{"paper_id":"a94eb982-12f6-442a-b7d2-b538fe4f8e70","body_text":"Mosquito Viromes in England and Wales Reveal Hidden 1 \nArbovirus Signals  and Limited Ecological Structuring  2 \n 3 \nAuthor names: 4 \nJack Pilgrim1†,  Emma Widlake 2, Roksana Wilson 3, Alexander G C Vaux 3, Jolyon M 5 \nMedlock3, Alistair C Darby1,4, Matthew Baylis1, Marcus SC Blagrove1†, 6 \n  7 \nAffiliation: 8 \n1. Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and 9 \nLife Sciences, University of Liverpool, Liverpool, L69 3BX, UK 10 \n2. The School of Life Sciences and the Centre for Applied Entomology and 11 \nParasitology, Keele University, Keele, Newcastle-under-Lyme, ST5 5BG, UK 12 \n3. Medical Entomology and Zoonoses Ecology Group, UK Health Security Agency, 13 \nPorton Down, Salisbury, SP4 0JG UK 14 \n4. Centre for Genomic Research, Institute of Systems, Molecular and Integrative 15 \nBiology, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, 16 \nL69 7ZB, UK 17 \n† Corresponding authors email addresses: 18 \nJack.pilgrim@liverpool.ac.uk 19 \ngrte0276@liverpool.ac.uk 20 \n 21 \nKey words: 22 \nSurveillance, Insect-specific virus, Arbovirus, Culex, Mosquito, Orthobunyavirus, 23 \nOrbivirus, Virome, Metatranscriptomics, High throughput sequencing 24 \n 25 \n 26 \n 27 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nAbstract 28 \nOutbreaks of mosquito-borne viruses are increasing in temperate regions, with West 29 \nNile and Usutu viruses now established in wide regions across Europe, and both 30 \ndetected in the UK. Current surveillance strategies focus on targeted approaches which 31 \nare well suited for monitoring established threats but limited in their ability to detect 32 \nrecently described or neglected viruses. High throughput sequencing (HTS) provides an 33 \nunbiased alternative, allowing simultaneous identification of well-recognised and 34 \noverlooked arboviruses, alongside insect-specific viruses (ISVs) that may modulate 35 \nvector competence of the insects transmitting these pathogens. 36 \nThis study presents the first comprehensive virome survey of Culex mosquitoes in the 37 \nUK, analysing populations collected from 93 sites across England and Wales through 38 \nHTS and a systematic virus discovery pipeline. Across these sites, 41 distinct viral taxa 39 \nwere identified, including 11 novel species. Most viruses were rare or confined to a few 40 \nsites, with only three detected in more than one third of sites, suggesting the absence of 41 \na broad conserved virome across populations. Within this diversity, three arbovirus-42 \nrelated lineages were detected: Hedwig virus (Peribunyaviridae), Umatilla virus 43 \n(Sedoreoviridae), and Atherstone virus (Peribunyaviridae), the former two representing 44 \nthe first detections in the UK. These putative arboviruses were embedded in viral 45 \ncommunities that showed minimal structuring by coarse land type but a modest 46 \ndecline in richness with latitude across rural sites, consistent with diversity gradients 47 \nobserved in other microbial systems. 48 \nTogether, these findings provide the first national-scale baseline of Culex mosquito-49 \nassociated viral diversity in the UK, and demonstrate the value of metagenomic 50 \napproaches in arbovirus preparedness. 51 \n 52 \n 53 \n 54 \n 55 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nIntroduction 56 \nArboviral activity in Europe has intensified in recent years, with previously sporadic 57 \ndetections giving way to sustained transmission in some regions [1] and novel viruses 58 \nappearing in areas where they were historically absent [2]. Usutu virus (USUV) and West 59 \nNile virus (WNV) are now established across parts of central and southern Europe [3–5], 60 \nwith USUV causing repeated epizootics in wild birds [6] and WNV showing seasonal 61 \ntransmission in countries such as Italy, Greece, and Spain [7–10]. In the UK, USUV 62 \nbecame the first enzootic mosquito-borne virus following its detection in birds and 63 \nmosquitoes in 2020 [11], and in 2023, WNV was detected in mosquitoes for the first 64 \ntime [12], reflecting the country’s growing alignment with broader European arbovirus 65 \ntrends. While USUV and WNV are currently viewed as the primary mosquito-borne 66 \nthreats, other arboviruses, including alphaviruses (e.g. Sindbis virus [13] and 67 \nchikungunya virus [14]) and orthobunyaviruses [15–17], such as Tahyna virus, have also 68 \nbeen reported in European mosquito populations. This highlights the wide range of 69 \narboviruses circulating across the continent, which may pose future emergence risks 70 \nfor the UK [18,19]. 71 \nDespite these detections, the UK has yet to conduct a large-scale survey of mosquito-72 \nassociated viral diversity. In common with most regions, current surveillance remains 73 \nfocused on a small number of established threats, primarily through targeted PCR or 74 \nvertebrate serology [20]. To address this gap, high-throughput sequencing (HTS) offers a 75 \npowerful and unbiased alternative, enabling simultaneous detection of recognised 76 \narboviruses, highly divergent taxa, and viruses with no prior association to mosquitoes 77 \n[21]. Recent metagenomic investigations from Europe [22–24], Asia [25–27], and the 78 \nAmericas [28,29] have confirmed that mosquito populations harbour unexpectedly rich 79 \nviral communities, including insect-specific viruses (ISVs) and novel lineages of 80 \nuncertain host range or pathogenic potential . 81 \nAmong these detections, ISVs have received growing attention due to their 82 \ndemonstrated ability to modulate arbovirus replication and transmission [30,31]. For 83 \nexample, several insect-specific flaviviruses have been shown to reduce dissemination 84 \nor replication of Zika, West Nile, and dengue viruses in both Aedes and Culex 85 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nmosquitoes [32,33]. The proposed mechanisms include superinfection exclusion [34], 86 \nin which closely related viruses compete for similar replication niches and cellular 87 \nfactors, or a broader immune priming effect through activation of host antiviral 88 \npathways [31]. Despite uncertainty about their role in wild populations, ISVs are 89 \nincreasingly investigated as candidates for biocontrol strategies [35,36]. 