Comprehensive virome analysis of Varroa destructor populations in South Korea

The ectoparasitic mite Varroa destructor and the viruses it transmits pose a significant threat to honey bee health (Apis mellifera), contributing to colony collapse disorder. In this study, a metatranscriptomic analysis of V. destructor from six regions in South Korea was conducted to characterize its viral communities. Using high-throughput sequencing (HTS), we identified 16 known viruses and classified them into three groups: honey bee-pathogenic, Varroa destructor viruses (VDVs), and viruses with unknown hosts. Reverse transcription polymerase chain reaction (RT-PCR) was used to validate the HTS results, revealing only two discrepancies out of 96 comparisons. This emphasizes the importance of integrating both methods for comprehensive virome analysis. Deformed wing virus was the most prevalent and abundant virus, comprising 75%–99% of viral reads in five out of six farms. One farm showed a high abundance of VDVs (3 and 9). Notably, two previously unreported viruses with unknown hosts, Hubei partiti-like virus 34 and Lilac leaf chlorosis virus (LLCV), were identified. For LLCV, the detection of all ribonucleic acid segments highlighted the critical impact of sequencing depth on viral genome analysis. To our knowledge, this study provides the first virome characterization of V. destructor in South Korea, revealing diverse viral communities. It also proposes an integrated analytical approach using RT-PCR and HTS, emphasizing the importance of sequencing depth. This analysis provides valuable insights into the potential impacts of viral infections on honey bee colony health and the epidemiology of viral transmission.

1 Introduction

Apis mellifera is a globally significant pollinator that plays a vital role in agricultural ecosystems by pollinating food crops and wild plants (1). In recent years, there has been a decline in honey bee populations due to the growing crisis of colony loss (2, 3). These declines are attributed to a combination of biotic and abiotic factors (4), with Varroa destructor and the viruses it transmits playing major roles in colony loss (5, 6). V. destructor parasitizes honey bee pupae by penetrating feeding holes in the abdominal sternites and extracting hemolymph and fat body, thereby compromising the host’s humoral immune system (7). As a result, the honey bee’s immune system is suppressed, making them highly susceptible to viral infections (8). Moreover, V. destructor serves as a vector for pathogenic viruses that are transmitted to honey bees during feeding (9). Hence, when Varroa mites are present in colonies, they can contribute to colony loss by impairing the host immune system and facilitating the transmission of viruses (6).

V. destructor mediates a variety of viruses, including pathogenic viruses in honey bees. Currently, more than 20 viral species have been identified in V. destructor (10–12), among which deformed wing virus (DWV), sacbrood virus (SBV), Israeli acute paralysis virus (IAPV), acute bee paralysis virus (ABPV), black queen cell virus (BQCV), Kashmir bee virus (KBV), and chronic bee paralysis virus (CBPV) cause severe diseases in honey bees (13). DWV replicates within the Varroa mite and suppresses host immunity, resulting in higher levels of DWV transmitted to honey bees (5, 12). DWV causes developmental deformities and premature aging, leading to high overwintering colony losses (14–16) ABPV, KBV, and IAPV (the AKI complex) belong to the same genus, Aparavirus (family Dicistroviridae), and share genome similarity (17). V. destructor serves as an effective vector of the AKI complex and causes acute paralysis in honey bees (18). BQCV and SBV primarily infect honey bees, causing failure in pupation and persisting in adult honey bees as asymptomatic infection (19–21). CBPV causes a severe chronic paralytic disease in adult honey bees (22). Several RNA viruses specific to Varroa (Varroa destructor virus 2, Varroa destructor virus 3, Varroa destructor virus 4, Varroa destructor virus 5, and Varroa destructor virus 9) have been identified in this ectoparasitic mite (23–25). These viruses can replicate not only in V. destructor but also in A. mellifera, indicating that their presence in honey bees is due to the Varroa’s feeding behavior. Most viruses found in V. destructor belong to positive-sense single-stranded RNA viruses, although a few other viral types have also been identified. Negative-sense RNA viruses, such as Apis rhabdovirus 1 (ARV1) and Apis rhabdovirus 2 (ARV2), and double-stranded DNA viruses, such as Apis mellifera filamentous virus, have also been identified in Varroa mites (26, 27).

