Mosquito Viromes in England and Wales Reveal Hidden Arbovirus Signals and Limited Ecological Structuring

28 Outbreaks of mosquito-borne viruses are increasing in temperate regions, with West 29 Nile and Usutu viruses now established in wide regions across Europe, and both 30 detected in the UK. Current surveillance strategies focus on targeted approaches which 31 are well suited for monitoring established threats but limited in their ability to detect 32 recently described or neglected viruses. High throughput sequencing (HTS) provides an 33 unbiased alternative, allowing simultaneous identification of well-recognised and 34 overlooked arboviruses, alongside insect-specific viruses (ISVs) that may modulate 35 vector competence of the insects transmitting these pathogens. 36 This study presents the first comprehensive virome survey of Culex mosquitoes in the 37 UK, analysing populations collected from 93 sites across England and Wales through 38 HTS and a systematic virus discovery pipeline. Across these sites, 41 distinct viral taxa 39 were identified, including 11 novel species. Most viruses were rare or confined to a few 40 sites, with only three detected in more than one third of sites, suggesting the absence of 41 a broad conserved virome across populations. Within this diversity, three arbovirus-42 related lineages were detected: Hedwig virus (Peribunyaviridae), Umatilla virus 43 (Sedoreoviridae), and Atherstone virus (Peribunyaviridae), the former two representing 44 the first detections in the UK. These putative arboviruses were embedded in viral 45 communities that showed minimal structuring by coarse land type but a modest 46 decline in richness with latitude across rural sites, consistent with diversity gradients 47 observed in other microbial systems. 48 Together, these findings provide the first national-scale baseline of Culex mosquito-49 associated viral diversity in the UK, and demonstrate the value of metagenomic 50 approaches in arbovirus preparedness. 51 52 53 54 55 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint

56 Arboviral activity in Europe has intensified in recent years, with previously sporadic 57 detections giving way to sustained transmission in some regions [1] and novel viruses 58 appearing in areas where they were historically absent [2]. Usutu virus (USUV) and West 59 Nile virus (WNV) are now established across parts of central and southern Europe [3–5], 60 with USUV causing repeated epizootics in wild birds [6] and WNV showing seasonal 61 transmission in countries such as Italy, Greece, and Spain [7–10]. In the UK, USUV 62 became the first enzootic mosquito-borne virus following its detection in birds and 63 mosquitoes in 2020 [11], and in 2023, WNV was detected in mosquitoes for the first 64 time [12], reflecting the country’s growing alignment with broader European arbovirus 65 trends. While USUV and WNV are currently viewed as the primary mosquito-borne 66 threats, other arboviruses, including alphaviruses (e.g. Sindbis virus [13] and 67 chikungunya virus [14]) and orthobunyaviruses [15–17], such as Tahyna virus, have also 68 been reported in European mosquito populations. This highlights the wide range of 69 arboviruses circulating across the continent, which may pose future emergence risks 70 for the UK [18,19]. 71 Despite these detections, the UK has yet to conduct a large-scale survey of mosquito-72 associated viral diversity. In common with most regions, current surveillance remains 73 focused on a small number of established threats, primarily through targeted PCR or 74 vertebrate serology [20]. To address this gap, high-throughput sequencing (HTS) offers a 75 powerful and unbiased alternative, enabling simultaneous detection of recognised 76 arboviruses, highly divergent taxa, and viruses with no prior association to mosquitoes 77 [21]. Recent metagenomic investigations from Europe [22–24], Asia [25–27], and the 78 Americas [28,29] have confirmed that mosquito populations harbour unexpectedly rich 79 viral communities, including insect-specific viruses (ISVs) and novel lineages of 80 uncertain host range or pathogenic potential . 81 Among these detections, ISVs have received growing attention due to their 82 demonstrated ability to modulate arbovirus replication and transmission [30,31]. For 83 example, several insect-specific flaviviruses have been shown to reduce dissemination 84 or replication of Zika, West Nile, and dengue viruses in both Aedes and Culex 85 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint mosquitoes [32,33]. The proposed mechanisms include superinfection exclusion [34], 86 in which closely related viruses compete for similar replication niches and cellular 87 factors, or a broader immune priming effect through activation of host antiviral 88 pathways [31]. Despite uncertainty about their role in wild populations, ISVs are 89 increasingly investigated as candidates for biocontrol strategies [35,36]. 90 We previously used metagenomic sequencing to investigate mosquito viromes at two 91 UK zoos, identifying 26 viruses, including the first report of two novel orthobunyaviruses 92 with putative arboviral potential [37]. However, the restricted geographic scope of that 93 study limited inferences about viral prevalence, and ecological drivers of diversity 94 across the UK. 95 Here, we build on that work by conducting the first comprehensive Culex spp. virome 96 survey across England and Wales, analysing mosquitoes collected from 93 sites. The 97

of this study were to (i) characterise the diversity and phylogenetic 98 relationships of viruses associated with native Culex populations, (ii) examine spatial 99 and ecological patterns in virome composition, (iii) identify candidate ISVs that may 100 influence vector competence, and (iv) detect viruses of possible relevance to animal or 101 public health. In doing so, we provide the first national-scale assessment of mosquito-102 associated viral diversity in the UK, highlighting the diversity of viruses present in 103 mosquito populations across the region and informing future surveillance strategies. 104 105 106 107 108 109 110 111 112 113 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint

114 Mosquito collections and pooling 115 Adult mosquitoes were obtained during July 2023 as part of a Culex trapping project 116 covering 200 sites across England and Wales (see [38]). Trapping employed BG-PRO® 117 traps (Biogents AG, Regensburg, Germany) baited with BG-Lure® and BG-CO₂ 118 Generators, together with BG-GAT® gravid traps, which were operated for 72 h at each 119 site. Mosquitoes were stored at –80 °C until processing. 120 Specimens belonging to the Culex pipiens complex and Culex torrentium were 121 identified morphologically and confirmed by PCR as previously described [38]. Between 122 1 and 10 individuals per site were combined to form a pool, depending on site yields 123 (See supplemental data for pooling and collection information). Where >10 mosquitoes 124 were collected from a site, multiple replicates were prepared. Whole mosquitoes were 125 homogenised using a bead beater (5 m/s, 40 s) with 2 mm silica beads in 100 µl 126 Proteinase K buffer (Life Sciences). Of this, 50 µl was reserved for species identification, 127 and 50 µl was retained for pooling. Pooled volumes were adjusted to 500 µl with 1× PBS 128 where required (if under 10 individuals). Only females were included in virome 129 sequencing. 130 In total, 151 pools representing 93 sites were generated for sequencing (Totalling 948 131 individuals). A PBS-only sample was included as a negative control. For the positive 132 control, a Culex pipiens molestus female was fed on a blood meal containing Usutu 133 virus at a final concentration of 4.0 × 10⁷ pfu/ml, corresponding to an estimated dose of 134 ~4.0 × 10⁴ pfu per mosquito (assuming ingestion of ~1 µl of blood). 135 Nucleic acid extraction and viral RNA enrichment 136 Pooled homogenates were centrifuged at 16,000 × g for 5 min at 4 °C, and 300 µl of 137 clarified supernatant was filtered through a 0.45 µm sterile spin filter (Corning Costar 138 Spin). If clogging occurred, the remaining material was transferred to a fresh spin 139 column until all supernatant was processed. 140 Filtered homogenates were treated with 2 units TURBO DNase (Thermo Fisher 141 Scientific) to remove host and bacterial DNA. RNA was purified using RNAClean xp 142 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint beads (Beckman Coulter) according to the manufacturer’s instructions. Ribosomal RNA 143 was depleted using the NEBNext rRNA Depletion Kit (New England Biolabs), 144 supplemented with custom probes targeting conserved Culex rRNA regions. Depletion 145 followed the manufacturer’s protocol with the addition of the mosquito-specific probes. 146 RNA quality and fragment size distribution were assessed using an Agilent 5300 147 Fragment Analyzer, and concentrations were determined using a Qubit™ RNA HS (High 148 Sensitivity) Assay Kit (Thermo Fisher Scientific). Reverse transcription and sequence-149 independent single primer amplification (SISPA) was conducted to enrich viral RNA 150 following the modified protocol described in Pilgrim et al., [37]. 151 Libraries were prepared using the NEBNext Ultra II FS DNA Library Prep Kit for Illumina 152 (New England Biolabs), incorporating fragmentation, end repair, adaptor ligation, and 153 indexing. Clean-up was performed with AMPure XP beads. Libraries were quantified 154 with a Qubit™ 1X dsDNA High Sensitivity assay kit and fragment distributions verified 155 with an Agilent 5300 Fragment Analyzer prior to sequencing. 156 Illumina sequencing 157 All libraries were sequenced on two lanes of the Illumina NovaSeq X Plus platform using 158 25B chemistry with 150 bp paired-end reads, generating 3.332 billion reads. 159 Read processing and assembly 160 Illumina adapter and SISPA primer sequences were trimmed from raw FASTQ files 161 using Cutadapt version 4.5 [39]. Reads were further trimmed to remove low quality 162 bases with a minimum window quality score of 20. Reads shorter than 15 bp were then 163 removed and sequencing quality was assessed with FastQC v0.12.1 [40]. De novo 164 assembly was carried out using MEGAHIT v1.2.9 [41] with default parameters, and only 165 contigs longer than 1,000 nucleotides were retained for further analysis. 166 Initial viral signal detection 167 Putative viral sequences were identified using a combination of homology- and 168 signature-based approaches. First, contigs were compared against the Virus-Host DB 169 virus [42] protein database using BLAST+ v2.15.0 [42], with an e-value cut-off of 1 × 10⁻⁵ 170 and a minimum query coverage per high-scoring segment of 30%. In parallel, VirSorter2 171 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint v2.2.3 [43] was run with default parameters to detect RNA viruses. Any contig identified 172 as viral by either method was carried forward to subsequent steps. 173 Post-assembly re-construction and viral gene detection 174 To improve contiguity, candidate viral contigs were processed with Contig Overlap 175 Based Re-Assembly (COBRA) [44]. Protein-coding genes were predicted from these 176 extended contigs using Prodigal v2.6.3 [45] with the “meta” mode, and the resulting 177 protein sequences were screened against RVDB-prot v29.0 [46] using HMMsearch 178 (HMMER v3.3.2 [47]). Contigs containing proteins with significant similarity to viral 179 families (e-value ≤ 1 × 10⁻⁵) were retained. 180 Completeness estimation and filtering 181 Viral contigs were evaluated for genome completeness using ViralQC [48]. Those with 182 an estimated completeness of at least 50% were retained. Contigs not scored by 183 ViralQC were assessed using a rescue pipeline in which predicted proteins were 184 queried against a custom ICTV-derived NR protein database with MMseqs2 v14.7e284 185 [49], and taxonomy was assigned using a lowest common ancestor approach. 186 Completeness was estimated from MMseqs2 assignments, and the same ≥50% 187 threshold was applied. Results from both approaches were integrated to yield a high-188 confidence viral contig set. 189 Dereplication and genome filtering 190 To reduce redundancy, high-confidence contigs were dereplicated with dRep v3.4.0 191 [50], using a minimum contig length of 1,000 bp, a primary clustering threshold of 90% 192 average nucleotide identity (ANI), and a secondary threshold of 95% ANI. The 193 dereplicated set was re-analysed with Prodigal, and only genomes containing at least 194 one complete open reading frame (partial=00 flag) were retained. 