90 \nWe previously used metagenomic sequencing to investigate mosquito viromes at two 91 \nUK zoos, identifying 26 viruses, including the first report of two novel orthobunyaviruses 92 \nwith putative arboviral potential [37]. However, the restricted geographic scope of that 93 \nstudy limited inferences about viral prevalence, and ecological drivers of diversity 94 \nacross the UK. 95 \nHere, we build on that work by conducting the first comprehensive Culex spp. virome 96 \nsurvey across England and Wales, analysing mosquitoes collected from 93 sites. The 97 \nobjectives of this study were to (i) characterise the diversity and phylogenetic 98 \nrelationships of viruses associated with native Culex populations, (ii) examine spatial 99 \nand ecological patterns in virome composition, (iii) identify candidate ISVs that may 100 \ninfluence vector competence, and (iv) detect viruses of possible relevance to animal or 101 \npublic health. In doing so, we provide the first national-scale assessment of mosquito-102 \nassociated viral diversity in the UK, highlighting the diversity of viruses present in 103 \nmosquito populations across the region and informing future surveillance strategies. 104 \n 105 \n 106 \n 107 \n 108 \n 109 \n 110 \n 111 \n 112 \n 113 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nMethods 114 \nMosquito collections and pooling 115 \nAdult mosquitoes were obtained during July 2023 as part of a Culex trapping project 116 \ncovering 200 sites across England and Wales (see [38]). Trapping employed BG-PRO® 117 \ntraps (Biogents AG, Regensburg, Germany) baited with BG-Lure® and BG-CO₂ 118 \nGenerators, together with BG-GAT® gravid traps, which were operated for 72 h at each 119 \nsite. Mosquitoes were stored at –80 °C until processing. 120 \nSpecimens belonging to the Culex pipiens complex and Culex torrentium were 121 \nidentified morphologically and confirmed by PCR as previously described [38]. Between 122 \n1 and 10 individuals per site were combined to form a pool, depending on site yields 123 \n(See supplemental data for pooling and collection information). Where >10 mosquitoes 124 \nwere collected from a site, multiple replicates were prepared. Whole mosquitoes were 125 \nhomogenised using a bead beater (5 m/s, 40 s) with 2 mm silica beads in 100 µl 126 \nProteinase K buffer (Life Sciences). Of this, 50 µl was reserved for species identification, 127 \nand 50 µl was retained for pooling. Pooled volumes were adjusted to 500 µl with 1× PBS 128 \nwhere required (if under 10 individuals). Only females were included in virome 129 \nsequencing. 130 \nIn total, 151 pools representing 93 sites were generated for sequencing (Totalling 948 131 \nindividuals). A PBS-only sample was included as a negative control. For the positive 132 \ncontrol, a Culex pipiens molestus female was fed on a blood meal containing Usutu 133 \nvirus at a final concentration of 4.0 × 10⁷ pfu/ml, corresponding to an estimated dose of 134 \n~4.0 × 10⁴ pfu per mosquito (assuming ingestion of ~1 µl of blood).  135 \nNucleic acid extraction and viral RNA enrichment 136 \nPooled homogenates were centrifuged at 16,000 × g for 5 min at 4 °C, and 300 µl of 137 \nclarified supernatant was filtered through a 0.45 µm sterile spin filter (Corning Costar 138 \nSpin). If clogging occurred, the remaining material was transferred to a fresh spin 139 \ncolumn until all supernatant was processed.  140 \nFiltered homogenates were treated with 2 units TURBO DNase (Thermo Fisher 141 \nScientific) to remove host and bacterial DNA. RNA was purified using RNAClean xp 142 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nbeads (Beckman Coulter) according to the manufacturer’s instructions. Ribosomal RNA 143 \nwas depleted using the NEBNext rRNA Depletion Kit (New England Biolabs), 144 \nsupplemented with custom probes targeting conserved Culex rRNA regions. Depletion 145 \nfollowed the manufacturer’s protocol with the addition of the mosquito-specific probes. 146 \nRNA quality and fragment size distribution were assessed using an Agilent 5300 147 \nFragment Analyzer, and concentrations were determined using a Qubit™ RNA HS (High 148 \nSensitivity) Assay Kit (Thermo Fisher Scientific). Reverse transcription and sequence-149 \nindependent single primer amplification (SISPA) was conducted to enrich viral RNA 150 \nfollowing the modified protocol described in Pilgrim et al., [37]. 151 \nLibraries were prepared using the NEBNext Ultra II FS DNA Library Prep Kit for Illumina 152 \n(New England Biolabs), incorporating fragmentation, end repair, adaptor ligation, and 153 \nindexing. Clean-up was performed with AMPure XP beads. Libraries were quantified 154 \nwith a  Qubit™ 1X dsDNA High Sensitivity assay kit and fragment distributions verified 155 \nwith an Agilent 5300 Fragment Analyzer prior to sequencing. 156 \nIllumina sequencing 157 \nAll libraries were sequenced on two lanes of the Illumina NovaSeq X Plus platform using 158 \n25B chemistry with 150 bp paired-end reads, generating 3.332 billion reads. 159 \nRead processing and assembly 160 \nIllumina adapter and SISPA primer sequences  were trimmed from raw FASTQ files 161 \nusing Cutadapt version 4.5 [39]. Reads were further trimmed to remove low quality 162 \nbases with a minimum window quality score of 20. Reads shorter than 15 bp were then 163 \nremoved and sequencing quality was assessed with FastQC v0.12.1 [40]. De novo 164 \nassembly was carried out using MEGAHIT v1.2.9 [41] with default parameters, and only 165 \ncontigs longer than 1,000 nucleotides were retained for further analysis. 166 \nInitial viral signal detection 167 \nPutative viral sequences were identified using a combination of homology- and 168 \nsignature-based approaches. First, contigs were compared against the Virus-Host DB 169 \nvirus [42] protein database using BLAST+ v2.15.0 [42], with an e-value cut-off of 1 × 10⁻⁵ 170 \nand a minimum query coverage per high-scoring segment of 30%. In parallel, VirSorter2 171 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nv2.2.3 [43] was run with default parameters to detect RNA viruses. Any contig identified 172 \nas viral by either method was carried forward to subsequent steps. 173 \nPost-assembly re-construction and viral gene detection 174 \nTo improve contiguity, candidate viral contigs were processed with Contig Overlap 175 \nBased Re-Assembly (COBRA) [44]. Protein-coding genes were predicted from these 176 \nextended contigs using Prodigal v2.6.3 [45] with the “meta” mode, and the resulting 177 \nprotein sequences were screened against RVDB-prot v29.0 [46] using HMMsearch 178 \n(HMMER v3.3.2 [47]). Contigs containing proteins with significant similarity to viral 179 \nfamilies (e-value ≤ 1 × 10⁻⁵) were retained. 