https://orcid.org/0000-0003-4090-0655
Figures
Abstract
Kissing bugs (Triatominae) are hematophagous insects and the principal vectors of Trypanosoma cruzi, the causative agent of Chagas disease. While their bacterial microbiomes have received considerable attention, the diversity of viruses associated with these insects remains poorly understood. To address this gap, we investigated the metavirome of five Triatominae species from the southern United States (Triatoma rubida, T. sanguisuga, T. gerstaeckeri, T. indictiva, and Hospesneotomae protracta), sampled in Texas, New Mexico, and Arizona. We sequenced 23 samples, including abdomen, gut and reproductive tissues from 13 field-collected individuals and assembled 41 viral operational taxonomic units (vOTUs), 40 of which are novel and together constitute 13 viral families, including Chuviridae, Arenaviridae, Orthomyxoviridae, Partitiviridae, Solemoviridae, Circoviridae, Rhabdoviridae, Microviridae, Xinmoviridae, Astroviridae, Narnaviridae, Tombusviridae, and the order Elliovirales. The vOTUs composition and abundance analysis examined variables including species, sex, tissue type, blood meal, and T. cruzi infection status, showing that metavirome diversity varied significantly among Triatominae species. Our findings demonstrate a species-specific metavirome and the presence of virus taxa linked to insects, plants, and vertebrates, highlighting the complex ecological interactions between viruses and triatomines. This study uncovers a diverse and largely novel set of metaviromes within North American Triatominae, providing a foundation for future research on virus-vector interactions.
Author summary
Kissing bugs are blood-feeding insects best known for transmitting Trypanosoma cruzi, the parasite that causes Chagas disease. Like most of other organisms, kissing bugs carry not only bacteria but also viruses as part of their natural microbial communities. These viruses, often harmless and sometimes potentially beneficial, can influence how insects develop, respond to infections, and interact with the pathogens they transmit. Yet, compared to bacterial microbiomes, the viral communities of kissing bugs have remained almost completely unexplored. In this study, we analyzed metaviromes present in digestive and reproductive tissues of five species of kissing bugs collected across the southern United States. Using high-throughput sequencing, we discovered 41 distinct viral OTUs nearly all of them previously unknown. These represent a wide range of viral families, including those typically associated with insects, plants, and vertebrates. We also found that each kissing bug species carries its own unique metavirome. By uncovering this hidden virome diversity, our work provides a foundation for understanding how viruses might shape the biology, immunity, and transmission capacity of these important vectors.
Citation: Habib I, Hernandez-Valencia JC, Martinu J, Novakova E (2026) Viral metagenome characterization reveals species-specific virome profiles in Triatominae populations from the southern United States. PLoS Negl Trop Dis 20(2): e0013576. https://doi.org/10.1371/journal.pntd.0013576
Editor: Adly M.M. Abd-Alla, International Atomic Energy Agency, AUSTRIA
Received: September 15, 2025; Accepted: January 13, 2026; Published: February 2, 2026
Copyright: © 2026 Habib et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting information files. Raw RNA-seq reads have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1279500. Viral genome assemblies have been deposited in GenBank under accession numbers PX139058 - PX139102.
Funding: This work was supported by the Czech Science Foundation (grant number 21-10185M to EN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Metagenomic and metatranscriptomic surveys in recent years have vastly expanded our understanding of the viromes associated with blood-feeding arthropods. These studies reveal that hematophagous vectors harbor a surprising diversity of viruses, ranging from well-known arboviruses to numerous insect-specific viruses (ISVs) that do not infect vertebrates [1]. While mosquitoes and ticks have received the most attention of vector virome research [2–5], kissing bugs (Triatominae) and their associated viruses have remained largely understudied. Despite the well-established role of Triatominae as vectors of Trypanosoma cruzi, the etiological agent of Chagas disease, current knowledge of their virome diversity, distribution, ecological dynamics, and host-virome interactions remains limited [6]. Until recently, only eight viruses had been described from Triatominae: the dicistrovirus Triatoma virus (TrV) and seven Rhodnius prolixus virus strains (RpV1-7). TrV, a non-enveloped positive-sense single-stranded RNA virus from the Dicistroviridae family, was first identified in Triatoma infestans populations in Argentina [7,8]. Subsequent studies demonstrated that TrV is the only known triatomine viral pathogen [9–12]. Due to its association with high nymphal mortality, delayed development, and impaired oogenesis, TrV has been proposed as a potential biological control agent for Chagas disease vectors [8,13]. In contrast, the phenotypic effects of the RpV1-7 viruses remain unknown. Discovered through transcriptome sequencing of Rhodnius prolixus tissues [14], these viruses are evolutionarily distinct from TrV and belong to the families Iflaviridae (RpV1, RpV2), Permutotetraviridae (RpV3, RpV4, RpV7), and Solemoviridae (RpV5, RpV6). Notably, both TrV and RpVs appear to be vertically transmitted to progeny [8,14], suggesting persistent infections within triatomine populations.
The first virome oriented surveys in Triatominae were conducted by Brito et al. [14] and Bourke et al. [15], who used RNA-seq data from public repositories to uncover a broader diversity of viruses associated with kissing bugs. Mining the datasets from 122 Triatominae specimens (eight species from six countries), the later study identified six distinct viruses, including the previously known RpV4-6 and widespread detection of TrV in nearly half of all screened individuals [15]. This work notably expanded TrV’s known host range, reporting its presence for the first time in Triatoma brasiliensis and Mepraia species. Additionally, two novel viruses were detected in kissing bugs: Meccus longipennis virus 1 and Drosophila melanogaster Nora virus. The former was identified in multiple T. infestans populations across Argentina, Brazil, Chile, and Peru, while the latter (a picorna-like virus previously known from fruit flies) was found in T. infestans in Argentina. These findings emphasize the vast, yet underutilized, potential of publicly available datasets for identifying both known and novel viruses, while also revealing significant gaps in our understanding of Triatominae-associated viromes.
The present metatranscriptomic study focuses on five Triatominae species native to the southern United States and provides a comprehensive analysis of the associated viral metagenomes. By characterizing viral communities in both gut and reproductive tissues of Triatoma rubida, Triatoma gerstaeckeri, Hospesneotomae protracta, Triatoma sanguisuga, and Triatoma indictiva, we aim to gain insight into tissue-specific viral diversity and transmission potential. In addition, we investigate how ecological and biological factors, such as T. cruzi infection status and the identity of recent blood meals, influence virome composition and structure across individuals and species. Identifying novel viruses associated with these medically relevant insects may reveal candidates for biological control, as seen with TrV adversely affecting triatomine development and survival.
Materials and methods
Sample set
The sample set was designed to include digestive and reproductive tracts from 13 individuals of fifth instars, males, and females representing five Triatominae species sampled in Arizona, New Mexico, and Texas in 2022. The individuals retrieved by active searches through vertebrate nests and burrows were morphologically determined to species, preserved in RNAlater solution and stored in -20°C before dissection. For two females, only whole abdomens (tissue sample extension “ABD”) could be processed because the abdominal incision made to allow RNAlater infiltration prevented reliable separation of individual tissues. For the remaining 11 individuals (5 females, 4 males, and 2 L5s), reproductive tissues and digestive tracts (excluding the crop) were dissected under a Nikon SMZ745T stereomicroscope and preserved in liquid nitrogen (S1 Table, tissue sample extension “GONADS” and “GUT”).