195 Provisional taxonomic annotation and validation 196 Provisional annotations were obtained using BLASTx against the Virus-Host DB, with an 197 e-value cutoff of 1 × 10⁻5, a minimum query coverage of 30%, and up to five hits per 198 contig retained. These assignments were used to guide phylogenetic placement. To 199 verify assembly quality, reads were mapped back to retained genomes with bwa-mem2 200 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint v2.2.1 [51] and coverage inspected in IGV v2.12.3 [52]. Terminal regions with 201 inconsistent read support were trimmed prior to downstream analyses. 202 Phylogenetic analysis 203 For tree reconstruction, only dereplicated contigs containing complete marker genes 204 were used. The RNA-dependent RNA polymerase (RdRp) was selected for RNA viruses, 205 and replication-associated proteins for DNA viruses. Open reading frames (ORFs) were 206 predicted with NCBI ORFfinder [53]. Each marker ORF was compared to the NCBI nr 207 database with BLASTp, and top hits were retrieved alongside representative sequences 208 curated according to International Committee on Taxonomy of Viruses (ICTV) reference 209 species. 210 Multiple sequence alignments were generated for each viral family or order using MAFFT 211 v7.525 [54] with the --maxiterate 1000 --globalpair option to maximise alignment 212 accuracy. Poorly aligned positions were removed with trimAl v1.5 [55], using a gap 213 threshold of 0.75 and a block size of 10. Maximum-likelihood phylogenies were then 214 reconstructed in IQ-TREE2 v2.3.4 [56], with branch support evaluated using 1,000 215 ultrafast bootstrap replicates. Resulting trees were rerooted manually in FigTree v1.4.4 216 [57] to optimise interpretability. Trees were visualised in RStudio v4.3.2 [58] using the 217 ggtree package v3.17.1 [59]. 218 Viral abundance estimation and visualisation 219 Viral abundance was quantified following the approach of De Coninck et al. [23]. Reads 220 were mapped back to the final dereplicated viral contigs using bwa-mem2 v2.2.1 [51], 221 and CoverM v0.7.0 [60] was used to estimate abundance at the contig level. A contig 222 was considered present within a pool if at least 50% of its length was covered by 223 mapped reads. Read counts for viral contigs were summed per pool to generate an 224 abundance matrix. This matrix was subsequently visualised in Rstudio using the 225 pheatmap package [61]. 226 Ecological and geographic distribution of viral communities 227 To explore spatial and ecological patterns, viral presence–absence was determined at 228 the site level based on contig detection criteria (≥50% breadth of coverage). Site-level 229 matrices were collapsed across biological replicates and mapped to surveillance 230 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint locations, with community composition visualised using piechart plots in R (scatterpie 231 v0.2.1 [62]) overlaid on basemaps from rnaturalearth (v0.3.2 [63]). Putative arboviruses 232 were defined as viruses assigned to genera containing recognised arboviral species, 233 and were mapped separately to highlight their distribution across sites. 234 The relative abundance of viruses was calculated to assess variation across 235 environmental and regional gradients. Viral read counts were aggregated at the family 236 level, normalised within each site, and averaged across groups. Comparisons were 237 made across land types (urban and rural) and first-level International Territorial Level 238 (ITL1) regions of England and Wales. Visualisation was carried out using ggplot2 v3.5.1 239 [64]. 240 To test for ecological associations, site-level presence–absence matrices were used to 241 compare detection frequencies between urban and rural sites. Fisher’s exact tests were 242 performed independently for each virus species and family, with false discovery rate 243 (FDR) correction applied. In parallel, differential abundance testing was carried out at 244 the site level to evaluate whether specific viral taxa were enriched in urban versus rural 245 sites. Read counts were collapsed across replicates, aggregated by species or family, 246 and analysed using DESeq2 (v1.36 [65]). To reduce the influence of rare taxa and 247 spurious enrichment driven by highly skewed read distributions in a small number of 248 samples [66,67], we applied a prevalence filter prior to differential abundance testing. 249 Specifically, we retained only families present in at least ~20% of sites within both urban 250 and rural groups (≥9 sites per group). Species-level patterns were examined only within 251 families that passed this filter, to help identify potential contributors to family-level 252 signals. 253 Alpha and beta diversity analyses 254 To examine within- and between-site viral diversity, viral read counts were rarefied to a 255 common depth corresponding to the 5th percentile of non-zero library sizes, with 256 rarefaction repeated 1,000 times and mean diversity values retained. 257 Alpha diversity was quantified using observed richness (number of distinct viral taxa) 258 and Shannon diversity, calculated in vegan v2.6-4 [68]. Diversity values were compared 259 across land types (Rural and Urban) and mosquito species using Wilcoxon rank-sum 260 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint tests, with p-values adjusted for multiple comparisons using the Benjamini–Hochberg 261 procedure. Associations between alpha diversity and geographic coordinates (latitude 262 and longitude) were first evaluated with Spearman rank correlations, and the strength of 263 linear trends was subsequently assessed using least-squares regression. 264 Beta diversity was assessed using Bray–Curtis dissimilarities computed from relative 265 abundance matrices. Ordinations were performed by principal coordinates analysis 266 (PCoA) and non-metric multidimensional scaling (NMDS) in vegan, with ordination plots 267 visualised in ggplot2. PERMANOVA (9,999 permutations) was used to test for effects of 268 land type, latitude, and longitude on viral community composition, focusing on Culex 269 pipiens to allow balanced comparisons across land types. Homogeneity of multivariate 270 dispersion (PERMDISP) was evaluated using centroid-based distances. 271 An overview of the full experimental workflow is summarised in Figure 1. 272 273 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint 274 Figure 1. Schematic overview of the project workflow showing sampling, laboratory, 275 and analytical steps used in the study. 276 277 278 279 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint

280 Taxonomic breadth and phylogenetic placement 281 The viral sequence processing pipeline yielded 253 dereplicated contigs containing at 282 least one complete ORF across the 151 libraries. Among these, complete hallmark 283 genes (RdRp for RNA viruses or Rep for DNA viruses) were recovered for 41 distinct taxa, 284 spanning RNA viruses and a single DNA virus. At least one of these viruses was detected 285 at 86 of the 93 sampled sites across England and Wales. These comprised negative-286 sense RNA viruses (n = 10), positive-sense RNA viruses (n = 22), double-stranded RNA 287 viruses (n = 8), and a single-stranded DNA virus (n = 1). Phylogenetic reconstruction 288 confirmed the placement of most lineages within recognised viral families, including 289 Iflaviridae (n = 5), Solemoviridae (n=3), Tymoviridae (n = 3), Peribunyaviridae (n = 2), 290 Partitiviridae (n = 2), Rhabdoviridae (n = 2), Sedoreoviridae (n = 2), Orthomyxoviridae (n = 291 2), Xinmoviridae (n = 2), Amalgaviridae (n = 1), Chrysoviridae (n = 1), Chuviridae (n = 1), 292 Dicistroviridae (n = 1), Draupnirviridae (n = 1), Mesoniviridae (n=1), Nodaviridae (n=1). In 293 addition, several sequences clustered outside established ICTV-designated viral 294 families including 4 Negev-like viruses, Culex bunyavirus 2 (Order: Hareavirales), 295 Daeseongdong-like virus 2, two Ghabrivirales spp., two Tolivirales spp. and one 296 Tymovirales spp. (Table 1 and supplemental data). 297 In total, 11 viruses met ICTV criteria for novel species, with RNA-dependent RNA 298 polymerase amino acid identities to their closest known relatives ranging from 31% to 299 84% (Table 1). All taxa were distinct based on dereplication and phylogenetic criteria, 300 except Ghabrivirales sp. 1 and Ghabrivirales sp. 2, which share 95 % amino-acid identity 301 in the RdRp and are therefore considered a single provisional species under ICTV 302 demarcation standards. 303 Taxonomic highlights 304 Twelve viruses were detected in both this national survey and our previous zoo-based 305 survey [37] (Table 1). The remaining detections represented taxa not previously 306 observed in our earlier dataset. Among RNA viruses, members of the Picornavirales (five 307 Iflaviridae and one Dicistroviridae) and Mononegavirales (four taxa) were prominent; 308 phylogenetic analyses placed all of these within insect-specific clades (Supplemental 309 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint data). Two taxa from the Quaranjavirus genus (Wuhan mosquito virus 4 and Wuhan 310 mosquito virus 6) and four Negev-like viruses were identified, grouping with established 311 mosquito-associated clades. Beyond insect-associated taxa, several lineages typically 312 linked to plants or fungi were also present, including members of the Solemoviridae, 313 Chrysoviridae, Ghabrivirales, Partitiviridae, and Amalgaviridae. One partitivirus 314 matched Culex pipiens betapartitivirus 2, previously reported in the UK [37], while 315 another represented a novel deltapartitivirus. 316 317 318 319 320 321 322 323 324 325 326 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint 327 Table 1. Summary of viruses detected in Culex spp., showing taxonomy based on phylogenetic placement and ICTV designation, mosquito hosts, nearest relatives, and 328 whether novel or previously reported in the UK. ANI = Average Nucleotide Identity. Asterisks mark cases with no defined ICTV species threshold, where a 90% cutoff was 329 applied based on the most commonly used standard.330 Virus name Virus order Virus family Virus genus Cx. pipiens Cx. molestus Cx. torrentium ICTV Species demarcation (RDRP amino acid %) Nearest hit accession (amino acid identity) Novel virus Previously detected in UK? Almendravirus Chester Mononegavirales Rhabdoviridae Almendravirus + - - 90 Almendravirus Chester; OZ253854.1 (100%) N Y Alphamesonivirus fluvideense Nidovirales Mesoniviridae Alphamesonivirus + + + 90* Alphamesonivirus cavallyense; ALP32023.1 (100%) N Y Amalgaviridae sp. Durnavirales Amalgaviridae Unclassified + - - 90* Tetrodontophora bielanensis associated virus 1; DAB41738.1 (37%) Y N Atherstone virus Elliovirales Peribunyaviridae Orthobunyavirus + - - 96 Atherstone virus; OZ253933.1 (100%) N Y Chrysoviridae sp. Ghabrivirales Chrysoviridae Alphachrysovirus + - - 70 Chrysoviridae sp.; XLV26575.1 (100%) N N Cripavirus pipiens Picornavirales Dicistroviridae Cripavirus + - - 90 (capsid protein) Aphis gossypii virus; UUG74202.1 (95%) N N Culex bunyavirus 2 Hareavirales Unclassified Unclassified + - - 90* Culex bunyavirus 2; QRW41985.1 (100%) N Y Culex circovirus-like virus Jormunvirales Draupnirviridae Valentivirus + + - 80 ANI Culex circovirus-like virus; OZ248129.1 (100%) N Y Culex luteo-like virus Sobelivirales Solemoviridae Unclassified + - + 90* Culex luteo-like virus; WVL03164.1 (98%) N N Culex mononega-like virus 1 Mononegavirales Xinmoviridae Unclassified + + - 90* Culex mononega-like virus 1; QGA70931.1 (100%) N N Culex mononega-like virus 2 Mononegavirales Xinmoviridae Unclassified + - + 90* Guadeloupe mosquito mononega-like virus; QEM39177.1 (52%) Y N Culex mosquito virus 4 Jingchuvirales Chuviridae Culicidavirus + - - 90 Culex mosquito virus 4; QRW42864.1 (99%) N Y Culex Negev-like virus 1 Unclassified Unclassified Unclassified + + - 90* Culex Negev-like virus NS46; OZ251790.1 (99%) N Y Culex Negev-like virus 