180 \nCompleteness estimation and filtering 181 \nViral contigs were evaluated for genome completeness using ViralQC [48]. Those with 182 \nan estimated completeness of at least 50% were retained. Contigs not scored by 183 \nViralQC were assessed using a rescue pipeline in which predicted proteins were 184 \nqueried against a custom ICTV-derived NR protein database with MMseqs2 v14.7e284 185 \n[49], and taxonomy was assigned using a lowest common ancestor approach. 186 \nCompleteness was estimated from MMseqs2 assignments, and the same ≥50% 187 \nthreshold was applied. Results from both approaches were integrated to yield a high-188 \nconfidence viral contig set. 189 \nDereplication and genome filtering 190 \nTo reduce redundancy, high-confidence contigs were dereplicated with dRep v3.4.0 191 \n[50], using a minimum contig length of 1,000 bp, a primary clustering threshold of 90% 192 \naverage nucleotide identity (ANI), and a secondary threshold of 95% ANI. The 193 \ndereplicated set was re-analysed with Prodigal, and only genomes containing at least 194 \none complete open reading frame (partial=00 flag) were retained. 195 \nProvisional taxonomic annotation and validation 196 \nProvisional annotations were obtained using BLASTx against the Virus-Host DB, with an 197 \ne-value cutoff of 1 × 10⁻5, a minimum query coverage of 30%, and up to five hits per 198 \ncontig retained. These assignments were used to guide phylogenetic placement. To 199 \nverify assembly quality, reads were mapped back to retained genomes with bwa-mem2 200 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nv2.2.1 [51] and coverage inspected in IGV v2.12.3 [52]. Terminal regions with 201 \ninconsistent read support were trimmed prior to downstream analyses. 202 \nPhylogenetic analysis 203 \nFor tree reconstruction, only dereplicated contigs containing complete marker genes 204 \nwere used. The RNA-dependent RNA polymerase (RdRp) was selected for RNA viruses, 205 \nand replication-associated proteins for DNA viruses. Open reading frames (ORFs) were 206 \npredicted with NCBI ORFfinder [53]. Each marker ORF was compared to the NCBI nr 207 \ndatabase with BLASTp, and top hits were retrieved alongside representative sequences 208 \ncurated according to International Committee on Taxonomy of Viruses (ICTV) reference 209 \nspecies. 210 \nMultiple sequence alignments were generated for each viral family or order using MAFFT 211 \nv7.525 [54] with the --maxiterate 1000 --globalpair option to maximise alignment 212 \naccuracy. Poorly aligned positions were removed with trimAl v1.5 [55], using a gap 213 \nthreshold of 0.75 and a block size of 10. Maximum-likelihood phylogenies were then 214 \nreconstructed in IQ-TREE2 v2.3.4 [56], with branch support evaluated using 1,000 215 \nultrafast bootstrap replicates. Resulting trees were rerooted manually in FigTree v1.4.4 216 \n[57] to optimise interpretability. Trees were visualised in RStudio v4.3.2 [58] using the 217 \nggtree package v3.17.1 [59]. 218 \nViral abundance estimation and visualisation 219 \nViral abundance was quantified following the approach of De Coninck et al. [23]. Reads 220 \nwere mapped back to the final dereplicated viral contigs using bwa-mem2 v2.2.1 [51], 221 \nand CoverM v0.7.0 [60] was used to estimate abundance at the contig level. A contig 222 \nwas considered present within a pool if at least 50% of its length was covered by 223 \nmapped reads. Read counts for viral contigs were summed per pool to generate an 224 \nabundance matrix. This matrix was subsequently visualised in Rstudio using the 225 \npheatmap package [61].  226 \nEcological and geographic distribution of viral communities 227 \nTo explore spatial and ecological patterns, viral presence–absence was determined at 228 \nthe site level based on contig detection criteria (≥50% breadth of coverage). Site-level 229 \nmatrices were collapsed across biological replicates and mapped to surveillance 230 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nlocations, with community composition visualised using piechart plots in R (scatterpie 231 \nv0.2.1 [62]) overlaid on basemaps from rnaturalearth (v0.3.2 [63]). Putative arboviruses 232 \nwere defined as viruses assigned to genera containing recognised arboviral species, 233 \nand were mapped separately to highlight their distribution across sites. 234 \nThe relative abundance of viruses was calculated to assess variation across 235 \nenvironmental and regional gradients. Viral read counts were aggregated at the family 236 \nlevel, normalised within each site, and averaged across groups. Comparisons were 237 \nmade across land types (urban and rural) and first-level International Territorial Level 238 \n(ITL1) regions of England and Wales. Visualisation was carried out using ggplot2 v3.5.1 239 \n[64]. 240 \nTo test for ecological associations, site-level presence–absence matrices were used to 241 \ncompare detection frequencies between urban and rural sites. Fisher’s exact tests were 242 \nperformed independently for each virus species and family, with false discovery rate 243 \n(FDR) correction applied. In parallel, differential abundance testing was carried out at 244 \nthe site level to evaluate whether specific viral taxa were enriched in urban versus rural 245 \nsites. Read counts were collapsed across replicates, aggregated by species or family, 246 \nand analysed using DESeq2 (v1.36 [65]). To reduce the influence of rare taxa and 247 \nspurious enrichment driven by highly skewed read distributions in a small number of 248 \nsamples [66,67], we applied a prevalence filter prior to differential abundance testing. 249 \nSpecifically, we retained only families present in at least ~20% of sites within both urban 250 \nand rural groups (≥9 sites per group). Species-level patterns were examined only within 251 \nfamilies that passed this filter, to help identify potential contributors to family-level 252 \nsignals. 253 \nAlpha and beta diversity analyses 254 \nTo examine within- and between-site viral diversity, viral read counts were rarefied to a 255 \ncommon depth corresponding to the 5th percentile of non-zero library sizes, with 256 \nrarefaction repeated 1,000 times and mean diversity values retained. 257 \nAlpha diversity was quantified using observed richness (number of distinct viral taxa) 258 \nand Shannon diversity, calculated in vegan v2.6-4 [68]. Diversity values were compared 259 \nacross land types (Rural and Urban) and mosquito species using Wilcoxon rank-sum 260 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\ntests, with p-values adjusted for multiple comparisons using the Benjamini–Hochberg 261 \nprocedure. Associations between alpha diversity and geographic coordinates (latitude 262 \nand longitude) were first evaluated with Spearman rank correlations, and the strength of 263 \nlinear trends was subsequently assessed using least-squares regression. 