Total DNA/RNA extraction, Next-generation sequencing, and blood meal analysis
DNA and RNA templates from 24 tissue samples were isolated with AllPrep DNA/RNA Micro Kit (Qiagen) following the manufacturer´s protocol. The quality of RNA was checked with Agilent 2100 Bioanalyzer (Agilent Technologies), and the sample concentration was measured using Qubit RNA High Sensitivity Assay Kit (Thermo Fisher Scientific). The samples passing QC (n = 23, S1 Table) were used for cDNA libraries construction including ribosomal RNA (rRNA) depletion, and sequenced as 150 bp long PE reads on Illumina NovaSeq 6000 by Novogene (United Kingdom). To identify the dominant bloodmeal source, analysis was conducted with 12 DNA isolates derived from gut tissues (S2 Table). We used vertebrate universal primers, amplifying 215 bp long fragment of the 12S rRNA gene: L1085 (5’-CCCAAACTGGGATTAGATACCC-3’) and H1259 (5’-GTTTGCTGAAGATGGCGGTA-3’) (Kitano et al. 2007). PCR products were purified and sent for Sanger sequencing. Identity of the sequences was assessed based on the first BLASTN hits against the core_nt database.
Raw data processing, assembly, and viral metagenome identification
Quality control of 23 raw sequence datasets was performed using the Trimmomatic (v0.39) tool with default parameters to remove the low-quality reads. rRNA contamination was removed using ILLUMINACLIP by matching reads against a custom rRNA reference file compiled from identified overrepresented sequences [16]. Clean reads were used as input and de novo assembled into contigs using MetaSPAdes (v3.15.2) (K-mers = 21, 33, 55; other parameters were kept at default) [17]. All the assembled contigs were aligned to the protein version (v26.0) of the Reference Viral Database (RVDB) [18] using the DIAMOND (v v0.9.14.115) with BLASTX algorithm using the --very-sensitive option, E-value of 1e-3 and the default parameters [19]. Additionally, to detect additional potential RNA-dependent RNA Polymerase (RdRp) from RNA viruses, publicly available HMM profiles of different RdRp of eukaryotic RNA virus families were obtained from Pfam database v36.0 (Mononega- and Chuviridae-like: Mononeg_RNA_pol [PF00946], Reoviridae-like: RdRP_5 [PF07925], Flaviviridae-like: Flavi_NS5 [PF00972], Bunyavirales-like: Bunya_RdRp [PF04196], Narnaviridae-like: Mitovir_RNA_pol [PF05919], Nidovirales-like: RdRP_1 [PF00680], Tymovirales-like and Hepe-Virga-like: RdRP_2 [PF00978], Tombusviridae-like and Nodaviridae-like: RdRP_3 [PF00998], Toti-, Luteo-, and Sobemoviridae-like: RdRP_4 [PF02123], Viral_RdRp_C [PF17501], and Birnaviridae-like: Birna_RdRp [PF04197]), available through InterPro [20,21]. From all the retrieved profile HMMs a combined HMM profile database was built using hmmbuild from the HMMER suite [22]. The contigs were translated to six possible open reading frames (ORFs) obtained from the online VirtualRibosome tool (https://services.healthtech.dtu.dk/services/VirtualRibosome-2.0/) [23], and the amino acid (aa) sequences were used as an input for hmmsearch. To avoid false positives, the contigs with virus hits in RVDB and HMM were aligned against the NCBI’s non-redundant (nr) protein database via NCBI-BLAST with an E-value of 1e-5 and the default parameters [24]. Furthermore, the non-redundant contigs greater than 300 bp (nt) were selected for functional and structural annotation, and phylogenetic analyses. The virus sequences were taxonomically assigned following the guidelines for sequence-based taxonomy provided by the International Committee on Taxonomy of Viruses (ICTV) [25]. The newly discovered virus sequences were named based on the genus or subfamily and the Triatominae species, or the abbreviation “spp.” if found in multiple species, followed by the standard virus taxon according to their taxonomic classification.
Molecular confirmation of Triatominae species and their phylogenetic background for the virome analyses
Molecular confirmation of the involved species, especially T. sanguisuga and T. indictiva was performed by recovering 8 mitochondrial genes, including, Cytochrome C Oxidase I (COXI), Cytochrome C Oxidase II (COXII), Cytochrome C Oxidase III (COXIII), NADH dehydrogenase subunit 1 (ND1), NADH dehydrogenase subunit 4 (ND4), NADH dehydrogenase subunit 5 (ND5), cytochrome b (cytb) and 16S ribosomal RNA (16S rRNA) sequences, from each meta-assembly. The gene sequences were aligned separately using MAFFT algorithm implemented in Geneious Prime v.2022.0.2 (http://www.geneious.com/) and then concatenated into the final matrix containing 9032 nucleotides. The phylogenetic analysis was conducted with a maximum likelihood approach using PhyML. Bootstrap analysis was run with 100 iterations.
Trypanosoma cruzi meta-assembly screening
To investigate viral metagenome diversity in the context of Trypanosoma cruzi infection, we explored our data to identify infected and uninfected Triatoma and Hospesneotomae individuals. All the assembled contigs were first subjected to BLASTN searches against the custom database created from all available T. cruzi genomes from TriTryDB (https://tritrypdb.org/) [26]. Identity of all contigs producing an alignment of 200 bp and longer in the first search was confirmed by BLASTN against NCBI’s nt database (S1 Table).
Phylogenetic analysis and annotation of viral sequences
For structural and functional annotation of viral sequences, the amino acid sequence of identified ORFs was queried against the Protein Data Bank (PDB) database using HHpred method [27]. For the phylogenetic analysis of the assembled virus sequences, the amino acid sequences of the RdRp were mainly used since they are highly conserved in nature. Where available, we also used the nucleocapsid, and capsid sequences. The datasets were assembled from all BLASTP hits with more than 30% identity found in the nr database. The source organism and geographic metadata for all the hits were retrieved using the NCBI Entrez-direct tool (v19.4) [28] with a custom bash script and added to the header of each FASTA file. To reconstruct phylogenetic relationships among identified virus sequences, all the datasets were aligned using MAFFT under automatic settings [29]. Aligned sequences were trimmed using trimAl v1.4 using the -gappyout algorithm to remove poorly aligned positions [30]. Maximum-likelihood trees were inferred in IQ-TREEE2, where ModelFinder Plus with partition merging (MFP+MERGE) selected the optimal amino-acid substitution model [31]. Node support was evaluated with 1000 ultrafast bootstrap replicates and 1000 SH-aLRT tests.
Viral metagenome diversity and abundance in Triatominae
Diversity analyses were conducted with an abundance-based matrix derived from 23 tissue samples, built using read counts assigned to identified viral non-redundant contigs, hereafter referred to as viral Operational Taxonomic Units (vOTUs). The read counts were obtained by mapping quality-filtered reads to the vOTUs collection through the BBmap tool (v39.01). To avoid false positives, we used a minimum threshold of 99% nucleotide identity for read mapping and >75% read coverage across the entire length of each vOTU [32]. Subsequently, the abundance matrix was normalized based on sequence length and library size (quality-filtered reads per sample). This normalization was estimated as Reads Per Kilobase per Million mapped reads (RPKM).
