli\u003eBugmyrin, S., Hokkanen, T. J., Romanova, L., Bespyatova, L., Fyodorov, F., Burenkova, L., Yakimova, A., \u0026amp; Ieshko, E. (2011). Ixodes persulcatus [Schulze 1930] (Acari: Ixodidae) in eastern Finland. \u003cem\u003eEntomologica Fennica\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(4). https://doi.org/10.33338/ef.84556\u003c/li\u003e\n\u003cli\u003eBushmanova, E., Antipov, D., Lapidus, A., \u0026amp; Prjibelski, A. D. (2019). RnaSPAdes: A de novo transcriptome assembler and its application to RNA-Seq data. \u003cem\u003eGigaScience\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(9). https://doi.org/10.1093/gigascience/giz100\u003c/li\u003e\n\u003cli\u003eCharrel, R. N., Attoui, H., Butenko, A. M., Clegg, J. C., Deubel, V., Frolova, T. V., Gould, E. A., Gritsun, T. S., Heinz, F. X., Labuda, M., Lashkevich, V. A., Loktev, V., Lundkvist, A., Lvov, D. V., Mandl, C. W., Niedrig, M., Papa, A., Petrov, V. S., Plyusnin, A., \u0026hellip; de Lamballerie, X. (2004). Tick-borne virus diseases of human interest in Europe. In \u003cem\u003eClinical Microbiology and Infection\u003c/em\u003e (Vol. 10, Number 12). https://doi.org/10.1111/j.1469-0691.2004.01022.x\u003c/li\u003e\n\u003cli\u003eCotes-Perdomo, A., Soleng, A., Alfsnes, K., \u0026amp; Andreassen, \u0026Aring;. (2025). First report of the taiga tick Ixodes persulcatus in Norway. \u003cem\u003eTicks and Tick-Borne Diseases\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4). https://doi.org/10.1016/j.ttbdis.2025.102508\u003c/li\u003e\n\u003cli\u003eCramaro, W. J., Revets, D., Hunewald, O. E., Sinner, R., Reye, A. L., \u0026amp; Muller, C. P. (2015). Integration of Ixodes ricinus genome sequencing with transcriptome and proteome annotation of the na\u0026iuml;ve midgut. \u003cem\u003eBMC Genomics\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1). https://doi.org/10.1186/s12864-015-1981-7\u003c/li\u003e\n\u003cli\u003eEloe-Fadrosh, E. A., Ahmed, F., Anubhav, Babinski, M., Baumes, J., Borkum, M., Bramer, L., Canon, S., Christianson, D. S., Corilo, Y. E., Davenport, K. W., Davis, B., Drake, M., Duncan, W. D., Flynn, M. C., Hays, D., Hu, B., Huntemann, M., Kelliher, J., \u0026hellip; Fagnan, K. (2022). The National Microbiome Data Collaborative Data Portal: An integrated multi-omics microbiome data resource. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(D1). https://doi.org/10.1093/nar/gkab990\u003c/li\u003e\n\u003cli\u003eErgunay, K., Bourke, B. P., Reinbold-Wasson, D. D., Nikolich, M. P., Nelson, S. P., Caicedo-Quiroga, L., Vaydayko, N., Kirkitadze, G., Chunashvili, T., Long, L. S., Blackburn, J. K., Cleary, N. G., Tucker, C. L., \u0026amp; Linton, Y. M. (2023). The expanding range of emerging tick-borne viruses in Eastern Europe and the Black Sea Region. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1). https://doi.org/10.1038/s41598-023-46879-2\u003c/li\u003e\n\u003cli\u003eEstrada-Pe\u0026ntilde;a, A., \u0026amp; de la Fuente, J. (2017). Host distribution does not limit the range of the tick ixodes ricinus but impacts the circulation of transmitted pathogens. \u003cem\u003eFrontiers in Cellular and Infection Microbiology\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(OCT). https://doi.org/10.3389/fcimb.2017.00405\u003c/li\u003e\n\u003cli\u003eEwels, P., Magnusson, M., Lundin, S., \u0026amp; K\u0026auml;ller, M. (2016). MultiQC: Summarize analysis results for multiple tools and samples in a single