264 \nBeta diversity was assessed using Bray–Curtis dissimilarities computed from relative 265 \nabundance matrices. Ordinations were performed by principal coordinates analysis 266 \n(PCoA) and non-metric multidimensional scaling (NMDS) in vegan, with ordination plots 267 \nvisualised in ggplot2. PERMANOVA (9,999 permutations) was used to test for effects of 268 \nland type, latitude, and longitude on viral community composition, focusing on Culex 269 \npipiens to allow balanced comparisons across land types. Homogeneity of multivariate 270 \ndispersion (PERMDISP) was evaluated using centroid-based distances.  271 \nAn overview of the full experimental workflow is summarised in Figure 1. 272 \n 273 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\n 274 \nFigure 1. Schematic overview of the project workflow showing sampling, laboratory, 275 \nand analytical steps used in the study. 276 \n 277 \n 278 \n 279 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nResults 280 \nTaxonomic breadth and phylogenetic placement 281 \nThe viral sequence processing pipeline yielded 253 dereplicated contigs containing at 282 \nleast one complete ORF across the 151 libraries. Among these, complete hallmark 283 \ngenes (RdRp for RNA viruses or Rep for DNA viruses) were recovered for 41 distinct taxa, 284 \nspanning RNA viruses and a single DNA virus. At least one of these viruses was detected 285 \nat 86 of the 93 sampled sites across England and Wales. These comprised negative-286 \nsense RNA viruses (n = 10), positive-sense RNA viruses (n = 22), double-stranded RNA 287 \nviruses (n = 8), and a single-stranded DNA virus (n = 1). Phylogenetic reconstruction 288 \nconfirmed the placement of most lineages within recognised viral families, including 289 \nIflaviridae (n = 5), Solemoviridae (n=3), Tymoviridae (n = 3), Peribunyaviridae (n = 2), 290 \nPartitiviridae (n = 2), Rhabdoviridae (n = 2), Sedoreoviridae (n = 2), Orthomyxoviridae (n = 291 \n2), Xinmoviridae (n = 2), Amalgaviridae (n = 1), Chrysoviridae (n = 1), Chuviridae (n = 1), 292 \nDicistroviridae (n = 1), Draupnirviridae (n = 1), Mesoniviridae (n=1), Nodaviridae (n=1). In 293 \naddition, several sequences clustered outside established ICTV-designated viral 294 \nfamilies including 4 Negev-like viruses, Culex bunyavirus 2 (Order: Hareavirales), 295 \nDaeseongdong-like virus 2, two Ghabrivirales spp., two Tolivirales spp. and one 296 \nTymovirales spp. (Table 1 and supplemental data).  297 \nIn total, 11 viruses met ICTV criteria for novel species, with RNA-dependent RNA 298 \npolymerase amino acid identities to their closest known relatives ranging from 31% to 299 \n84% (Table 1). All taxa were distinct based on dereplication and phylogenetic criteria, 300 \nexcept Ghabrivirales sp. 1 and Ghabrivirales sp. 2, which share 95 % amino-acid identity 301 \nin the RdRp and are therefore considered a single provisional species under ICTV 302 \ndemarcation standards. 303 \nTaxonomic highlights 304 \nTwelve viruses were detected in both this national survey and our previous zoo-based 305 \nsurvey [37] (Table 1). The remaining detections represented taxa not previously 306 \nobserved in our earlier dataset. Among RNA viruses, members of the Picornavirales (five 307 \nIflaviridae and one Dicistroviridae) and Mononegavirales (four taxa) were prominent; 308 \nphylogenetic analyses placed all of these within insect-specific clades (Supplemental 309 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\ndata). Two taxa from the Quaranjavirus genus (Wuhan mosquito virus 4 and Wuhan 310 \nmosquito virus 6) and four Negev-like viruses were identified, grouping with established 311 \nmosquito-associated clades. Beyond insect-associated taxa, several lineages typically 312 \nlinked to plants or fungi were also present, including members of the Solemoviridae, 313 \nChrysoviridae, Ghabrivirales, Partitiviridae, and Amalgaviridae. One partitivirus 314 \nmatched Culex pipiens betapartitivirus 2, previously reported in the UK [37], while 315 \nanother represented a novel deltapartitivirus.  316 \n 317 \n 318 \n 319 \n 320 \n 321 \n 322 \n 323 \n 324 \n 325 \n 326 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\n 327 \nTable 1.  Summary of viruses detected in Culex spp., showing taxonomy based on phylogenetic placement and ICTV designation, mosquito hosts, nearest relatives, and 328 \nwhether 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 \napplied based on the most commonly used standard.330 \nVirus 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?\nAlmendravirus Chester Mononegavirales Rhabdoviridae Almendravirus + - - 90 Almendravirus Chester; OZ253854.1 (100%) N Y\nAlphamesonivirus fluvideense Nidovirales Mesoniviridae Alphamesonivirus + + + 90* Alphamesonivirus cavallyense; ALP32023.1 (100%) N Y\nAmalgaviridae sp. Durnavirales Amalgaviridae Unclassified + - - 90* Tetrodontophora bielanensis associated virus 1; DAB41738.1 (37%) Y N\nAtherstone virus Elliovirales Peribunyaviridae Orthobunyavirus + - - 96 Atherstone virus; OZ253933.1 (100%) N Y\nChrysoviridae sp. Ghabrivirales Chrysoviridae Alphachrysovirus + - - 70 Chrysoviridae sp.; XLV26575.1 (100%) N N\nCripavirus pipiens Picornavirales Dicistroviridae Cripavirus + - - 90 (capsid protein) Aphis gossypii virus; UUG74202.1 (95%) N N\nCulex bunyavirus 2 Hareavirales Unclassified Unclassified + - - 90* Culex bunyavirus 2; QRW41985.1 (100%) N Y\nCulex circovirus-like virus Jormunvirales Draupnirviridae Valentivirus + + - 80 ANI Culex circovirus-like virus; OZ248129.1 (100%) N Y\nCulex luteo-like virus Sobelivirales Solemoviridae Unclassified + - + 90* Culex luteo-like virus; WVL03164.1 (98%) N N\nCulex mononega-like virus 1 Mononegavirales Xinmoviridae Unclassified + + - 90* Culex mononega-like virus 1; QGA70931.1 (100%) N N\nCulex mononega-like virus 2 Mononegavirales Xinmoviridae Unclassified + - + 90* Guadeloupe mosquito mononega-like virus; QEM39177.1 (52%) Y N\nCulex mosquito virus 4 Jingchuvirales Chuviridae Culicidavirus + - - 90 Culex mosquito virus 4; QRW42864.1 (99%) N Y\nCulex Negev-like virus 1 Unclassified Unclassified Unclassified + + - 90* Culex Negev-like virus NS46; OZ251790.1 (99%) N Y\nCulex Negev-like virus 2 Unclassified