2 Unclassified Unclassified Unclassified + + - 90* Negev virus; BAR91505.1 (100%) N N Culex Negev-like virus 3 Unclassified Unclassified Unclassified + - - 90* Utsjoki negevirus 1; UYL94304.1 (31%) Y N Culex Negev-like virus 4 Unclassified Unclassified Unclassified + - - 90* Culex Negev-like virus 1; UUG74013.1 (97%) N N Culex pipiens betapartitivirus Durnavirales Partitiviridae Betapartitivirus + - - 90 Culex pipiens betapartitivirus 2; OZ253781.1 (100%) N Y Culex pipiens deltapartitivirus Durnavirales Partitiviridae Deltapartitivirus + - - 90 Inari deltapartitivirus; UUV42371.1 (77%) Y N Culex pipiens Ifla-like virus 1 Picornavirales Iflaviridae Iflavirus + - - 90 (capsid protein) Picornavirales sp.; QKN88975.1 (98%) N N Culex pipiens Ifla-like virus 2 Picornavirales Iflaviridae Iflavirus + - - 90 (capsid protein) Culex Iflavi-like virus 4; YP_009552017.1 (98%) N N Culex pipiens nodavirus Nodamuvirales Nodaviridae Unclassified + - - 90* Wufeng shrew nodavirus 5; WPV63049.1 (98%) N N Culex pipiens Tymo-like virus 1 Tymovirales Tymoviridae Unclassified + - - 90* Nasturtium officinale macula-like virus 1; QQG34658.1 (62%) Y N Culex pipiens Tymo-like virus 2 Tymovirales Tymoviridae Unclassified + - + 90* Lampyris noctiluca tymovirus-like virus 1; QBP37021.1 (59%) Y N Culex pipiens Tymo-like virus 3 Tymovirales Tymoviridae Unclassified + - - 90* Sichuan mosquito tymo-like virus; UBJ25983.1 (91%) N N Culex pipiens Tymovirales sp. Tymovirales Unclassified Unclassified + - - 90* Diaporthe helianthi tymovirus 1; WNM95042.1 (70%) Y N Culex Sobemo-like virus Sobelivirales Solemoviridae Unclassified + - - 90* Plasmopara viticola lesion associated sobemo-like 1; QHD64767.1 (51%) Y N Daeseongdong virus 2 Unclassified Unclassified Unclassified + + + 90* Orthornavirae sp.; XLV26731.1 (100%) N Y Ghabrivirales sp. 1 Ghabrivirales Unclassified Unclassified + - + 90* Ghabrivirales sp.; XLV26802.1 (95%) N N Ghabrivirales sp. 2 Ghabrivirales Unclassified Unclassified + - - 90* Ghabrivirales sp.; XLV26802.1 (100%) N N Hedwig virus Elliovirales Peribunyaviridae Gryffinivirus + + - 90 Asum virus; QGA70944.1 (100%) N N Ista virus Picornavirales Iflaviridae Iflavirus + - + 90 (capsid protein) Ista virus; OZ251434.1 (99%) N Y Jotan virus Picornavirales Iflaviridae Iflavirus + - + 90 (capsid protein) Jotan virus; UYL94332.1 (99%) N N Jotan-like virus Picornavirales Iflaviridae Iflavirus + - + 90 (capsid protein) Culex Iflavi-like virus 1; WVL03087.1 (84%) Y N Marma virus Sobelivirales Solemoviridae Unclassified + - - 90* Marma virus; OZ251086 (100%) N Y Merida virus Mononegavirales Rhabdoviridae Merhavirus + - - 90 Merida virus; AWJ96718.1 (97%) N N Tolivirales sp. 1 Tolivirales Unclassified Unclassified + - - 90* Sanya tombus-like virus 2; UHM27571.1 (31%) Y N Tolivirales sp. 2 Tolivirales Unclassified Unclassified + - - 90* Sanxia water strider virus 14; APG76440.1 (43%) Y N Umatilla virus Reovirales Sedoreoviridae Orbivirus + - - 78 Umatilla virus; WZL41624.1 (99%) N N Valmbacken virus Reovirales Sedoreoviridae Unclassified + - - 90 Valmbacken virus; WJJ55410.1 (100%) N N Wuhan Mosquito virus 4 Articulavirales Orthomyxoviridae Quaranjavirus + - - 90* Wuhan Mosquito Virus 4; OZ251991.1 (100%) N Y Wuhan Mosquito virus 6 Articulavirales Orthomyxoviridae Quaranjavirus + + - 90* Wuhan Mosquito Virus 6; AJG39092.1 (98%) N N .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint Arbovirus-related detections 331 Beyond insect-specific lineages, several viruses closely related to recognised 332 arboviruses were also detected (Figures 2 and 4B). Hedwig virus (Family: 333 Peribunyaviridae; Genus: Gryffinivirus) was the most widespread, observed at 10 sites 334 across southern England and Wales: Swansea, Newport, Bristol, Ipswich, Melton 335 Mowbray, Newmarket, Slimbridge, Upper Stoke, and two London localities (Harlesden 336 and Walworth). In six of these detections, complete RdRp ORFs were recovered, each 337 showing >98% amino acid identity to previously reported Hedwig virus sequences 338 (Figure 2B). Some clustered most closely with isolates from Germany, others with 339 viruses reported from Sweden or France, indicating close relationships to multiple 340 European lineages. 341 Umatilla virus (Family: Reoviridae; Genus: Orbivirus) was found at three sites, including 342 Slimbridge, Newport and Plymouth. Representative sequences for the two sites 343 clustered with others obtained from birds caught in Germany during 2019 surveillance 344 (Figure 2A). 345 Atherstone virus (Family: Peribunyaviridae; Genus: Orthobunyavirus) was restricted to 346 two sites in Swindon and Cambridge (Figure 2C and 4B), with RdRp genes showing near 347 identical amino acid identity to the virus reported in our previous study [37] (Accession: 348 OZ254907), and closely related to a partial sequence recently released from a detection 349 in France from 2015 (Accession: PV682945). 350 For each of these viruses, all expected genome segments were recovered (except 351 segment 3 of Umatilla virus) and co-occurred within single pools, confirming assembly 352 of near-complete genomes rather than partial detections (ENA accessions: Hedwig 353 virus – 3 segments [OZ335791- OZ335793]; Umatilla virus – 9 segments [OZ335966, 354 OZ335967, OZ335972, OZ335978, OZ335979, OZ335986, OZ335988, OZ335991, 355 OZ367119]; Atherstone virus – 3 segments [OZ335505, OZ335506, OZ335605]). 356 357 358 359 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint 360 361 Figure 2. Maximum-likelihood trees of the RNA-dependent RNA polymerase (RdRp) ORFs of (A) 362 Umatilla virus (Sedoreoviridae; Orbivirus) (B) Hedwig virus (Peribunyaviridae; Gryffinivirus) (C) 363 Atherstone virus (Peribunyaviridae; Orthobunyavirus). Scale bars represent the number of 364 amino acid substitutions per site. Silhouettes represent host source. 365 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint Virus distribution patterns 366 Virus detections spanned a gradient from widespread to highly restricted taxa (Figure 3). 