report. \u003cem\u003eBioinformatics\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(19), 3047\u0026ndash;3048. https://doi.org/10.1093/bioinformatics/btw354\u003c/li\u003e\n\u003cli\u003eGurevich, A., Saveliev, V., Vyahhi, N., \u0026amp; Tesler, G. (2013). QUAST: Quality assessment tool for genome assemblies. \u003cem\u003eBioinformatics\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(8). https://doi.org/10.1093/bioinformatics/btt086\u003c/li\u003e\n\u003cli\u003eHorak, I. G., Jordaan, A. J., Nel, P. J., van Heerden, J., Heyne, H., \u0026amp; van Dalen, E. M. (2015). Distribution of endemic and introduced tick species in Free State Province, South Africa. \u003cem\u003eJournal of the South African Veterinary Association\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e(1). https://doi.org/10.4102/jsava.v86i1.1255\u003c/li\u003e\n\u003cli\u003eJ\u0026auml;\u0026auml;skel\u0026auml;inen, A. E., Sironen, T., Murueva, G. B., Subbotina, N., Alekseev, A. N., Castr\u0026eacute;n, J., Alitalo, I., Vaheri, A., \u0026amp; Vapalahti, O. (2010). Tick-borne encephalitis virus in ticks in Finland, Russian Karelia and Buryatia. \u003cem\u003eJournal of General Virology\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(11). https://doi.org/10.1099/vir.0.023663-0\u003c/li\u003e\n\u003cli\u003eJia, N., Wang, J., Shi, W., Du, L., Sun, Y., Zhan, W., Jiang, J. F., Wang, Q., Zhang, B., Ji, P., Bell-Sakyi, L., Cui, X. M., Yuan, T. T., Jiang, B. G., Yang, W. F., Lam, T. T. Y., Chang, Q. C., Ding, S. J., Wang, X. J., \u0026hellip; Cao, W. C. (2020). Large-Scale Comparative Analyses of Tick Genomes Elucidate Their Genetic Diversity and Vector Capacities. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e182\u003c/em\u003e(5). https://doi.org/10.1016/j.cell.2020.07.023\u003c/li\u003e\n\u003cli\u003eJongejan, F., \u0026amp; Uilenberg, G. (2004). The global importance of ticks. In \u003cem\u003eParasitology\u003c/em\u003e (Vol. 129, Number SUPPL.). https://doi.org/10.1017/S0031182004005967\u003c/li\u003e\n\u003cli\u003eKatoh, K., \u0026amp; Standley, D. M. (2013). MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4). https://doi.org/10.1093/molbev/mst010\u003c/li\u003e\n\u003cli\u003eKelliher, J. M., Xu, Y., Flynn, M. C., Babinski, M., Canon, S., Cavanna, E., Clum, A., Corilo, Y. E., Fujimoto, G., Giberson, C., Johnson, L. Y. D., Li, K. J., Li, P. E., Li, V., Lo, C. C., Lynch, W., Piehowski, P., Prime, K., Purvine, S., \u0026hellip; Chain, P. S. G. (2024). Standardized and accessible multi-omics bioinformatics workflows through the NMDC EDGE resource. \u003cem\u003eComputational and Structural Biotechnology Journal\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e. https://doi.org/10.1016/j.csbj.2024.09.018\u003c/li\u003e\n\u003cli\u003eKholodilov, I. S., Belova, O. A., Ivannikova, A. Y., Gadzhikurbanov, M. N., Makenov, M. T., Yakovlev, A. S., Polienko, A. E., Dereventsova, A. V., Litov, A. G., Gmyl, L. V., Okhezin, E. V., Luchinina, S. V., Klimentov, A. S., \u0026amp; Karganova, G. G. (2022). Distribution and Characterisation of Tick-Borne Flavi-, Flavi-like, and Phenuiviruses in the Chelyabinsk Region of Russia. \u003cem\u003eViruses\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(12). https://doi.org/10.3390/v14122699\u003c/li\u003e\n\u003cli\u003eKim, D., Paggi, J. M., Park, C., Bennett, C., \u0026amp; Salzberg, S. L. (2019). Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. \u003cem\u003eNature Biotechnology\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(8), 907\u0026ndash;915. https://doi.org/10.1038/s41587-019-0201-4\u003c/li\u003e\n\u003cli\u003eKishimoto, M., Itakura, Y., Tabata, K., Komagome, R., Yamaguchi, H., Ogasawara, K., Nakao, R., Qiu, Y., Sato, K., Kawabata, H., Kajihara, M., Monma, N., Seto, J., Shigeno, A., Horie, M., Sasaki, M., Hall, W. W., Sawa, H., Orba, Y., \u0026amp; Matsuno, K. (2024). A wide distribution of Beiji nairoviruses and related viruses in Ixodes ticks in Japan. \u003cem\u003eTicks and Tick-Borne Diseases\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(6). https://doi.org/10.1016/j.ttbdis.2024.102380\u003c/li\u003e\n\u003cli\u003eKuivanen, S., Levanov, L., Kareinen, L., Sironen, T., J\u0026auml;\u0026auml;skel\u0026auml;inen, A. J., Plyusnin, I., Zakham, F., Emmerich, P., Schmidt-Chanasit, J., Hepojoki, J., Smura, T., \u0026amp; Vapalahti, O. (2019). Detection of novel tick-borne pathogen, alongshan virus, in Ixodes ricinus ticks, south-eastern Finland, 2019. \u003cem\u003eEurosurveillance\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(27). https://doi.org/10.2807/1560-7917.ES.2019.24.27.1900394\u003c/li\u003e\n\u003cli\u003eKumar, M., Sharma, A., \u0026amp; Grover, P. (2019). Triple Tick Attack. \u003cem\u003eCureus\u003c/em\u003e. https://doi.org/10.7759/cureus.4064\u003c/li\u003e\n\u003cli\u003eLaaksonen, M., Sajanti, E., Sormunen, J. J., Penttinen, R., H\u0026auml;nninen, J., Ruohom\u0026auml;ki, K., S\u0026auml;\u0026auml;ksj\u0026auml;rvi, I., Vesterinen, E. J., Vuorinen, I., Hyt\u0026ouml;nen, J., \u0026amp; Klemola, T. (2017). Crowdsourcing-based nationwide tick collection reveals the distribution of Ixodes ricinus and I. persulcatus and associated pathogens in Finland. \u003cem\u003eEmerging Microbes and Infections\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(5). https://doi.org/10.1038/emi.2017.17\u003c/li\u003e\n\u003cli\u003eLarsson, A. (2014). AliView: A fast and lightweight alignment viewer and editor for large datasets. \u003cem\u003eBioinformatics\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(22). https://doi.org/10.1093/bioinformatics/btu531\u003c/li\u003e\n\u003cli\u003eLefkowitz, E. J., Dempsey, D. M., Hendrickson, R. C., Orton, R. J., Siddell, S. G., \u0026amp; Smith, D. B. (2018). Virus taxonomy: The database of the International Committee on Taxonomy of Viruses (ICTV). \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(D1). https://doi.org/10.1093/nar/gkx932\u003c/li\u003e\n\u003cli\u003eLetunic, I., \u0026amp; Bork, P. (2024). Interactive Tree of Life (iTOL) v6: Recent updates to the phylogenetic tree display and annotation tool. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(W1). https://doi.org/10.1093/nar/gkae268\u003c/li\u003e\n\u003cli\u003eLi, C. X., Shi, M., Tian, J. H., Lin, X. D., Kang, Y. J., Chen, L. J., Qin, X. C., Xu, J., Holmes, E. C., \u0026amp; Zhang, Y. Z. (2015). Unprecedented genomic diversity of RNA viruses in arthropods reveals the ancestry of negative-sense RNA viruses. \u003cem\u003eELife\u003c/em\u003e, \u003cem\u003e2015\u003c/em\u003e(4). https://doi.org/10.7554/eLife.05378\u003c/li\u003e\n\u003cli\u003eLindgren, E., T\u0026auml;lleklint, L., \u0026amp; Polfeldt, T. (2000). Impact of climatic change on the northern latitude limit and population density of the disease-transmitting European tick Ixodes