Unclassified Unclassified + + - 90* Negev virus; BAR91505.1 (100%) N N\nCulex Negev-like virus 3 Unclassified Unclassified Unclassified + - - 90* Utsjoki negevirus 1; UYL94304.1 (31%) Y N\nCulex Negev-like virus 4 Unclassified Unclassified Unclassified + - - 90* Culex Negev-like virus 1; UUG74013.1 (97%) N N\nCulex pipiens betapartitivirus Durnavirales Partitiviridae Betapartitivirus + - - 90 Culex pipiens betapartitivirus 2; OZ253781.1 (100%) N Y\nCulex pipiens deltapartitivirus Durnavirales Partitiviridae Deltapartitivirus + - - 90 Inari deltapartitivirus; UUV42371.1 (77%) Y N\nCulex pipiens Ifla-like virus 1 Picornavirales Iflaviridae Iflavirus + - - 90 (capsid protein) Picornavirales sp.; QKN88975.1 (98%) N N\nCulex pipiens Ifla-like virus 2 Picornavirales Iflaviridae Iflavirus + - - 90 (capsid protein) Culex Iflavi-like virus 4; YP_009552017.1 (98%) N N\nCulex pipiens nodavirus Nodamuvirales Nodaviridae Unclassified + - - 90* Wufeng shrew nodavirus 5; WPV63049.1 (98%) N N\nCulex pipiens Tymo-like virus 1 Tymovirales Tymoviridae Unclassified + - - 90* Nasturtium officinale macula-like virus 1; QQG34658.1 (62%) Y N\nCulex pipiens Tymo-like virus 2 Tymovirales Tymoviridae Unclassified + - + 90* Lampyris noctiluca tymovirus-like virus 1; QBP37021.1 (59%) Y N\nCulex pipiens Tymo-like virus 3 Tymovirales Tymoviridae Unclassified + - - 90* Sichuan mosquito tymo-like virus; UBJ25983.1 (91%) N N\nCulex pipiens Tymovirales sp. Tymovirales Unclassified Unclassified + - - 90* Diaporthe helianthi tymovirus 1; WNM95042.1 (70%) Y N\nCulex Sobemo-like virus Sobelivirales Solemoviridae Unclassified + - - 90* Plasmopara viticola lesion associated sobemo-like 1; QHD64767.1 (51%) Y N\nDaeseongdong virus 2 Unclassified Unclassified Unclassified + + + 90* Orthornavirae sp.; XLV26731.1 (100%) N Y\nGhabrivirales sp. 1 Ghabrivirales Unclassified Unclassified + - + 90* Ghabrivirales sp.; XLV26802.1 (95%) N N\nGhabrivirales sp. 2 Ghabrivirales Unclassified Unclassified + - - 90* Ghabrivirales sp.; XLV26802.1 (100%) N N\nHedwig virus Elliovirales Peribunyaviridae Gryffinivirus + + - 90 Asum virus; QGA70944.1 (100%) N N\nIsta virus Picornavirales Iflaviridae Iflavirus + - + 90 (capsid protein) Ista virus; OZ251434.1 (99%) N Y\nJotan virus Picornavirales Iflaviridae Iflavirus + - + 90 (capsid protein) Jotan virus; UYL94332.1 (99%) N N\nJotan-like virus Picornavirales Iflaviridae Iflavirus + - + 90 (capsid protein) Culex Iflavi-like virus 1; WVL03087.1 (84%) Y N\nMarma virus Sobelivirales Solemoviridae Unclassified + - - 90* Marma virus; OZ251086 (100%) N Y\nMerida virus Mononegavirales Rhabdoviridae Merhavirus + - - 90 Merida virus; AWJ96718.1 (97%) N N\nTolivirales sp. 1 Tolivirales Unclassified Unclassified + - - 90* Sanya tombus-like virus 2; UHM27571.1 (31%) Y N\nTolivirales sp. 2 Tolivirales Unclassified Unclassified + - - 90* Sanxia water strider virus 14; APG76440.1 (43%) Y N\nUmatilla virus Reovirales Sedoreoviridae Orbivirus + - - 78 Umatilla virus; WZL41624.1 (99%) N N\nValmbacken virus Reovirales Sedoreoviridae Unclassified + - - 90 Valmbacken virus; WJJ55410.1 (100%) N N\nWuhan Mosquito virus 4 Articulavirales Orthomyxoviridae Quaranjavirus + - - 90* Wuhan Mosquito Virus 4;  OZ251991.1 (100%) N Y\nWuhan Mosquito virus 6 Articulavirales Orthomyxoviridae Quaranjavirus + + - 90* Wuhan Mosquito Virus 6; AJG39092.1 (98%) N N\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nArbovirus-related detections 331 \nBeyond insect-specific lineages, several viruses closely related to recognised 332 \narboviruses were also detected (Figures 2 and 4B). Hedwig virus (Family: 333 \nPeribunyaviridae; Genus: Gryffinivirus) was the most widespread, observed at 10 sites 334 \nacross southern England and Wales: Swansea, Newport, Bristol, Ipswich, Melton 335 \nMowbray, Newmarket, Slimbridge, Upper Stoke, and two London localities (Harlesden 336 \nand Walworth). In six of these detections, complete RdRp ORFs were recovered, each 337 \nshowing >98% amino acid identity to previously reported Hedwig virus sequences 338 \n(Figure 2B). Some clustered most closely with isolates from Germany, others with 339 \nviruses reported from Sweden or France, indicating close relationships to multiple 340 \nEuropean lineages.  341 \nUmatilla virus (Family: Reoviridae; Genus: Orbivirus) was found at three sites, including 342 \nSlimbridge, Newport and Plymouth. Representative sequences for the two sites 343 \nclustered with others obtained from birds caught in Germany during 2019 surveillance 344 \n(Figure 2A).  345 \nAtherstone virus (Family: Peribunyaviridae; Genus: Orthobunyavirus) was restricted to 346 \ntwo sites in Swindon and Cambridge (Figure 2C and 4B), with RdRp genes showing near 347 \nidentical amino acid identity to the virus reported in our previous study [37] (Accession: 348 \nOZ254907), and closely related to a partial sequence recently released from a detection 349 \nin France from 2015 (Accession: PV682945). 350 \nFor each of these viruses, all expected genome segments were recovered (except 351 \nsegment 3 of Umatilla virus) and co-occurred within single pools, confirming assembly 352 \nof near-complete genomes rather than partial detections (ENA accessions: Hedwig 353 \nvirus – 3 segments [OZ335791- OZ335793]; Umatilla virus – 9 segments [OZ335966, 354 \nOZ335967, OZ335972, OZ335978, OZ335979, OZ335986, OZ335988, OZ335991, 355 \nOZ367119]; Atherstone virus – 3 segments [OZ335505, OZ335506, OZ335605]). 356 \n 357 \n 358 \n 359 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\n 360 \n 361 \nFigure 2. Maximum-likelihood trees of the RNA-dependent RNA polymerase (RdRp) ORFs of (A) 362 \nUmatilla virus (Sedoreoviridae; Orbivirus) (B) Hedwig virus (Peribunyaviridae; Gryffinivirus) (C) 363 \nAtherstone virus (Peribunyaviridae; Orthobunyavirus). Scale bars represent the number of 364 \namino acid substitutions per site. Silhouettes represent host source. 365 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nVirus distribution patterns 366 \nVirus detections spanned a gradient from widespread to highly restricted taxa (Figure 3). 