Previous research on viruses associated with V. destructor in South Korea was conducted by Moon et al. (28), who identified six honey bee pathogenic viruses (DWV, SBV, IAPV, BQCV, CBPV, and KBV) in V. destructor from 46 apiaries across the country using the PCR method, with DWV showing the highest prevalence. However, PCR-based methods using specific primers have certain limitations in detecting novel viruses and quantifying viral loads (29).

Over the past decade, high-throughput sequencing (HTS) has revolutionized virology by enabling the identification of viral abundance in hosts (30). This approach allows for the detection of novel viruses and has significantly shifted the paradigm of viral diversity and ecological dynamics in insects such as mosquitoes and bees (31–33). In parallel, several countries have conducted mass screenings to explore potential virological links between V. destructor and colony collapse disorder (CCD) (10, 11, 19, 24, 34). For example, Lester et al. (2022) performed RNA-seq analysis of V. destructor collected across New Zealand and identified 10 viruses, including nine that were also found in honey bees (34). Similarly, Levin et al. (2016) reported 22 viruses in V. destructor, including two that had not been previously described (25). A recent study from China reported the discovery of more than 23 novel viruses in V. destructor and A. mellifera colonies, with the viral community in V. destructor exhibiting greater richness compared to that in A. mellifera (19). In addition, Eliash et al. (2022) analyzed a large dataset of 66 Varroa transcriptomes and identified at least three types of viruses, including VDV2, which was co-infecting with V. destructor (11). Virome analysis using HTS allows for consistent scale studies of the spatial and temporal distribution of viral communities and facilitates the discovery of novel pathogenic viruses within V. destructor (35).

Currently, Varroa mite populations in South Korea have developed resistance to several pesticides, including pyrethroids and formamidines (36). This resistance results in failure to control Varroa mites, threatening conventional Varroa management strategies for maintaining healthy honey bee colonies. Therefore, the ectoparasitic mite V. destructor has been recognized as the primary cause of honey bee population losses during overwintering in South Korea (37). Moreover, previous research on Varroa-associated viruses has primarily focused on detecting known pathogenic viruses using PCR assays (28). Therefore, in this study, we conducted a comprehensive virome analysis of V. destructor using the HTS approach. Metatranscriptomic data were obtained from mites collected across six geographically distinct regions of South Korea in 2022, which enabled the identification of the composition, distribution, and abundance of the viral community. The distribution and abundance of the virus were found to be different among the six regions. These findings were further validated using PCR-based assays. We also conducted phylogenetic analyses to investigate the genetic relationships among viral genotypes and infer their possible evolutionary origins. Our study provides a comprehensive overview of the viral community within V. destructor populations in South Korea and also fundamental insights into the dynamics of Varroa-mediated viral transmission.

2 Materials and methods

2.1 Insects and RNA sequencing

V. destructor samples were collected from multiple hives across six regions in South Korea in September 2022 and were placed in 70% ethanol and stored at −80°C. These samples were pooled into groups of 10–20 individuals per sample (Supplementary Data 1 in Supplementary File 1) and transferred to 2 mL tubes containing Total RNA Isolation Reagent^® (Molecular Research Center, Cincinnati, OH, USA) along with four steel beads. Each pooled sample was thoroughly homogenized using a Precellys Evolution (Bertin Technologies, Montigny-le-Bretonneux, France) at 6,300 rpm for two cycles of 20 s each, with a 30-s pause. After homogenization, RNA extraction was performed using the Direct-zol™ RNA MiniPrep Plus (Zymo Research, Irvine, CA, USA), following the manufacturer’s instructions. DNase I (Thermo Fisher Scientific, Waltham, MA, USA) was added during RNA extraction to remove residual DNA. The concentration and purity of the RNA were evaluated using a QuickDrop spectrophotometer (Molecular Devices, Sunnyvale, CA, USA), and the samples were stored at −80 °C.