The diversity analyses considered four factors: 1) species (T. rubida, T. sanguisuga, T. indictiva, T. gersteackeri, and H. protracta), 2) tissue type (GUT, GONADS and ABD), 3) geographical location (Arizona, New Mexico, and Texas), and 4) sex (male and female). Alpha diversity was evaluated using the Richness (S), Shannon-Weaver (H), Simpson (1-D), and Evenness (E) indices, while Beta diversity was calculated using Bray-Curtis (BC) and Euclidean distances. Statistical differences between defined groups were determined through analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA). To identify the vOTUs contributing the most to the dissimilarity between groups, similarity percentages analysis (SIMPER) was employed. All diversity indices and statistical tests were calculated using R [33] with the metagenome-Seq [34], Vegan [35], and ggplot2 [36] packages, as well as PAST software (version 4) [37]. To visualise overlap among viral OTUs detected in the studied Triatominae, we first transformed the vOTU list (S3 Table) into a binary presence-absence matrix with a custom-made R script. The binary matrix was passed to the UpSetR package (v1.4.0) [38] to create an UpSet plot showing the unique and shared vOTUs across Triatominae species.
Results
Sequencing of 23 Triatominae tissue samples generated a total read length of 277 Gb. Following quality control, the reads were de novo assembled into 6,748,633 contigs. Viral RNA constituted a mean of 0.307% of the total RNA per sample (post rRNA removal), with an interquartile range (IQR) of 0.019%-0.848%. Detailed sequencing and assembly metrics for each tissue sample are presented in (S4 Table). For ten Triatominae individuals, dominant blood meal analysis identified five different hosts, including wood rat, opossum, toads (likely Incilius nebulifer), and human (S2 Table). While we cannot reliably exclude the possibility of human DNA contamination during the collection and dissection process, the results unequivocally show that all analyzed individuals of T. rubida and H. protracta fed on Neotoma albigula, whereas some T. indictiva and T. gerstaeckeri utilized toads and opossums as blood meal sources.
Triatominae virome composition and virus sequences annotation
The de novo assembly revealed 41 unique vOTUs, which included 40 complete or partial genomes of novel viruses. Most of the Triatominae virome consists of complete or partially complete genomes of single-stranded RNA (ssRNA) viruses. Among these, six negative-sense ssRNA can be reliably assigned to the families Rhabdoviridae, Chuviridae, Arenaviridae, Xinmoviridae, and Orthomyxoviridae, and the order Elliovirales (Fig 1). The positive-sense ssRNA viruses detected belonged to the families Virgaviridae, Benyviridae, Tombusviridae, Solemoviridae, Astroviridae, and Narnaviridae. Additionally, a single double-stranded RNA (dsRNA) virus from the family Partitiviridae and two single-stranded DNA (ssDNA) viruses from the families Circoviridae and Microviridae were detected. All five Triatominae species harbor a diverse virome. We identified three to ten viral families, with T. indictiva and T. sanguisuga harboring the highest diversity (ten and eight families, respectively). The viral diversity at the individual sample level includes a median of four vOTUs, with an interquartile range (IQR) of four and a maximum of eleven vOTUs.
The schematic phylogeny represents Maximum Likelihood tree derived from eight mitochondrial genes with collapsed species/population clusters (S1 Fig). The abbreviations in parentheses following TX refer to two nearby sampling localities in Texas (detailed in S1 Table). The numbers analyzed per branch designate individuals in blue and tissue samples in red. The scale bar indicates the number of identified viral families and orders per branch. Map base layers: 1) Ecoregions: https://www.epa.gov/eco-research/ecoregions-north-america U.S. 2) Administrative boundaries: https://www.geoboundaries.org.
Negative-sense, single-stranded RNA metavirome
A total of 17 vOTUs associated with negative-sense single-stranded RNA (ssRNA-) viruses were identified across the Triatominae tissue samples (Fig 2 and S3 Table). Two vOTUs from the Arenaviridae (Fig 2A) family detected in H. protracta and T. rubida clustered together with Big Brushy Tank Virus (BBTV) (ACL36816.1) in the phylogeny. BBTV was previously identified in the white-throated woodrat, Neotoma albigula, from Arizona, suggesting ecological link between these viruses, reservoir hosts and triatomine vectors in this region. Seven vOTUs were detected within the Chuviridae (Fig 2B) family. One vOTU from T. sanguisuga comprised a complete chuvirus genome and clustered as a sister taxon to Guiyang Chuvirus 1 (UHK03046.1), previously described from the paddy ear head bug (Cletus punctiger) in China. Six distinct vOTUs were identified in T. indictiva, T. sanguisuga, and T. gerstaeckeri. Phylogenetic comparison showed that these vOTUs are most closely related to Sanya Chuvirus 2 (UHK03098.1), detected in a brown planthopper Nilaparvata lugens in China. In addition, we identified two vOTUs, corresponding to the full-length RdRp segments of an Elliovirales (Fig 2C) genome. Both vOTUs found in T. sanguisuga and T. gersteackeri shared the highest similarity with the Orius laevigatus bunyavirus 2 (PP908622.1; S3 Table). The phylogenetic analysis places them as sister taxa to a clade of Orthohantaviruses isolated from various rodents. Within the Orthomyxoviridae (Fig 2D–2E) family, we assembled two vOTUs corresponding to partial polymerase and nucleocapsid segments. The first, detected in both H. protracta and T. indictiva samples, with the closest relative Halyomorpha halys orthomyxo-like virus 1 (URQ09140.1). The phylogenetic analysis placed the virus within hemipteran and neuropteran orthomyxoviruses. Similar phylogenetic placement was recovered for the second vOTU found exclusively in H. protracta, which showed the highest similarity to Hemipteran Orthomyxo-related Virus OKIAV191 (QMP82198.1), previously isolated from hawthorn shield bug Acanthosoma haemorrhoidale in Germany.
Maximum-likelihood trees were inferred from RdRp, Polymerase, and Nucleocapsid protein sequences of Xinmoviridae, Arenaviridae, Chuviridae, Rhabdoviridae, Elliovirales, and Orthomyxoviridae. Newly identified virus sequences in bold and support values evaluated with 1,000 ultrafast bootstrap replicates and 1,000 SH-aLRT tests are shown under nodes. Schematic diagrams depict the genomic architecture of representative viruses, with the hatched regions indicating the part of virome obtained in this study.
In the Rhabdoviridae (Fig 2F) family, three vOTUs were assembled from T. indictiva, T. sanguisuga, and T. rubida. Two vOTUs corresponded to RdRp-encoding segments closely related to Sanya Conocephalus Maculatus Rhabdovirus 1 (UHK03110.1), originally reported from bush crickets in rice fields in China, and one vOTU to Blattodean Rhabdo-related Virus OKIAV14 (QMP82336.1), previously detected in red-headed cockroaches from Germany. Finally, in the Xinmoviridae (Fig 2G) family, partial RdRp segments were assembled from T. gerstaeckeri and T. rubida samples showing similarity to Hangzhou Cletus punctiger Xinmovirus 1 (UHK03158.1), a virus originally characterized from rice stink bug Cletus punctiger in China.