367 \nOnly three viruses, Daeseongdong virus 2, Wuhan mosquito virus 4, and 368 \nAlphamesonivirus fluvideense, were widespread, each detected at more than a third of 369 \nall sites and across all 10 ITL regions (Figures 3B and C). Sixteen viruses showed 370 \nintermediate distributions, occurring at 6–25 sites, including Chrysoviridae sp. (25 371 \nsites), Culex Negev-like virus 1 (16 sites), and Marma virus (18 sites). By contrast, the 372 \nmajority of taxa (22/41) were restricted, being found at four or fewer sites, with eight 373 \nobserved only once. While most singletons have not previously been reported in Culex 374 \n(e.g., Amalgaviridae sp., Culex Negev-like virus 3, Culex pipiens Tymo-like virus 1 and 375 \nCulex pipiens Tymovirales sp.), others such as Culex pipiens betapartitivirus 2, 376 \nAlmendravirus Chester and Valmbacken virus have been documented in earlier studies 377 \n[24,37,69], supporting their likely mosquito association despite low prevalence here. 378 \n 379 \nFigure 3. (A) Heatmap of virus reads detected in Culex spp. pools across 151 libraries from 93 380 \nsites. Number of sites (B) and ITL regions (C) each taxon was detected across. 381 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nAcross 312 taxa detections spanning 86 sites, six families (Mesoniviridae, 382 \nChrysoviridae, Orthomyxoviridae, Iflaviridae, Xinmoviridae, and Solemoviridae) 383 \naccounted for over half of all records (59%; Figure 4A). An additional 23% of detections 384 \nfell into the ‘Unclassified’ category, reflecting viruses that could not be placed within 385 \nestablished families. The majority of these undesignated detections reflected 386 \nDaeseongdong virus 2, which was widespread, being detected at 70 sites across all 10 387 \nITL regions (Figures 3B and C). Relative abundance profiles (read count) across ITL 388 \nregions (Figure 4C), as well as land type (Figure 4D) were also dominated by these same 389 \nfamilies. At the detection (presence/absence) level, no viral families or species differed 390 \nsignificantly in frequency between urban and rural sites (Fisher’s exact test, Table S1), 391 \nsuggesting no evidence of habitat-specific enrichment. 392 \n 393 \nFigure 4. (A) Geographic distribution (presence/absence) of viral family detections across 86 of 394 \n93 sites where at least one taxon was detected. (B) Distribution of putative arboviruses across 395 \nEngland and Wales. (C) Mean relative viral abundance (read counts) per site across the 10 ITL 396 \nregions surveyed. (D) Mean relative viral abundance (read counts) per site by coarse land type. 397 \nAsterisks denotes enriched taxa (rural vs urban).  398 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nIn contrast, abundance-based comparisons (relative viral read counts) identified five 399 \nfamilies that met the prevalence filter for inclusion (>10% of both urban and rural sites): 400 \nOrthomyxoviridae, Mesoniviridae, Iflaviridae, Xinmoviridae, and Chrysoviridae. Among 401 \nthese, only Mesoniviridae (urban-enriched, Deseq2 padj = 5.4 × 10⁻⁴) and Xinmoviridae 402 \n(rural-enriched, Deseq2 padj = 0.031) showed significant differences. 403 \nDiversity patterns 404 \nViral richness and Shannon diversity did not differ significantly between urban and rural 405 \nsites (Wilcoxon rank test, p > 0.05), with both measures showing similar ranges and 406 \ndispersion within groups (Figures 5A–C). In Cx. pipiens, median richness was 2.6 (IQR 407 \n1.9) in rural sites and 2.5 (IQR 1.5) in urban sites, while Shannon diversity was likewise 408 \nsimilar (rural: 0.34, IQR 0.65; urban: 0.32, IQR 0.62). For Cx. torrentium (n = 8) and Cx. 409 \nmolestus (n = 3), sample sizes were too limited for meaningful comparisons, though no 410 \nclear land-use effect was evident. 411 \nWithin Cx. pipiens, alpha diversity showed a significant negative association with 412 \nlatitude across rural sites for observed richness (Spearman’s ρ = –0.44, p = 0.0042; 413 \nlinear regression: R² = 0.182, p = 0.006), but not across urban sites (Spearman’s ρ = –414 \n0.062, p = 0.72; linear regression: R² = 0.005, p = 0.667). This relationship was not 415 \ndetected for Shannon diversity, indicating that the number of viral taxa declined with 416 \nincreasing latitude but community evenness remained stable (Figures 5D and E). No 417 \nassociations with longitude were observed (See supplemental data). 418 \nBeta diversity analysis (Figure 5F) based on Bray–Curtis dissimilarities revealed no 419 \nsignificant structuring of viral communities by land type (PERMANOVA: R² = 0.015, p = 420 \n0.264), but showed a borderline association with latitude (R² = 0.022, p = 0.051). 421 \nHomogeneity of multivariate dispersion was confirmed (PERMDISP; rural mean 0.651 ± 422 \n0.011 SE, urban 0.630 ± 0.016 SE; p > 0.05), indicating that the lack of PERMANOVA 423 \nsignificance reflected a true absence of compositional differences rather than unequal 424 \nwithin-group variance. These results were consistent with the NMDS ordination, which 425 \nshowed broad overlap of communities across land types (Figure 5G). 426 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\n 427 \nFigure 5. (A) Observed richness across Culex species (B) Observed richness stratified by 428 \nspecies and compared between urban and rural sites (C) Shannon diversity stratified by species 429 \nand compared between urban and rural sites (D) Association between latitude and observed 430 \nrichness in Cx. pipiens pipiens (solid line = statistical significance) (E) Association between 431 \nlatitude and Shannon diversity in Cx. pipiens pipiens (F) Principal coordinates analysis (PCoA) of 432 \nBray–Curtis dissimilarities in Cx. pipiens pipiens, with PERMANOVA testing effects of land type 433 \n(urban vs. rural) and latitude. (G) Non-metric multidimensional scaling (NMDS) ordination of 434 \nBray–Curtis dissimilarities in Cx. pipiens pipiens. 435 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nDiscussion 436 \nThis study represents the first comprehensive virome survey of UK Culex species, 437 \nidentifying 41 distinct viral taxa, among them three arbovirus-related lineages, including 438 \ntwo detected for the first time in the UK. Our dataset, collected in the same year as the 439 \nfirst UK WNV detection in Aedes vexans [12], found no evidence of WNV in Culex 440 \npopulations across any site, nor of USUV , the only mosquito-borne virus currently 441 \nconsidered established in the UK [70]. By contrast, the detection of other putative 442 \narboviruses highlights potential emerging threats, spanning a continuum from 443 \nneglected but increasingly reported (Umatilla virus) [71,72] to recently characterised 444 \nwith limited detection histories (Hedwig and Atherstone viruses) [37,72]. These results 445 \nillustrate the added value of high-throughput sequencing as a complementary 446 \napproach to existing UK surveillance, which has been primarily directed toward WNV 447 \nand USUV. 448 \nUmatilla virus (UMAV), an orbivirus in the Sedoreoviridae family, was first isolated in the 449 \n1960s from Culex spp. collected in the USA [73]. It has since been detected in Australia 450 \n[74], Japan [75], and Europe [71,72], and has re-emerged as a candidate pathogen in 451 \nbirds. In Germany, UMAV-positive blue tits were repeatedly reported with splenomegaly 452 \nconsistent with acute infection [72]. More strikingly, UMAV infection was confirmed in 453 \nmultiple deceased Cape penguins from a zoo, with one presenting with hepatitis and 454 \nhigh viral loads across liver, spleen, and kidney [71]. In addition, a serological survey 455 \nrevealed high exposure rates in free-living pheasants, indicating frequent infection [71], 456 \nsuggesting that some avian species may serve as amplifying hosts, whereas others may 457 \nbe more prone to severe pathology. 