367 Only three viruses, Daeseongdong virus 2, Wuhan mosquito virus 4, and 368 Alphamesonivirus fluvideense, were widespread, each detected at more than a third of 369 all sites and across all 10 ITL regions (Figures 3B and C). Sixteen viruses showed 370 intermediate distributions, occurring at 6–25 sites, including Chrysoviridae sp. (25 371 sites), Culex Negev-like virus 1 (16 sites), and Marma virus (18 sites). By contrast, the 372 majority of taxa (22/41) were restricted, being found at four or fewer sites, with eight 373 observed only once. While most singletons have not previously been reported in Culex 374 (e.g., Amalgaviridae sp., Culex Negev-like virus 3, Culex pipiens Tymo-like virus 1 and 375 Culex pipiens Tymovirales sp.), others such as Culex pipiens betapartitivirus 2, 376 Almendravirus Chester and Valmbacken virus have been documented in earlier studies 377 [24,37,69], supporting their likely mosquito association despite low prevalence here. 378 379 Figure 3. (A) Heatmap of virus reads detected in Culex spp. pools across 151 libraries from 93 380 sites. Number of sites (B) and ITL regions (C) each taxon was detected across. 381 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint Across 312 taxa detections spanning 86 sites, six families (Mesoniviridae, 382 Chrysoviridae, Orthomyxoviridae, Iflaviridae, Xinmoviridae, and Solemoviridae) 383 accounted for over half of all records (59%; Figure 4A). An additional 23% of detections 384 fell into the ‘Unclassified’ category, reflecting viruses that could not be placed within 385 established families. The majority of these undesignated detections reflected 386 Daeseongdong virus 2, which was widespread, being detected at 70 sites across all 10 387 ITL regions (Figures 3B and C). Relative abundance profiles (read count) across ITL 388 regions (Figure 4C), as well as land type (Figure 4D) were also dominated by these same 389 families. At the detection (presence/absence) level, no viral families or species differed 390 significantly in frequency between urban and rural sites (Fisher’s exact test, Table S1), 391 suggesting no evidence of habitat-specific enrichment. 392 393 Figure 4. (A) Geographic distribution (presence/absence) of viral family detections across 86 of 394 93 sites where at least one taxon was detected. (B) Distribution of putative arboviruses across 395 England and Wales. (C) Mean relative viral abundance (read counts) per site across the 10 ITL 396 regions surveyed. (D) Mean relative viral abundance (read counts) per site by coarse land type. 397 Asterisks denotes enriched taxa (rural vs urban). 398 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint In contrast, abundance-based comparisons (relative viral read counts) identified five 399 families that met the prevalence filter for inclusion (>10% of both urban and rural sites): 400 Orthomyxoviridae, Mesoniviridae, Iflaviridae, Xinmoviridae, and Chrysoviridae. Among 401 these, only Mesoniviridae (urban-enriched, Deseq2 padj = 5.4 × 10⁻⁴) and Xinmoviridae 402 (rural-enriched, Deseq2 padj = 0.031) showed significant differences. 403 Diversity patterns 404 Viral richness and Shannon diversity did not differ significantly between urban and rural 405 sites (Wilcoxon rank test, p > 0.05), with both measures showing similar ranges and 406 dispersion within groups (Figures 5A–C). In Cx. pipiens, median richness was 2.6 (IQR 407 1.9) in rural sites and 2.5 (IQR 1.5) in urban sites, while Shannon diversity was likewise 408 similar (rural: 0.34, IQR 0.65; urban: 0.32, IQR 0.62). For Cx. torrentium (n = 8) and Cx. 409 molestus (n = 3), sample sizes were too limited for meaningful comparisons, though no 410 clear land-use effect was evident. 411 Within Cx. pipiens, alpha diversity showed a significant negative association with 412 latitude across rural sites for observed richness (Spearman’s ρ = –0.44, p = 0.0042; 413 linear regression: R² = 0.182, p = 0.006), but not across urban sites (Spearman’s ρ = –414 0.062, p = 0.72; linear regression: R² = 0.005, p = 0.667). This relationship was not 415 detected for Shannon diversity, indicating that the number of viral taxa declined with 416 increasing latitude but community evenness remained stable (Figures 5D and E). No 417 associations with longitude were observed (See supplemental data). 418 Beta diversity analysis (Figure 5F) based on Bray–Curtis dissimilarities revealed no 419 significant structuring of viral communities by land type (PERMANOVA: R² = 0.015, p = 420 0.264), but showed a borderline association with latitude (R² = 0.022, p = 0.051). 421 Homogeneity of multivariate dispersion was confirmed (PERMDISP; rural mean 0.651 ± 422 0.011 SE, urban 0.630 ± 0.016 SE; p > 0.05), indicating that the lack of PERMANOVA 423 significance reflected a true absence of compositional differences rather than unequal 424 within-group variance. These results were consistent with the NMDS ordination, which 425 showed broad overlap of communities across land types (Figure 5G). 426 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint 427 Figure 5. (A) Observed richness across Culex species (B) Observed richness stratified by 428 species and compared between urban and rural sites (C) Shannon diversity stratified by species 429 and compared between urban and rural sites (D) Association between latitude and observed 430 richness in Cx. pipiens pipiens (solid line = statistical significance) (E) Association between 431 latitude and Shannon diversity in Cx. pipiens pipiens (F) Principal coordinates analysis (PCoA) of 432 Bray–Curtis dissimilarities in Cx. pipiens pipiens, with PERMANOVA testing effects of land type 433 (urban vs. rural) and latitude. (G) Non-metric multidimensional scaling (NMDS) ordination of 434 Bray–Curtis dissimilarities in Cx. pipiens pipiens. 435 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint

436 This study represents the first comprehensive virome survey of UK Culex species, 437 identifying 41 distinct viral taxa, among them three arbovirus-related lineages, including 438 two detected for the first time in the UK. Our dataset, collected in the same year as the 439 first UK WNV detection in Aedes vexans [12], found no evidence of WNV in Culex 440 populations across any site, nor of USUV , the only mosquito-borne virus currently 441 considered established in the UK [70]. By contrast, the detection of other putative 442 arboviruses highlights potential emerging threats, spanning a continuum from 443 neglected but increasingly reported (Umatilla virus) [71,72] to recently characterised 444 with limited detection histories (Hedwig and Atherstone viruses) [37,72]. These results 445 illustrate the added value of high-throughput sequencing as a complementary 446 approach to existing UK surveillance, which has been primarily directed toward WNV 447 and USUV. 448 Umatilla virus (UMAV), an orbivirus in the Sedoreoviridae family, was first isolated in the 449 1960s from Culex spp. collected in the USA [73]. It has since been detected in Australia 450 [74], Japan [75], and Europe [71,72], and has re-emerged as a candidate pathogen in 451 birds. In Germany, UMAV-positive blue tits were repeatedly reported with splenomegaly 452 consistent with acute infection [72]. More strikingly, UMAV infection was confirmed in 453 multiple deceased Cape penguins from a zoo, with one presenting with hepatitis and 454 high viral loads across liver, spleen, and kidney [71]. In addition, a serological survey 455 revealed high exposure rates in free-living pheasants, indicating frequent infection [71], 456 suggesting that some avian species may serve as amplifying hosts, whereas others may 457 be more prone to severe pathology. 458 Hedwig virus (HEDV), a species in the Gryffinivirus genus (Peribunyaviridae) was first 459 reported in Culex pipiens from France in 2015, and has since been detected in 460 mosquitoes in Sweden and Germany, as well as two birds (straw-necked ibis and 461 ferruginous duck) [24,72]; of the necropsy reports available for these animals, 462 pathological findings were inconsistent, leaving the pathogenic role of HEDV 463 unresolved. 464 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint UMAV was detected at two sites near the Severn Estuary, while HEDV was detected at 465 multiple sites near both the Severn and Thames Estuaries. This geographic overlap 466 highlights estuarine regions as potential entry points for these viruses, consistent with 467 proposed routes of historical incursions of vector-borne pathogens across Europe via 468 migratory bird flyways [76–78]. 469 Another member of the Peribunyaviridae, Atherstone virus, was first characterised in 470 our previous zoo-based study [37]. Shortly after, it was reported from archived Culex 471 pools in southern France, originally screened following the detection of Umbre virus in 472 patient brain tissue [79]. Although Umbre virus was not identified in these mosquitoes, 473 a partial sequence corresponding to Atherstone virus was recovered (reported as “Gili 474 orthobunyavirus”). Together with additional detections at sites in Cambridgeshire and 475 Wiltshire from the present study, these data indicate that Atherstone virus is more 476 widely distributed than initially recognised. Unlike UMAV and HEDV , which already have 477 vertebrate detections, Atherstone virus has so far been detected only in mosquitoes. 478 Nevertheless, its placement within the Orthobunyavirus genus, which includes multiple 479 established arboviruses, and the presence of a vertebrate-specific virulence factor 480 (non-structural S protein) [37,80] support its classification as a putative arbovirus and a 481 priority candidate for studies on vector competence, host range, and potential health 482 significance. 483 Beyond arboviruses, a further 38 distinct viral taxa were detected, with only three 484 viruses detected at more than a third of sampling sites, indicating that most taxa were 485 geographically restricted. A similar pattern was observed by Pan et al. [27], who 486 reported that just 27 of 393 viruses were present in more than 25% of individual 487 mosquitoes across China, likewise suggesting strong spatial structuring of mosquito 488 viromes. In contrast, some studies, often based on large mosquito pools and short 489 contigs annotated at broad taxonomic ranks (e.g. family level or by lowest common 490 ancestor methods) rather than species-level phylogenetic classification, have reported 491 a more conserved core virome [81,82]. Such higher-level analyses, however, tend to 492 overestimate viral ubiquity by grouping genetically distinct species into broader 493 taxonomic units, thereby masking underlying spatial and host-associated heterogeneity. 494 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint Of the most prevalent taxa, Alphamesonivirus fluvideense was particularly notable. 495 Although mesoniviruses are generally regarded as insect-specific [83], recent reports of 496 Alphamesonivirus sequences in horse lung and lymph node tissues associated with 497 respiratory disease [84] raise questions about their broader host associations. The 498 significance of these vertebrate detections remains uncertain and may reflect rare 499 spillover events, but the widespread occurrence of this lineage in this study highlights 500 opportunities for exposure and underscores the value of including mesoniviruses in 501 surveillance frameworks. 502 The interpretation of rarer viruses poses further challenges, as some may reflect dietary 503 or environmental acquisition rather than true mosquito associations [85,86]. However, 504 most singleton detections in this study involved viruses previously reported in Culex 505 viromes (e.g., Valmbacken virus) or belonging to established mosquito-specific lineages 506 (e.g., Negev-like viruses). Similarly, Chrysoviridae, Solemoviridae and Partitiviridae were 507 once considered dietary contaminants but are now consistently recovered across 508 independent Culex virome studies [23,24,81], indicating that they represent persistent 509 mosquito-associated lineages. 