ricinus. \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e, \u003cem\u003e108\u003c/em\u003e(2). https://doi.org/10.2307/3454509\u003c/li\u003e\n\u003cli\u003eLove, M. I., Huber, W., \u0026amp; Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(12). https://doi.org/10.1186/s13059-014-0550-8\u003c/li\u003e\n\u003cli\u003eMa, J., Lv, X. L., Zhang, X., Han, S. Z., Wang, Z. D., Li, L., Sun, H. T., Ma, L. X., Cheng, Z. L., Shao, J. W., Chen, C., Zhao, Y. H., Sui, L., Liu, L. N., Qian, J., Wang, W., \u0026amp; Liu, Q. (2021). Identification of a new orthonairovirus associated with human febrile illness in China. \u003cem\u003eNature Medicine\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(3). https://doi.org/10.1038/s41591-020-01228-y\u003c/li\u003e\n\u003cli\u003eMartyn, C., Hayes, B. M., Lauko, D., Midthun, E., Castaneda, G., Bosco-Lauth, A., Salkeld, D. J., Kistler, A., Pollard, K. S., \u0026amp; Chou, S. (2024). Metatranscriptomic investigation of single Ixodes pacificus ticks reveals diverse microbes, viruses, and novel mRNA-like endogenous viral elements . \u003cem\u003eMSystems\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(6). https://doi.org/10.1128/msystems.00321-24\u003c/li\u003e\n\u003cli\u003eMeng, F., Ding, M., Tan, Z., Zhao, Z., Xu, L., Wu, J., He, B., \u0026amp; Tu, C. (2019). Virome analysis of tick-borne viruses in Heilongjiang Province, China. \u003cem\u003eTicks and Tick-Borne Diseases\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2). https://doi.org/10.1016/j.ttbdis.2018.12.002\u003c/li\u003e\n\u003cli\u003eNamina, A., Kazarina, A., Lazovska, M., Akopjana, S., Ulanova, V., Kivrane, A., Freimane, L., Sadovska, D., Kimsis, J., Bormane, A., Capligina, V., \u0026amp; Ranka, R. (2023). Comparative Microbiome Analysis of Three Epidemiologically Important Tick Species in Latvia. \u003cem\u003eMicroorganisms\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(8). https://doi.org/10.3390/microorganisms11081970\u003c/li\u003e\n\u003cli\u003eNayfach, S., Camargo, A. P., Schulz, F., Eloe-Fadrosh, E., Roux, S., \u0026amp; Kyrpides, N. C. (2021). CheckV assesses the quality and completeness of metagenome-assembled viral genomes. \u003cem\u003eNature Biotechnology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(5). https://doi.org/10.1038/s41587-020-00774-7\u003c/li\u003e\n\u003cli\u003eNekoei, S., Khamesipour, F., Benchimol, M., Bueno-Mar\u0026iacute;, R., \u0026amp; Ommi, D. (2022). SARS-CoV-2 Transmission by Arthropod Vectors: A Scoping Review. In \u003cem\u003eBioMed Research International\u003c/em\u003e (Vol. 2022). https://doi.org/10.1155/2022/4329423\u003c/li\u003e\n\u003cli\u003eNguyen, L. T., Schmidt, H. A., Von Haeseler, A., \u0026amp; Minh, B. Q. (2015). IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(1). https://doi.org/10.1093/molbev/msu300\u003c/li\u003e\n\u003cli\u003eParola, P., \u0026amp; Raoult, D. (2001). Tick-borne bacterial diseases emerging in Europe. \u003cem\u003eClinical Microbiology and Infection\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2). https://doi.org/10.1046/j.1469-0691.2001.00200.x\u003c/li\u003e\n\u003cli\u003ePettersson, J. H. O., Shi, M., Bohlin, J., Eldholm, V., Brynildsrud, O. B., Paulsen, K. M., Andreassen, \u0026Aring;., \u0026amp; Holmes, E. C. (2017). Characterizing the virome of Ixodes ricinus ticks from northern Europe. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1). https://doi.org/10.1038/s41598-017-11439-y\u003c/li\u003e\n\u003cli\u003eR Core Team. (2022). R Core Team 2021 R: A language and environment for statistical computing. R foundation for statistical computing. https://www.R-project.org/. \u003cem\u003eR Foundation for Statistical Computing\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eReuben Kaufman, W. (2007). Gluttony and sex in female ixodid ticks: How do they compare to other blood-sucking arthropods? \u003cem\u003eJournal of Insect Physiology\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(3). https://doi.org/10.1016/j.jinsphys.2006.10.004\u003c/li\u003e\n\u003cli\u003eShi, M., Lin, X. D., Tian, J. H., Chen, L. J., Chen, X., Li, C. X., Qin, X. C., Li, J., Cao, J. P., Eden, J. S., Buchmann, J., Wang, W., Xu, J., Holmes, E. C., \u0026amp; Zhang, Y. Z. (2016). Redefining the invertebrate RNA virosphere. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e540\u003c/em\u003e(7634). https://doi.org/10.1038/nature20167\u003c/li\u003e\n\u003cli\u003eShi, M., Lin, X.-D., Vasilakis, N., Tian, J.-H., Li, C.-X., Chen, L.-J., Eastwood, G., Diao, X.-N., Chen, M.-H., Chen, X., Qin, X.-C., Widen, S. G., Wood, T. G., Tesh, R. B., Xu, J., Holmes, E. C., \u0026amp; Zhang, Y.-Z. (2016). Divergent Viruses Discovered in Arthropods and Vertebrates Revise the Evolutionary History of the Flaviviridae and Related Viruses. \u003cem\u003eJournal of Virology\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(2). https://doi.org/10.1128/jvi.02036-15\u003c/li\u003e\n\u003cli\u003eShimada, S., Aoki, K., Nabeshima, T., Fuxun, Y., Kurosaki, Y., Shiogama, K., Onouchi, T., Sakaguchi, M., Fuchigami, T., Ono, H., Nishi, K., Posadas-Herrera, G., Uchida, L., Takamatsu, Y., Yasuda, J., Tsutsumi, Y., Fujita, H., Morita, K., \u0026amp; Hayasaka, D. (2016). Tofla virus: A newly identified Nairovirus of the Crimean-Congo hemorrhagic fever group isolated from ticks in Japan. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e. https://doi.org/10.1038/srep20213\u003c/li\u003e\n\u003cli\u003eSteenwyk, J. L., Buida, T. J., Li, Y., Shen, X. X., \u0026amp; Rokas, A. (2020). ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference. \u003cem\u003ePLoS Biology\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(12). https://doi.org/10.1371/journal.pbio.3001007\u003c/li\u003e\n\u003cli\u003eTokarz, R., Sameroff, S., Tagliafierro, T., Jain, K., Williams, S. H., Cucura, D. M., Rochlin, I., Monzon, J., Carpi, G., Tufts, D., Diuk-Wasser, M., Brinkerhoff, J., \u0026amp; Lipkin, W. I. (2018). Identification of Novel Viruses in Amblyomma americanum , Dermacentor variabilis, and Ixodes scapularis Ticks . \u003cem\u003eMSphere\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(2). https://doi.org/10.1128/msphere.00614-17\u003c/li\u003e\n\u003cli\u003eUusitalo, R., Siljander, M., Lind\u0026eacute;n, A., Sormunen, J. J., Aalto, J., Hendrickx, G., Kallio, E., Vajda, A., Gregow, H., Henttonen, H., Marsboom, C., Korhonen, E. M., Sironen, T., Pellikka, P., \u0026amp; Vapalahti, O. (2022). Predicting habitat suitability for Ixodes ricinus and Ixodes persulcatus ticks in Finland. \u003cem\u003eParasites and Vectors\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1). https://doi.org/10.1186/s13071-022-05410-8\u003c/li\u003e\n\u003cli\u003eVanmechelen, B., Merino, M., Vergote, V., Laenen, L., Thijssen, M., Mart\u0026iacute;-Carreras, J., Claerebout, E., \u0026amp; Maes, P. (2021). Exploration of the Ixodes ricinus virosphere unveils an extensive virus diversity including novel coltiviruses and other reoviruses. \u003cem\u003eVirus