Positive-sense, single-stranded RNA metavirome
A total of 15 vOTUs associated with positive-sense single-stranded RNA (ssRNA+) viruses were identified (Fig 3 and S3 Table). A single vOTU associated with the partial genome of the family Astroviridae (Fig 3A) was assembled from T. indictiva. This vOTU showed similarity to Flumine Astrovirus 3 (UQB76172.1), which was sampled from a natural water source in New Zealand. However, although the phylogenetic analysis placed the vOTU within the “Flumine astrovirus cluster”, its position within the tree remained unclear due to low support (Fig 3A).
Maximum-likelihood trees were reconstructed from RdRp protein sequences of Virgaviridae, Benyviridae, Solemoviridae, Astroviridae, Tombusviridae, and Narnaviridae. Node support was evaluated with 1,000 ultrafast bootstrap replicates and 1,000 SH-aLRT tests, and viruses identified in this study are highlighted in bold. Schematic diagrams represent the genomic architecture of each virus family, with the hatched regions indicating the part of virome obtained in this study.
Four vOTUs were identified in the family Benyviridae (Fig 3B), with two from T. gerstaeckeri and two from H. protracta, all clustering with Sanya Benyvirus 1 (UHK03121.1), previously isolated from Sesamia inferens in China. We assembled two complete Narnaviridae (Fig 3C) genomes, from T. indictiva. One vOTU showed sequence identity and clustered with Serbia Narna-like Virus 3 (QNS17454.1), found in Culex pipiens from Serbia. The other full segment showed nucleotide similarity to Wuhan spider virus 7 (NC_033702.1), however the phylogenetic analysis placed this vOTU with Kummerowia striata ourmiavirus (QKI29250.1) with low support values. Two unrelated vOTUs corresponding to complete genomes from the family Solemoviridae (Fig 3D) were also identified. One vOTU, from T. indictiva, contained a fully assembled RdRp sequence showing high sequence identity Atrato Sobemo-like Virus 2 (QHA33888.1), previously identified in Culex sp. from Colombia. The second vOTU is related to both Rhodnius prolixus Virus 5 and 6 (QYC92635.1, QYC92637.1), found previously in ovaries of Rhodnius prolixus from Brazil.
Additionally, in both T. sanguisuga and T. indictiva, a unique vOTU related to the family Tombusviridae (Fig 3E) was identified. This vOTU corresponded to a complete genome that showed similarity to Hemipteran Tombus-related Virus (QTJ63611.1) from elongated-grass bugs, however its position within the topology was not well supported. Finally, five vOTUs were identified as Virgaviridae (Fig 3F), which include both complete and partial genomes. Three vOTUs recovered from T. gersteackeri exhibited similarity to Xiangshan Martelli-like Virus 3 (UDL14011.1) and Xiangshan martelli-like virus 2 (UDL14009.1), previously identified from an insect collected in China. The remaining two vOTUs were recovered from T. sanguisuga and T. indictiva and showed similarity to the Pedersore Virga-like Virus (UYL94368.1) and Ginka Virga-like Virus (UYE93845.1), which were identified in Ochlerotatus communis in Sweden and Finland. Phylogenetically, all these vOTUs create a monophyletic cluster closely related to the viruses mentioned above (Fig 3F).
Single-stranded DNA metavirome
Five vOTUs were identified, belonging to two families of ssDNA viruses, Circoviridae and Microviridae (Fig 4 and S3 Table). Four vOTUs were assigned to the Circoviridae (Fig 4A–4B) family. Three of these vOTUs detected in T. rubida, T. sanguisuga, and T. indictiva, showed high similarity to a capsid (WGL41060.1) and replicase segments (XNO10979.1) of Circovirus sp., which were sampled from the feces of Neophema chrysogaster in Australia and Ixodes ovatus from China. The additional vOTU was found in T. rubida and T. indictiva. It showed similarity to Cressdnaviricota sp. (AXH74253.1), however its phylogenetic position remained unstable (Fig 4B). The Microviridae (Fig 4C) family was exclusively detected in T. rubida and was found to be related to Microvirus sp. (WNK12593.1), sampled from the New Zealand mud snail.
Maximum-likelihood trees were reconstructed from RdRp protein sequences of Partitiviridae, Capsid protein of Microviridae and Circoviridae, and replication-associated protein of Circoviridae. Identified viruses in this study are highlighted in bold letters. Node support was evaluated with 1,000 ultrafast bootstrap replicates and 1,000 SH-aLRT tests and is shown in branches. Schematic diagrams represent the genomic architecture of each virus family, with the shaded regions indicating the sequence areas successfully recovered in this study.
Double-stranded RNA metavirome
Only one family of double-stranded RNA viruses, Partitiviridae (Fig 4D), was identified in the Triatominae samples. Partial genome segments were assembled into four distinct vOTUs from the species T. sanguisuga, T. indictiva, and H. protracta (Fig 4 and S3 Table). Three of these vOTUs, exhibited high similarity to Hubei Partiti-like Virus 10 (APG78227.1) and Hubei Partiti-like Virus 11 (YP_009329875.1) both the viruses were previously reported in dragonflies from China. The fourth vOTU, detected both in T. sanguisuga, and T. indictiva, was closely related to Hangzhou partitivirus 1 (UHK03007.1), reported in dragonflies found in rice fields in China.
Geographic specificity of the Triatominae metavirome
Among the 41 vOTUs identified in this study, 26 were found exclusively in Texas, while five were unique to Arizona. In Arizona, we found vOTUs belonging exclusively to the Arenaviridae and Microviridae families detected in T. rubida and H. protracta. Whereas, in Texas, vOTUs exclusively belonged to the order Elliovirales and the families Chuviridae, Tombusviridae, Virgaviridae, Astroviridae, Solemoviridae, and Narna-related viruses. Notably, two vOTUs were identified in triatomines from three states, corresponding to ssDNA viruses of the Circoviridae family. Some vOTUs showed high relative abundance in various samples, particularly those related to Solemoviridae, and Elliovirales (S3 Table and S2 Fig).
Although 31 of the 41 vOTUs were geographically specific, alpha diversity indices, as measured by the Shannon diversity index, showed slight variation among specimens from the three states (mean Shannon index = 0.675, SD = 0.398), but no statistically significant differences were observed (Kruskal-Wallis test, p > 0.05). Similarly, richness and evenness metrics did not differ significantly between states (Kruskal-Wallis test, p > 0.05), indicating a broadly uniform distribution of alpha diversity indices across geographical locations. In contrast, beta diversity analyses revealed some geographic structuring. Bray-Curtis-based Non-metric multidimensional scaling (NMDS) and Euclidean-based heatmaps suggested distinct patterns in the composition and abundance of vOTUs by state. These observed groupings were statistically significant (PERMANOVA test, p = 0.003, R² = 0.171) (S2 Fig).
Species-specific metavirome profile in triatomines
Considering that some Triatominae were collected exclusively in Texas and Arizona, and the observed geographic patterns might also reflect species effect in shaping the virome, we further investigated whether vOTUs shared across locations exhibited species or lineage-specific patterns. Out of the 41 vOTUs identified, 24 were detected exclusively in a single species: nine in T. gerstaeckeri, seven in T. indictiva, three each in T. rubida and H. protracta, and two in T. sanguisuga. The remaining 17 vOTUs were not species-specific and were shared among different species. None of these vOTUs were found in all five species. Specifically, eight vOTUs were shared between T. indictiva and T. sanguisuga; two of these vOTUs, belonging to the families Circoviridae and Partitiviridae, were shared with T. rubida and H. protracta, respectively. The other non-species-specific vOTUs were exclusively shared between pairs of species: T. rubida had a unique vOTU shared with T. indictiva, T. sanguisuga, and T. gerstaeckeri. Similarly, T. indictiva and H. protracta shared one vOTU, while T. gerstaeckeri and T. sanguisuga had two vOTUs in common (Fig 5).