510 The UK Culex virome showed little evidence of strong ecological structuring by coarse 511 land type. Frequency-based comparisons indicated no habitat-specific enrichment of 512 viral lineages, but abundance-based analyses suggested modest shifts, with 513 Mesoniviridae more common in urban pools and Xinmoviridae more abundant in rural 514 ones. These differences imply that while presence/absence patterns remain broadly 515 consistent across habitats, virus abundances may still capture ecological contrasts 516 such as variation in larval environments or feeding behaviours [87,88]. A modest but 517 statistically significant negative correlation between latitude and viral richness in Culex 518 pipiens across rural sites was also observed, with higher diversity observed in southern 519 sampling locations. This pattern mirrors the broader latitudinal diversity gradients 520 documented in viral and microbial communities [89,90]. These trends have been 521 attributed to factors such as temperature-dependent insect activity and differences in 522 environmental viral stability [91]. In the case of mosquitoes, warmer temperatures in 523 southern regions may support longer activity periods and larger population sizes, 524 thereby increasing opportunities for viral transmission and maintenance. The absence 525 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint of this trend across our urban sites could reflect the urban heat island effect, which may 526 buffer mosquitoes from broader latitudinal temperature differences [92,93]. 527 The limited and inconsistent ecological structuring observed here highlights the 528 challenges of incorporating virome data into arbovirus risk models. While metagenomic 529 surveillance is clearly valuable for detecting circulating arboviruses, the heterogeneous 530 distribution of ISVs, without evidence of a broad core virome or predictable structuring 531 by land type, makes it difficult to parameterise their potential modulatory effects. 532 Without clearer understanding of where and when particular ISVs are likely to occur, 533 their influence on arbovirus transmission at a population level cannot be reliably 534 incorporated into predictive frameworks. 535 Nonetheless, ISVs are increasingly considered as candidates for biological control [86]. 536 For example, insect-specific flaviviruses have been shown to inhibit the replication of 537 arbo-flaviviruses such as WNV and Dengue fever viruses [32,33]. However, the absence 538 of Insect-specific flaviviruses in our dataset is consistent with other European Culex 539 virome investigations [23,24,69,81]. 540 In contrast, we detected several other ISVs of potential interest, including 541 Bunyaviricetes members such as Culex bunyavirus 2, which has been reported 542 previously from Culex populations [28,37,94], as well as four Tymovirales species 543 (Alsuviricetes), two of which are novel. Given their phylogenetic proximity to arboviruses 544 of concern in Europe, such as Rift Valley fever virus (Bunyaviricetes), as well as 545 chikungunya and Sindbis viruses (Alsuviricetes), these lineages represent logical 546 candidates for targeted evaluation. Rift Valley fever virus has not shown local 547 transmission in continental Europe but remains a priority for surveillance and 548 preparedness due to the risk of introduction and establishment through animal 549 movements [95]. In contrast, chikungunya virus has already caused repeated 550 autochthonous outbreaks in southern Europe [14,96], while Sindbis virus is endemic in 551 parts of northern Europe, where it occasionally causes human infections [13]. 552 553 554 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint

555 As arbovirus threats increase in temperate regions, agnostic virus screening 556 approaches should be seen as essential components of vector-borne disease 557 preparedness. This study exemplifies this with the detection of two known arboviruses 558 previously undetected in the UK and a further putative arbovirus with unknown health 559 impacts. Beyond arboviruses, the UK Culex virome appears heterogeneous, with limited 560 evidence of a conserved core or ecological structuring, aside from a modest latitudinal 561 gradient in richness across rural sites. This suggests that viral communities are shaped 562 predominantly by fine-scale or stochastic processes. Building on this national baseline, 563 longitudinal sampling will be essential to capture temporal dynamics and evaluate 564 broader virome stability. In parallel, integration of accumulating virome data with 565 vertebrate surveillance, including serology in birds, mammals and humans, will help 566 clarify host associations and refine arbovirus risk evaluation. 567 Data availability 568 Virus sequences and raw reads are available at the European Nucleotide Archive under 569 project accession number PRJEB98260. 570 Author contributions 571 MB, MSCB and JM secured funding for this project. JP, MSCB and ACD contributed to 572 the conceptual development of the project. EW, RW, AGCV, JM, MB and MSCB co-573 ordinated fieldwork. JP and EW conducted laboratory work. JP carried out bioinformatic 574 analysis. JP, MSCB and ACD interpreted data. JP produced figures and drafts of the 575 manuscript. All authors assisted in critical revision of the manuscript. 576 577 578 579 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint Funding 580 This work was supported by the United Kingdom Research Innovation/Department for 581 Environment Food and Rural Affairs: Culex distribution, vector competence and threat 582 of transmission of arboviruses to humans and animals in the UK (BB/X018172/1). This 583 research was also partly funded by an HBLB Research Fellowship awarded to JP, as 584 well as a BBSRC grant (BB/W002906/1) awarded to MSCB and MB. 585 Conflicts of interest 586 The authors declare that there are no conflicts of interest. 587

588 We thank Agata Delnicka, Amelia Simpson, Anthony J. Abbott, Colin J. Johnston, Jude 589 Martin, Kendall Barlow, Eloise Aliski, Saffron Shiels, Sara Gandy, and Sarah M. 590 Biddlecombe for their invaluable assistance with mosquito field collections. We also 591 acknowledge the support of Richard Gregory and the Centre for Genomic Research 592 (CGR) at the University of Liverpool for providing access to computational resources 593 used in this research, as well as for the use of CGR’s sequencing facilities. 594

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