458 \nHedwig virus (HEDV), a species in the Gryffinivirus genus (Peribunyaviridae) was first 459 \nreported in Culex pipiens from France in 2015, and has since been detected in 460 \nmosquitoes in Sweden and Germany, as well as two birds (straw-necked ibis and 461 \nferruginous duck) [24,72]; of the necropsy reports available for these animals, 462 \npathological findings were inconsistent, leaving the pathogenic role of HEDV 463 \nunresolved. 464 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nUMAV was detected at two sites near the Severn Estuary, while HEDV was detected at 465 \nmultiple sites near both the Severn and Thames Estuaries. This geographic overlap 466 \nhighlights estuarine regions as potential entry points for these viruses, consistent with 467 \nproposed routes of historical incursions of vector-borne pathogens across Europe via 468 \nmigratory bird flyways [76–78]. 469 \nAnother member of the Peribunyaviridae, Atherstone virus, was first characterised in 470 \nour previous zoo-based study [37]. Shortly after, it was reported from archived Culex 471 \npools in southern France, originally screened following the detection of Umbre virus in 472 \npatient brain tissue [79]. Although Umbre virus was not identified in these mosquitoes, 473 \na partial sequence corresponding to Atherstone virus was recovered (reported as “Gili 474 \northobunyavirus”). Together with additional detections at sites in Cambridgeshire and 475 \nWiltshire from the present study, these data indicate that Atherstone virus is more 476 \nwidely distributed than initially recognised. Unlike UMAV and HEDV , which already have 477 \nvertebrate detections, Atherstone virus has so far been detected only in mosquitoes. 478 \nNevertheless, its placement within the Orthobunyavirus genus, which includes multiple 479 \nestablished arboviruses, and the presence of a vertebrate-specific virulence factor 480 \n(non-structural S protein) [37,80] support its classification as a putative arbovirus and a 481 \npriority candidate for studies on vector competence, host range, and potential health 482 \nsignificance. 483 \nBeyond arboviruses, a further 38 distinct viral taxa were detected, with only three 484 \nviruses detected at more than a third of sampling sites, indicating that most taxa were 485 \ngeographically restricted. A similar pattern was observed by Pan et al. [27], who 486 \nreported that just 27 of 393 viruses were present in more than 25% of individual 487 \nmosquitoes across China, likewise suggesting strong spatial structuring of mosquito 488 \nviromes. In contrast, some studies, often based on large mosquito pools and short 489 \ncontigs annotated at broad taxonomic ranks (e.g. family level or by lowest common 490 \nancestor methods) rather than species-level phylogenetic classification, have reported 491 \na more conserved core virome [81,82]. Such higher-level analyses, however, tend to 492 \noverestimate viral ubiquity by grouping genetically distinct species into broader 493 \ntaxonomic units, thereby masking underlying spatial and host-associated heterogeneity.  494 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nOf the most prevalent taxa, Alphamesonivirus fluvideense was particularly notable. 495 \nAlthough mesoniviruses are generally regarded as insect-specific [83], recent reports of 496 \nAlphamesonivirus sequences in horse lung and lymph node tissues associated with 497 \nrespiratory disease [84] raise questions about their broader host associations. The 498 \nsignificance of these vertebrate detections remains uncertain and may reflect rare 499 \nspillover events, but the widespread occurrence of this lineage in this study highlights 500 \nopportunities for exposure and underscores the value of including mesoniviruses in 501 \nsurveillance frameworks. 502 \nThe interpretation of rarer viruses poses further challenges, as some may reflect dietary 503 \nor environmental acquisition rather than true mosquito associations [85,86]. However, 504 \nmost singleton detections in this study involved viruses previously reported in Culex 505 \nviromes (e.g., Valmbacken virus) or belonging to established mosquito-specific lineages 506 \n(e.g., Negev-like viruses). Similarly, Chrysoviridae, Solemoviridae and Partitiviridae were 507 \nonce considered dietary contaminants but are now consistently recovered across 508 \nindependent Culex virome studies [23,24,81], indicating that they represent persistent 509 \nmosquito-associated lineages.  510 \nThe UK Culex virome showed little evidence of strong ecological structuring by coarse 511 \nland type. Frequency-based comparisons indicated no habitat-specific enrichment of 512 \nviral lineages, but abundance-based analyses suggested modest shifts, with 513 \nMesoniviridae more common in urban pools and Xinmoviridae more abundant in rural 514 \nones. These differences imply that while presence/absence patterns remain broadly 515 \nconsistent across habitats, virus abundances may still capture ecological contrasts 516 \nsuch as variation in larval environments or feeding behaviours [87,88]. A modest but 517 \nstatistically significant negative correlation between latitude and viral richness in Culex 518 \npipiens across rural sites was also observed, with higher diversity observed in southern 519 \nsampling locations. This pattern mirrors the broader latitudinal diversity gradients 520 \ndocumented in viral and microbial communities [89,90]. These trends have been 521 \nattributed to factors such as temperature-dependent insect activity and differences in 522 \nenvironmental viral stability [91]. In the case of mosquitoes, warmer temperatures in 523 \nsouthern regions may support longer activity periods and larger population sizes, 524 \nthereby increasing opportunities for viral transmission and maintenance. The absence 525 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nof this trend across our urban sites could reflect the urban heat island effect, which may 526 \nbuffer mosquitoes from broader latitudinal temperature differences [92,93]. 