A) NMDS based on Bray-Curtis distances, showing clustering patterns by Triatominae species (Stress = 0.097, PERMANOVA, p < 0.001; R² = 0.502). B) Shannon diversity index of vOTUs by Triatominae species (Kruskal-Wallis test, p = 0.399). C) Venn diagram displaying the number of shared vOTUs among Triatominae species. D) Heatmap based on normalized read counts of vOTUs and their Euclidean-based clustering among Triatominae species.
The Shannon diversity index of vOTUs revealed no significant differences among Triatominae species (Kruskal-Wallis test, p > 0.05). In contrast, significant differences were observed in vOTUs richness and evenness across the five Triatominae species (Kruskal-Wallis test, p < 0.05). Beta diversity analyses revealed distinct clustering patterns in vOTUs composition and abundance among species, with statistically significant groupings (PERMANOVA test, p = 0.001, R² = 0.502; ANOSIM test, p = 0.0001, R = 0.713) (Fig 5). Notably, samples from different Triatominae species generally clustered distinctly, and the virome of T. indictiva exhibited the highest diversity of vOTUs and clustered more distant from the virome of other species, aligning more closely with T. sanguisuga, as these two species shared the most vOTUs.
The SIMPER analysis showed that a group of six vOTUs explained more than 74% of the dissimilarity in the virome composition among the species, highlighting the contribution of the species-specific vOTUs. Solemo_vOTU25, unique to T. indictiva, was the main contributor, explaining 26.8% of the dissimilarity. It was followed by Solemo_vOTU26 (14.7%), found exclusively in T. sanguisuga. Additionally, Rhabdo_vOTU41 was present only in T. rubida, while Arena_vOTU15 was unique to H. protracta. Narna_vOTU24, contributing 6.3% to the dissimilarity, was also found only in T. indictiva, and Beny_vOTU19, with a contribution of 3%, was unique to T. gerstaeckeri. Furthermore, the Ellio_vOTU13 and Ellio_vOTU14 contributed an additional 11.37% and 7.89%, respectively, and were more abundant in T. sanguisuga and T. gerstaeckeri (S5 Table).
Sex, tissue, and T. cruzi infection status determinants
A uniform distribution of alpha viral diversity indices was observed across tissue type, sex, and T. cruzi infection status. Shannon, richness, and evenness indices did not show statistically significant differences among these factors (Kruskal-Wallis tests, p > 0.05). L5 specimens exhibited a higher Shannon index (Mean Shannon index = 1.18, SD = 0.163) compared to males (Mean Shannon index = 0.548, SD = 0.408). The Beta diversity analyses revealed no distinct clustering patterns in vOTU composition or abundance based on sex, tissue type, or T. cruzi infection status (PERMANOVA test, p > 0.05; ANOSIM test, p > 0.05) (S2 Fig).
Remarkably, 24 of the vOTUs identified in this study were found in reproductive tissue. Additionally, two vOTUs from T. cruzi-positive samples, which belong to the viral families Orthomyxoviridae and Chuviridae, were not exclusive to the infected individuals.
Discussion
Our study advances the field by characterizing the virome of five Triatominae species from natural populations in the southern United States: Triatoma rubida, Hospesneotomae protracta, Triatoma indictiva, Triatoma sanguisuga, and Triatoma gerstaeckeri. We identified a diverse range of viral metagenomes, including negative- and positive-sense ssRNA, dsRNA, and small circular ssDNA viruses from various families. Our findings suggest that viral composition and abundance are primarily shaped by Triatominae species identity, highlighting a species-specific viral metagenome profile.
A significant proportion of the identified sequences belonged to Chuviridae, Virgaviridae, Benyviridae, Partitiviridae, and Rhabdoviridae. While some members of these families infect non-arthropod hosts, the phylogenetic analyses showed that most viral sequences clustered within clades of previously described arthropod-associated viruses, reinforcing their specificity to insects. Additionally, these viral taxa have been consistently reported in insect viromes [2,39,40] and, in some cases, as endogenous viral elements [41–44], further supporting their specificity to arthropods.
Interestingly, viral sequences from families primarily associated with plant infections were also detected. Solemoviruses, in particular, have been repeatedly identified in triatomines. Rhodnius prolixus virus 5 (RpV5) and Rhodnius prolixus virus 6 (RpV6), initially reported in R. prolixus [14], have since been found in Rhodnius robustus in French Guiana and Rhodnius neglectus in Brazil [15]. This study expands the known host range of Solemoviridae by identifying Triatoma indictiva solemoviridae vOTU25 and Triatoma sanguisuga solemoviridae vOTU26. The recurring presence of these viruses in multiple triatomine species across different regions suggests a widespread distribution throughout the Americas. Additionally, we identified several plant-virus-related sequences, including Triatoma spp. tombusviridae vOTU22 and Benyviruses (Beny_vOTU18-21) in T. gerstaeckeri and H. protracta. While Benyviridae members are primarily associated with plant infections [45], their detection in insects has been documented [46–48], though their role in non-plant hosts remains unclear [49]. The presence of plant-associated viruses in Triatominae metaviromes may appear unexpected, however, it is not implausible. Ability of triatomines to ingest plant-derived sugars have been previously recorded, and they retain functional α-amylase genes necessary for starch digestion [50]. Although our study does not evaluate whether triatomines can act as vectors of plant viruses, this possibility cannot be excluded, particularly given that host accessibility strongly shapes kissing-bug feeding patterns and previous reports indicate occasional plant feeding in Triatominae [50,51].
Vertebrate-associated viruses within the family Arenaviridae were also identified, specifically Hospesneotomae protracta arenaviridae vOTU15 and Triatoma rubida arenaviridae vOTU16. These viruses are phylogenetically related to BBTV, previously isolated from Neotoma albigula in Arizona [52,53]. BBTV belongs to the genus Mammarenavirus, which includes 51 species primarily associated with rodents [54]. Notably, these arenaviruses were detected exclusively in T. rubida and H. protracta from Arizona. Consistent with these findings, we detected Neotoma blood in all analyzed individuals of T. rubida and H. protracta, further supporting the association between arenavirus sequences and rodent-derived blood meals. The presence of Arenaviridae vOTUs in the intestinal tract suggests that triatomines may act as mechanical carriers, dispersing viral particles through feces when interacting with rodent hosts. However, it remains unclear whether triatomines contribute to the natural transmission cycles of Mammarenavirus, or whether these findings simply reflect transient viral passage. Further investigation is needed to elucidate the role of triatomines in Mammarenavirus epidemiology.
In addition to RNA virome, we identified small circular DNA viruses from Circoviridae and Microviridae family. Circovirus sequences were phylogenetically related to viruses previously found in environmental samples and insects. Although most known Circoviridae members infect vertebrates [55] Cyclovirus species have also been identified in arthropods [56]. Their presence in hematophagous insects may be linked to the ingestion of viremic blood [57]. Additionally, circoviruses have been detected circulating in vertebrates from the study region [58,59], which could explain their presence in triatomines. In contrast, Microviridae are bacteriophages, likely representing components of the bacterial microbiome within triatomines. These bacteriophages have been consistently detected in insect microbiomes [60,61], including species from regions examined in this study [62].