Evolution\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2). https://doi.org/10.1093/ve/veab066\u003c/li\u003e\n\u003cli\u003eWang, J., Wang, J., Kuang, G., Wu, W., Yang, L., Yang, W., Pan, H., Han, X., Yang, T., Shi, M., \u0026amp; Feng, Y. (2023). Meta-transcriptomics for the diversity of tick-borne virus in Nujiang, Yunnan Province. \u003cem\u003eFrontiers in Cellular and Infection Microbiology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e. https://doi.org/10.3389/fcimb.2023.1283019\u003c/li\u003e\n\u003cli\u003eWang, R., Liu, S., Sun, H., Xu, C., Wen, Y., Wu, X., Zhang, W., Nie, K., Li, F., Fu, S., Yin, Q., He, Y., Xu, S., Liang, G., Deng, L., Wei, Q., \u0026amp; Wang, H. (2024). Metatranscriptomics Reveals the RNA Virome of Ixodes Persulcatus in the China\u0026ndash;North Korea Border, 2017. \u003cem\u003eViruses\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1). https://doi.org/10.3390/v16010062\u003c/li\u003e\n\u003cli\u003eWang, S. S., Liu, J. Y., Wang, B. Y., Wang, W. J., Cui, X. M., Jiang, J. F., Sun, Y., Guo, W. Bin, Pan, Y. S., Zhou, Y. H., Lin, Z. T., Jiang, B. G., Zhao, L., \u0026amp; Cao, W. C. (2023). Geographical distribution of Ixodes persulcatus and associated pathogens: Analysis of integrated data from a China field survey and global published data. \u003cem\u003eOne Health\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e. https://doi.org/10.1016/j.onehlt.2023.100508\u003c/li\u003e\n\u003cli\u003eWang, S., Zhao, T., Yu, X., Lin, Z., Hua, X., \u0026amp; Cui, L. (2020). Characterization of tick viromes collected from dogs in China. \u003cem\u003eBiosafety and Health\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(2). https://doi.org/10.1016/j.bsheal.2020.03.002\u003c/li\u003e\n\u003cli\u003eWang, Y. C., Wei, Z., Lv, X., Han, S., Wang, Z., Fan, C., Zhang, X., Shao, J., Zhao, Y. H., Sui, L., Chen, C., Liao, M., Wang, B., Jin, N., Li, C., Ma, J., Hou, Z. J., Yang, Z., Han, Z., \u0026hellip; Liu, Q. (2021). A new nairo-like virus associated with human febrile illness in China. \u003cem\u003eEmerging Microbes and Infections\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1). https://doi.org/10.1080/22221751.2021.1936197\u003c/li\u003e\n\u003cli\u003eWickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., Fran\u0026ccedil;ois, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., M\u0026uuml;ller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., \u0026hellip; Yutani, H. (2019). Welcome to the Tidyverse. \u003cem\u003eJournal of Open Source Software\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(43). https://doi.org/10.21105/joss.01686\u003c/li\u003e\n\u003cli\u003eWood, D. E., Lu, J., \u0026amp; Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. \u003cem\u003eGenome Biology\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1). https://doi.org/10.1186/s13059-019-1891-0\u003c/li\u003e\n\u003cli\u003eZając, Z., Kulisz, J., Bartosik, K., Woźniak, A., Dzierżak, M., \u0026amp; Khan, A. (2021). Environmental determinants of the occurrence and activity of Ixodes ricinus ticks and the prevalence of tick-borne diseases in eastern Poland. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1). https://doi.org/10.1038/s41598-021-95079-3\u003c/li\u003e\n\u003cli\u003eZakham, F., Albalawi, A. E., Alanazi, A. D., Nguyen, P. T., Alouffi, A. S., Alaoui, A., Sironen, T., Smura, T., \u0026amp; Vapalahti, O. (2021). Viral RNA metagenomics of Hyalomma ticks collected from dromedary camels in Makkah province, Saudi Arabia. \u003cem\u003eViruses\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(7). https://doi.org/10.3390/v13071396\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"tick-borne viruses, metatranscriptomics, Ixodes ricinus, Ixodes persulcatus, viral ecology, phylogenomics","lastPublishedDoi":"10.21203/rs.3.rs-9279202/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9279202/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTick-borne viruses (TBVs) are a threat to human and animal health in northern Europe, where \u003cem\u003eIxodes ricinus\u003c/em\u003e and \u003cem\u003eIxodes persulcatus\u003c/em\u003e are undergoing range expansion. In Finland, the genetic diversity, population structure, and tick-associated RNA virome of these species remain insufficiently characterized. Characterizing tick-borne viruses in Finland is essential to detect emerging pathogens at the northern edge of tick expansion, where climate-driven ecological change may facilitate the spread of novel viruses with potential risks to human and animal health. We applied single-tick metatranscriptomic approach to profile and compare the RNA viromes of 92 adult \u003cem\u003eI. ricinus\u003c/em\u003e and \u003cem\u003eI. persulcatus\u003c/em\u003e collected from two locations in Finland. We identified 49 RNA viruses spanning 12 families, with Nairoviridae, Phenuiviridae, Partitiviridae, Flaviviridae, and Rhabdoviridae being the most represented. Viral community composition differed significantly between tick species, with \u003cem\u003eI. persulcatus\u003c/em\u003e exhibiting more constrained but bunyaviridae-dominated viromes, whereas \u003cem\u003eI. ricinus\u003c/em\u003e showed greater inter-individual variability and enrichment of partiti-like and iflavirus taxa. Phylogenetic reconstruction of conserved viral proteins supported the presence of several divergent viral lineages and confirmed first detection of multiple Eurasian tick-associated viruses in Finland, including Nuomin virus, Gakugsa tick virus, and Onega tick phlebovirus. Several detected viruses clustered within clades that include known or suspected zoonotic agents, highlighting the need for close monitoring of tick populations, especially in zones of sympatric occurrence where hybridization and associated close contact between the tick species has been observed. These findings reveal substantial, species-structured viral diversity in Finnish \u003cem\u003eIxodes\u003c/em\u003e ticks.\u003c/p\u003e","manuscriptTitle":"Meta-transcriptomic profiling of Ixodes ricinus and Ixodes persulcatus ticks from Finland reveals diverse RNA viromes, community structuring, and emerging viral lineages","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 18:36:39","doi":"10.21203/rs.3.rs-9279202/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9be7c2f5-647f-4a4b-b1e6-b84afa4a461b","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-07T13:23:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T10:21:40+00:00","index":60,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T16:13:04+00:00","index":59,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T13:38:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 18:36:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9279202","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9279202","identity":"rs-9279202","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}