527 \nThe limited and inconsistent ecological structuring observed here highlights the 528 \nchallenges of incorporating virome data into arbovirus risk models. While metagenomic 529 \nsurveillance is clearly valuable for detecting circulating arboviruses, the heterogeneous 530 \ndistribution of ISVs, without evidence of a broad core virome or predictable structuring 531 \nby land type, makes it difficult to parameterise their potential modulatory effects. 532 \nWithout clearer understanding of where and when particular ISVs are likely to occur, 533 \ntheir influence on arbovirus transmission at a population level cannot be reliably 534 \nincorporated into predictive frameworks. 535 \nNonetheless, ISVs are increasingly considered as candidates for biological control [86]. 536 \nFor example, insect-specific flaviviruses have been shown to inhibit the replication of 537 \narbo-flaviviruses such as WNV and Dengue fever viruses [32,33]. However, the absence 538 \nof Insect-specific flaviviruses in our dataset is consistent with other European Culex 539 \nvirome investigations [23,24,69,81].  540 \nIn contrast, we detected several other ISVs of potential interest, including 541 \nBunyaviricetes members such as Culex bunyavirus 2, which has been reported 542 \npreviously from Culex populations [28,37,94], as well as four Tymovirales species 543 \n(Alsuviricetes), two of which are novel. Given their phylogenetic proximity to arboviruses 544 \nof concern in Europe, such as Rift Valley fever virus (Bunyaviricetes), as well as 545 \nchikungunya and Sindbis viruses (Alsuviricetes), these lineages represent logical 546 \ncandidates for targeted evaluation. Rift Valley fever virus has not shown local 547 \ntransmission in continental Europe but remains a priority for surveillance and 548 \npreparedness due to the risk of introduction and establishment through animal 549 \nmovements [95]. In contrast, chikungunya virus has already caused repeated 550 \nautochthonous outbreaks in southern Europe [14,96], while Sindbis virus is endemic in 551 \nparts of northern Europe, where it occasionally causes human infections [13]. 552 \n 553 \n 554 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nConclusions 555 \nAs arbovirus threats increase in temperate regions, agnostic virus screening 556 \napproaches should be seen as essential components of vector-borne disease 557 \npreparedness. This study exemplifies this with the detection of two known arboviruses 558 \npreviously undetected in the UK and a further putative arbovirus with unknown health 559 \nimpacts. Beyond arboviruses, the UK Culex virome appears heterogeneous, with limited 560 \nevidence of a conserved core or ecological structuring, aside from a modest latitudinal 561 \ngradient in richness across rural sites. This suggests that viral communities are shaped 562 \npredominantly by fine-scale or stochastic processes. Building on this national baseline, 563 \nlongitudinal sampling will be essential to capture temporal dynamics and evaluate 564 \nbroader virome stability. In parallel, integration of accumulating virome data with 565 \nvertebrate surveillance, including serology in birds, mammals and humans, will help 566 \nclarify host associations and refine arbovirus risk evaluation. 567 \nData availability  568 \nVirus sequences and raw reads are available at the European Nucleotide Archive under 569 \nproject accession number PRJEB98260. 570 \nAuthor contributions 571 \nMB, MSCB and JM secured funding for this project. JP, MSCB and ACD contributed to 572 \nthe conceptual development of the project. EW, RW, AGCV, JM, MB and MSCB co-573 \nordinated fieldwork. JP and EW conducted laboratory work. JP carried out bioinformatic 574 \nanalysis. JP, MSCB and ACD interpreted data. JP produced figures and drafts of the 575 \nmanuscript. All authors assisted in critical revision of the manuscript.  576 \n 577 \n 578 \n 579 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\nFunding 580 \nThis work was supported by the United Kingdom Research Innovation/Department for 581 \nEnvironment Food and Rural Affairs: Culex distribution, vector competence and threat 582 \nof transmission of arboviruses to humans and animals in the UK (BB/X018172/1). This 583 \nresearch was also partly funded by an HBLB Research Fellowship awarded to JP, as 584 \nwell as a BBSRC grant (BB/W002906/1) awarded to MSCB and MB.  585 \nConflicts of interest 586 \nThe authors declare that there are no conflicts of interest. 587 \nAcknowledgements 588 \nWe thank Agata Delnicka, Amelia Simpson, Anthony J. Abbott, Colin J. Johnston, Jude 589 \nMartin, Kendall Barlow, Eloise Aliski, Saffron Shiels, Sara Gandy, and Sarah M. 590 \nBiddlecombe for their invaluable assistance with mosquito field collections. We also 591 \nacknowledge the support of Richard Gregory and the Centre for Genomic Research 592 \n(CGR) at the University of Liverpool for providing access to computational resources 593 \nused in this research, as well as for the use of CGR’s sequencing facilities. 594 \nBibliography 595 \n[1] Laverdeur J, Amory H, Beckers P , Desmecht D, Francis F , Garigliany M-M, et al. West Nile 596 \nand Usutu viruses: current spreading and future threats in a warming northern Europe. 597 \nFrontiers in Virology 2025;5. https://doi.org/10.3389/fviro.2025.1544884. 598 \n[2] Logiudice J, Alberti M, Ciccarone A, Rossi B, Tiecco G, De Francesco MA, et al. 599 \nIntroduction of Vector-Borne Infections in Europe: Emerging and Re-Emerging Viral 600 \nPathogens with Potential Impact on One Health. 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It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\n[96] Fournier L, Durand GA, Cochet A, Brottet E, Fiet C, Mano Q, et al. Multiple early local 883 \ntransmissions of chikungunya virus, Mainland France, from May 2025. Euro Surveill 884 \n2025;30. https://doi.org/10.2807/1560-7917.ES.2025.30.32.2500545. 885 \n 886 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 11, 2025. ; https://doi.org/10.1101/2025.11.11.687861doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseperpetuity. 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