Analysis of viral diversity and abundance revealed significant differences in vOTU richness and evenness among the five Triatominae species. Beta diversity analyses consistently showed species-specific clustering patterns, while sex and tissue type had minimal influence. These findings align with previous studies on mosquitoes and ticks, where virome richness and diversity primarily vary at the species level [3,63–68]. Beyond species identity, host interactions may also play a role in shaping virome composition in Triatominae, as observed in other hematophagous arthropods such as mosquitoes and ticks, where host associations significantly influence virome structure [3,66]. Here, the detection of viral sequences from the families Solemoviridae and Arenaviridae suggests that host interactions contribute to virome composition in Triatominae. Notably, Triatoma indictiva solemoviridae vOTU25 and Triatoma sanguisuga solemoviridae vOTU26 were identified as key drivers of differences in viral abundance and composition among the Triatominae species.
Another factor known to influence insect virome composition is geography [5,66,69]. Our analysis revealed that although 31 of the 41 vOTUs were geographically specific, beta diversity analyses identified significant geographic structuring among specimens from Arizona, Texas, and New Mexico. Nevertheless, since some Triatominae species were collected exclusively in Texas and Arizona, the observed geographic patterns may, in part, reflect the influence of Triatominae species identity on virome composition. These findings suggest that while overall viral richness and evenness remain relatively stable across regions, differences in virome composition may primarily be shaped by species identity, as mentioned earlier, without ruling out the complex interplay of localized environmental factors, host distributions, and ecological interactions. Future research should aim to disentangle the relative contributions of these factors, refining our understanding of virus-host dynamics in Triatominae.
Finally, a key challenge in viral metagenome studies is differentiating actively replicating viruses from endogenous viral elements (EVEs) [70]. EVEs arise from viral insertions into host genomes, complicating their distinction from exogenous viruses [71]. Although complete RNA virus genomes can occasionally integrate into a host genome, such events are relatively rare. EVEs are typically fragmented viral sequences with disrupted ORFs [72]. In our study, however, most of the viral sequences we identified are full-length or nearly full-length coding regions of RNA viral genomes. This suggests that these sequences are unlikely to represent EVEs expressed by the host. In addition to challenges related to EVEs, our study is constrained by the relatively small sample size, which may limit the generalizability of our findings, particularly regarding the effects of geographic variation on viral abundance and composition in triatomine natural populations. Overcoming these limitations will require expanded sampling efforts together with complementary approaches, such as whole-genome and small RNA sequencing, to assess viral replication and host antiviral responses [65,73]. Integrating these methodologies in future work will improve the distinction between EVEs and actively replicating viruses in Triatominae and provide deeper insight into virus–host interactions. Moreover, broader sampling across the geographic and ecological ranges of triatomine species will be essential for any robust conclusions on the factors that shape viral diversity in these important disease vectors.
Conclusion
This study offers the most comprehensive characterization of Triatominae viromes to date in the United States, significantly advancing our understanding of viral diversity, species specificity, and potential ecological interactions in these vectors. We reveal species-specific virome patterns and show that Triatominae identity is a dominant factor shaping viral composition. The detection of viruses associated with insects, plants, and vertebrates highlights the intricate nature of viral interactions within these hematophagous hosts. Given this complexity, together with the limited number of species and individuals analyzed so far, further research will be essential to explore the functional implications of these viral components and their potential impact on Triatominae ecology and vector competence.
Supporting information
S1 Table. Sample metadata and T. cruzi meta-assembly screening.
Summary of 23 Triatominae individuals with species, sex, geographic location, tissue type, QC, and T. cruzi detection.
https://doi.org/10.1371/journal.pntd.0013576.s001
(PDF)
S2 Table. Blood meal analysis of Triatominae gut tissues.
Twelve DNA isolates from gut samples analysed using vertebrate 12S rRNA gene sequences to identify blood meal sources.
https://doi.org/10.1371/journal.pntd.0013576.s002
(PDF)
S3 Table. Viral operational taxonomic units (vOTUs).
List of vOTUs detected in Triatominae natural populations from the southern United States.
https://doi.org/10.1371/journal.pntd.0013576.s003
(PDF)
S4 Table. Sequencing metrics.
Summary of raw sequencing reads, reads retained after quality control, total number of assembled contigs, and number of viral reads per sample.
https://doi.org/10.1371/journal.pntd.0013576.s004
(PDF)
S5 Table. SIMPER analysis.
Contribution of each Viral Operational Taxonomic Units (vOTUs) to the Bray-Curtis dissimilarities observed in Triatominae natural populations between species (H. protracta, T. rubida, T. indictiva, T. sanguisuga, T. gersteackeri).
https://doi.org/10.1371/journal.pntd.0013576.s005
(PDF)
S1 Fig. Phylogeny of sampled Triatominae.
Maximum-likelihood tree based on eight mitochondrial genes retrieved from each tissue sample analyzed in this study which correspond to five species (Triatoma rubida, Hospesneotomae protracta, Triatoma gerstaeckeri, Triatoma sanguisuga, and Triatoma indictiva). Multiple libraries from the same individual are shown as an internal quality-control measure to confirm consistent mitochondrial signal and detect potential issues such as sample mislabeling or multiplexing errors. Node labels indicate bootstrap support values (100 replicates).
https://doi.org/10.1371/journal.pntd.0013576.s006
(PDF)
S2 Fig. Alpha and beta diversity of vOTUs in natural populations of Triatoma.
A) Non-metric multidimensional scaling (NMDS) based on Bray-Curtis distances, showing clustering patterns by study location (PERMANOVA, p = 0.003; R² = 0.171), sex (PERMANOVA, p = 0.426; R² = 0.093), and tissue (PERMANOVA, p = 0.97; R² = 0.138). B) Shannon diversity index of vOTUs by location (Kruskal-Wallis test, p = 0.816), sex (Kruskal-Wallis test, p = 0.320), and tissue (Kruskal-Wallis test, p = 0.744). C) Venn diagrams displaying the number of shared vOTUs among triatomine populations, grouped by location, sex, and tissue type.
https://doi.org/10.1371/journal.pntd.0013576.s007
(PDF)
Acknowledgments
We would like to acknowledge our collaborators that contributed to the sampling efforts Robert L. Smith, Walter Roachell and Alvaro Romero.
References
- 1. Bolling BG, Weaver SC, Tesh RB, Vasilakis N. Insect-specific virus discovery: significance for the arbovirus community. Viruses. 2015;7(9):4911–28. pmid:26378568
- 2. de Almeida JP, Aguiar ER, Armache JN, Olmo RP, Marques JT. The virome of vector mosquitoes. Curr Opin Virol. 2021;49:7–12. pmid:33991759
- 3. Ni X-B, Cui X-M, Liu J-Y, Ye R-Z, Wu Y-Q, Jiang J-F, et al. Metavirome of 31 tick species provides a compendium of 1,801 RNA virus genomes. Nat Microbiol. 2023;8(1):162–73. pmid:36604510
- 4. Vandegrift KJ, Kapoor A. The ecology of new constituents of the tick virome and their relevance to public health. Viruses. 2019;11(6):529. pmid:31181599
- 5. Pan Y-F, Zhao H, Gou Q-Y, Shi P-B, Tian J-H, Feng Y, et al. Metagenomic analysis of individual mosquito viromes reveals the geographical patterns and drivers of viral diversity. Nat Ecol Evol. 2024;8(5):947–59. pmid:38519631
- 6. Cardoso MA, Brito TFD, Brito IADA, Berni MA, Coelho VL, Pane A. The neglected virome of triatomine insects. Front Trop Dis. 2022;3.
- 7. Muscio OA, LaTorre JL, Scodeller EA. Small nonoccluded viruses from triatomine bug Triatoma infestans (Hemiptera: Reduviidae). J Invertebr Pathol. 1987;49(2):218–20. pmid:3549911
- 8. Muscio OA, La Torre JL, Scodeller EA. Characterization of Triatoma virus, a picorna-like virus isolated from the triatomine bug Triatoma infestans. J Gen Virol. 1988;69 ( Pt 11):2929–34. pmid:3053988
- 9. Rozas-Dennis GS, Cazzaniga NJ. Effects of Triatoma virus (TrV) on the fecundity and moulting of Triatoma infestans (Hemiptera: Reduviidae). Ann Trop Med Parasitol. 2000;94(6):633–41. pmid:11064765
- 10. Susevich ML, Marti GA, Serena MS, Echeverría MG. New Triatoma virus hosts in wild habitats of Argentina. J Invertebr Pathol. 2012;110(3):405–7. pmid:22484232
- 11. Marti GA, Echeverria MG, Susevich ML, Becnel JJ, Pelizza SA, García JJ. Prevalence and distribution of parasites and pathogens of triatominae from Argentina, with emphasis on Triatoma infestans and Triatoma virus TrV. J Invertebr Pathol. 2009;102(3):233–7. pmid:19660466
- 12. Marti GA, Ragone P, Balsalobre A, Ceccarelli S, Susevich ML, Diosque P, et al. Can Triatoma virus inhibit infection of Trypanosoma cruzi (Chagas, 1909) in Triatoma infestans (Klug)? A cross infection and co-infection study. J Invertebr Pathol. 2017;150:101–5. pmid:28962837
- 13. Muscio OA, La Torre J, Bonder MA, Scodeller EA. Triatoma virus pathogenicity in laboratory colonies of Triatoma infestans (Hemiptera:Reduviidae). J Med Entomol. 1997;34(3):253–6. pmid:9151486
- 14. Brito TFD, Coelho VL, Cardoso MA, Brito IA de A, Berni MA, Zenk FL, et al. Transovarial transmission of a core virome in the Chagas disease vector Rhodnius prolixus. PLoS Pathog. 2021;17(8):e1009780. pmid:34407148
- 15. Bourke BP, de Oliveira J, Ergunay K, Linton Y-M. Leveraging transcriptome sequence read archives for virus detection in wild and colony populations of triatomines (Hemiptera: Reduviidae: Triatominae). Arch Virol. 2024;169(10):215. pmid:39365450
- 16. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20. pmid:24695404
- 17. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27(5):824–34. pmid:28298430
- 18. Bigot T, Temmam S, Pérot P, Eloit M. RVDB-prot, a reference viral protein database and its HMM profiles. F1000Res. 2019;8:530. pmid:32983411
- 19. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59–60. pmid:25402007
- 20. El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47(D1):D427–32. pmid:30357350
- 21. Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer ELL, et al. Pfam: The protein families database in 2021. Nucleic Acids Res. 2021;49(D1):D412–9. pmid:33125078
- 22. Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 2011;39(suppl_2):W29–37. pmid:21593126
- 23. Wernersson R. Virtual Ribosome--a comprehensive DNA translation tool with support for integration of sequence feature annotation. Nucleic Acids Res. 2006;34(suppl_2):W385–8. pmid:16845033
- 24. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10. pmid:2231712
- 25. Simmonds P, Adams MJ, Benkő M, Breitbart M, Brister JR, Carstens EB, et al. Consensus statement: virus taxonomy in the age of metagenomics. Nat Rev Microbiol. 2017;15(3):161–8. pmid:28134265
- 26. Alvarez-Jarreta J, Amos B, Aurrecoechea C, Bah S, Barba M, Barreto A, et al. VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023. Nucleic Acids Res. 2024;52(D1):D808–16. pmid:37953350
- 27. Söding J, Biegert A, Lupas AN. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 2005;33(Web Server issue):W244-8. pmid:15980461
- 28.
Kans J. Entrez direct: e-utilities on the UNIX command line. Entrez programming utilities help [Internet]. National Center for Biotechnology Information (US); 2023.
- 29. Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30(14):3059–66. pmid:12136088
- 30. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25(15):1972–3. pmid:19505945
- 31. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Mol Biol Evol. 2020;37(5):1530–4. pmid:32011700
- 32. Roux S, Adriaenssens EM, Dutilh BE, Koonin EV, Kropinski AM, Krupovic M, et al. Minimum Information about an Uncultivated Virus Genome (MIUViG). Nat Biotechnol. 2019;37(1):29–37. pmid:30556814
- 33. Team RC. R: A language and environment for statistical computing. 4.1.2 ed. 2021.
- 34. Paulson JN, Pop M, Bravo HC. metagenomeSeq: Statistical analysis for sparse high-throughput sequencing. Bioconductor Package. 2013;1:191.
- 35. Oksanen JP. vegan: Community Ecology Package-R package version 1.17-8. 2011.http://CRANR-projectorg/package=vegan
- 36. Wickham H, Chang W, Henry L, Pedersen TL, Takahashi K, Wilke C, et al. ggplot2: create elegant data visualisations using the grammar of graphics. 2020. 2022;3(0):147. https://CRANR-projectorg/package=ggplot2Rpackageversion
- 37. Harper D. Past: paleontological statistics software package for education and data analysis. Palaeontol Electron. 2001;4:4.
- 38. Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics. 2017;33(18):2938–40. pmid:28645171
- 39. Hernandez-Valencia JC, Muñoz-Laiton P, Gómez GF, Correa MM. A systematic review on the viruses of anopheles mosquitoes: the potential importance for public health. Trop Med Infect Dis. 2023;8(10):459. pmid:37888587
- 40. Moming A, Bai Y, Wang J, Zhang Y, Tang S, Fan Z, et al. The known and unknown of global tick-borne viruses. Viruses. 2024;16(12):1807. pmid:39772118
- 41. Gilbert C, Belliardo C. The diversity of endogenous viral elements in insects. Curr Opin Insect Sci. 2022;49:48–55. pmid:34839030
- 42. Hernandez-Valencia JC, Muñoz-Laiton P, Gómez GF, Correa MM. Evidence of endogenous non-retroviral RNA virus sequences into the genome and transcriptome of the malaria vector Anopheles darlingi. Acta Trop. 2024;260:107469. pmid:39549981
- 43. Russo AG, Kelly AG, Enosi Tuipulotu D, Tanaka MM, White PA. Novel insights into endogenous RNA viral elements in Ixodes scapularis and other arbovirus vector genomes. Virus Evol. 2019;5(1):vez010. pmid:31249694
- 44. Wallau GL. RNA virus EVEs in insect genomes. Curr Opin Insect Sci. 2022;49:42–7. pmid:34839033
- 45. Gilmer D, Ratti C, Consortium IR. ICTV virus taxonomy profile: Benyviridae. J General Virol. 2017;98(7):1571–2.
- 46. Debat H, Farrher ES, Bejerman N. Insights into the RNA virome of the corn Leafhopper Dalbulus maidis, a major emergent threat of maize in Latin America. Viruses. 2024;16(10):1583. pmid:39459917
- 47. Wu H, Pang R, Cheng T, Xue L, Zeng H, Lei T, et al. Abundant and diverse RNA viruses in insects revealed by RNA-Seq analysis: ecological and evolutionary implications. mSystems. 2020;5(4):e00039-20. pmid:32636338
- 48. Huang H-J, Ye Z-X, Wang X, Yan X-T, Zhang Y, He Y-J, et al. Diversity and infectivity of the RNA virome among different cryptic species of an agriculturally important insect vector: whitefly Bemisia tabaci. NPJ Biofilms Microbiomes. 2021;7(1):43. pmid:33986295
- 49. Solovyev AG, Morozov SY. Uncovering plant virus species forming novel provisional taxonomic units related to the family Benyviridae. Viruses. 2022;14(12):2680. pmid:36560684
- 50. Da Lage J-L, Fontenelle A, Filée J, Merle M, Béranger J-M, Almeida CE, et al. Hematophagous triatomine bugs feed also on plants and express functional amylase. bioRxiv. 2023.
- 51. Díaz-Albiter HM, Ferreira TN, Costa SG, Rivas GB, Gumiel M, Cavalcante DR, et al. Everybody loves sugar: first report of plant feeding in triatomines. Parasit Vectors. 2016;9:114. pmid:26928036
- 52. Cajimat MNB, Milazzo ML, Haynie ML, Hanson JD, Bradley RD, Fulhorst CF. Diversity and phylogenetic relationships among the North American Tacaribe serocomplex viruses (family Arenaviridae). Virology. 2011;421(2):87–95. pmid:21982818
- 53. Milazzo ML, Cajimat MNB, Haynie ML, Abbott KD, Bradley RD, Fulhorst CF. Diversity among tacaribe serocomplex viruses (family Arenaviridae) naturally associated with the white-throated woodrat (Neotoma albigula) in the southwestern United States. Vector Borne Zoonotic Dis. 2008;8(4):523–40. pmid:18454597
- 54. Radoshitzky SR, Buchmeier MJ, Charrel RN, Gonzalez J-PJ, Günther S, Hepojoki J, et al. ICTV virus taxonomy profile: Arenaviridae 2023. J Gen Virol. 2023;104(9):001891. pmid:37698490
- 55. Breitbart M, Delwart E, Rosario K, Segalés J, Varsani A, Ictv Report Consortium. ICTV virus taxonomy profile: Circoviridae. J Gen Virol. 2017;98(8):1997–8. pmid:28786778
- 56. Garigliany M-M, Börstler J, Jöst H, Badusche M, Desmecht D, Schmidt-Chanasit J, et al. Characterization of a novel circo-like virus in Aedes vexans mosquitoes from Germany: evidence for a new genus within the family Circoviridae. J Gen Virol. 2015;96(Pt 4):915–20. pmid:25535324
- 57. Ha Z, Li J-F, Xie C-Z, Li C-H, Zhou H-N, Zhang Y, et al. First detection and genomic characterization of porcine circovirus 3 in mosquitoes from pig farms in China. Vet Microbiol. 2020;240:108522. pmid:31902486
- 58. Cerna GM, Serieys LEK, Riley SPD, Richet C, Kraberger S, Varsani A. A circovirus and cycloviruses identified in feces of bobcats (Lynx rufus) in California. Arch Virol. 2023;168(1):23. pmid:36593430
- 59. Hess SC, Weiss KCB, Custer JM, Lewis JS, Kraberger S, Varsani A. Identification of small circular DNA viruses in coyote fecal samples from Arizona (USA). Arch Virol. 2023;169(1):12. pmid:38151635
- 60. Flynn PJ, Moreau CS. Viral diversity and co-evolutionary dynamics across the ant phylogeny. Mol Ecol. 2024;33(19):e17519. pmid:39192682
- 61. Kraberger S, Cook CN, Schmidlin K, Fontenele RS, Bautista J, Smith B, et al. Diverse single-stranded DNA viruses associated with honey bees (Apis mellifera). Infect Genet Evol. 2019;71:179–88. pmid:30928605
- 62. Bandoo RA, Kraberger S, Ozturk C, Lund MC, Zhu Q, Cook C, et al. Identification of diverse Bacteriophages Associated with Bees and Hoverflies. Viruses. 2025;17(2):201. pmid:40006956
- 63. Calle-Tobón A, Pérez-Pérez J, Forero-Pineda N, Chávez OT, Rojas-Montoya W, Rúa-Uribe G, et al. Local-scale virome depiction in Medellín, Colombia, supports significant differences between Aedes aegypti and Aedes albopictus. PLoS One. 2022;17(7):e0263143. pmid:35895627
- 64. Feng Y, Gou Q-Y, Yang W-H, Wu W-C, Wang J, Holmes EC, et al. A time-series meta-transcriptomic analysis reveals the seasonal, host, and gender structure of mosquito viromes. Virus Evol. 2022;8(1):veac006. pmid:35242359
- 65. Li C, Liu S, Zhou H, Zhu W, Cui M, Li J, et al. Metatranscriptomic sequencing reveals host species as an important factor shaping the mosquito virome. Microbiol Spectr. 2023;11(2):e0465522. pmid:36786616
- 66. Liu Q, Cui F, Liu X, Fu Y, Fang W, Kang X, et al. Association of virome dynamics with mosquito species and environmental factors. Microbiome. 2023;11(1):101. pmid:37158937
- 67. Tian D, Ye R-Z, Li Y-Y, Wang N, Gao W-Y, Wang B-H, et al. Virome specific to tick genus with distinct ecogeographical distribution. Microbiome. 2025;13(1):57. pmid:40022268
- 68. Shi C, Beller L, Deboutte W, Yinda KC, Delang L, Vega-Rúa A, et al. Stable distinct core eukaryotic viromes in different mosquito species from Guadeloupe, using single mosquito viral metagenomics. Microbiome. 2019;7(1):121. pmid:31462331
- 69. Hernandez-Valencia JC, Gómez GF, Correa MM. Metagenomic analysis evidences a core virome in Anopheles darlingi from three contrasting Colombian ecoregions. PLoS One. 2025;20(4):e0320593. pmid:40305569
- 70. Ritsch M, Brait N, Harvey E, Marz M, Lequime S. Endogenous viral elements: insights into data availability and accessibility. Virus Evol. 2024;10(1):veae099. pmid:39659497
- 71. Brait N, Hackl T, Morel C, Exbrayat A, Gutierrez S, Lequime S. A tale of caution: How endogenous viral elements affect virus discovery in transcriptomic data. Virus Evol. 2023;10(1):vead088. pmid:38516656
- 72. Katzourakis A, Gifford RJ. Endogenous viral elements in animal genomes. PLoS Genet. 2010;6(11):e1001191. pmid:21124940
- 73. Santos D, Feng M, Kolliopoulou A, Taning CNT, Sun J, Swevers L. What are the functional roles of Piwi proteins and piRNAs in insects? Insects. 2023;14(2):187. pmid:36835756