Total RNA concentration was calculated using Quant-IT RiboGreen (Invitrogen, Waltham, MA, USA). To evaluate the integrity of the total RNA, the samples were run on TapeStation RNA ScreenTape (Agilent Technologies, Santa Clara, CA, USA), and only high-quality RNA samples with an RIN value greater than 5.0 were used for constructing RNA libraries. An RNA library was prepared using 0.5 µg of total RNA for each sample using Illumina TruSeq Stranded Total RNA Library Prep Gold Kit (Illumina, Inc., San Diego, CA, USA). After the removal of rRNA, the remaining mRNA was fragmented into small pieces using divalent cations under temperature. The cleaved RNA fragments were copied into first-strand cDNA using SuperScript II reverse transcriptase (Invitrogen, Waltham, MA, USA) and random primers, followed by second-strand cDNA synthesis using DNA polymerase I, RNase H, and dUTP. These cDNA fragments then underwent an end-repair process, addition of a single “A” base, and then ligation of the adapters. The products were then purified and enriched by PCR to generate the final cDNA library. The libraries were quantified using KAPA Library Quantification kits for Illumina Sequencing platforms according to the qPCR Quantification Protocol Guide (KAPA BIOSYSTEMS, Wilmington, MA, USA) and qualified using TapeStation D1000 ScreenTape (Agilent Technologies, Santa Clara, CA, USA). Indexed libraries were then submitted to Illumina NovaSeqX (Illumina, Inc., San Diego, CA, USA), and paired-end (2 × 150 bp) sequencing was performed by Macrogen Incorporated (Macrogen, Seoul, South Korea). The sequencing depth was calculated using the total number of reads per sample. All samples were sequenced at a 40× depth. For farm 2, we requested 90× depth to capture the sequencing accuracy.

2.2 Virome analysis

Virome analysis was conducted using paired-end sequencing data derived from the Illumina platform. Quality control of the raw reads was performed using FastQC (version 0.12.1) (38). Adapters and low-quality reads (Phred quality score <30) were removed using FastP (version 0.23.4) (39). The clean reads were aligned to the host genome assembly of V. destructor (accession: GCA000181155.2). Host-derived reads were removed by mapping them to the host genome using Bowtie2 (version 2.5.4) (40). Unmapped reads were then assembled using metaviralSPAdes (version 4.0.0) (41). To identify viral transcripts, the assembled contigs were aligned against the NCBI RefSeq database using the BLASTn algorithm. Viral genomes were confirmed using a cutoff E-value of 1e-50. Taxonomic classification of the viruses was determined using the NCBI reference genome database. The abundance of viral transcripts was quantified using the transcripts per million (TPM) value with Kallisto (version 0.46.2) (42).

2.3 Phylogenetic analysis

A phylogenetic analysis was performed for the following viruses: DWV-A, SBV, and ARV1. Their sequences were aligned using ClustalW (version 2.0) (43), and a phylogenetic tree was constructed using the maximum likelihood method in the MEGA11 program (version 11.0.10) (44). The phylogenetic relationships were displayed as a rooted phylogenetic tree. This branch was determined by performing 1,000 bootstrap replications.

2.4 RT-PCR assays

RT-PCR was performed to confirm the presence of 16 viruses in V. destructor identified through transcriptomic analysis. After sequencing, the remaining RNA (total 500 ng) was used to generate cDNA using ReverTra Ace qPCR RT Master Mix with gDNA Remover (Toyobo, Osaka, Japan), following the manufacturer’s protocol. Primer sets were designed using the PrimerQuest Tool provided by Integrated DNA Technologies (https://sg.idtdna.com/pages/tools/primerquest). The RT-PCR mixture consisted of 10 µL AccuPower Master Mix (Bioneer, Daejeon, South Korea), 1 µL of each primer (10 µM), and 1 µL of cDNA, prepared to a total volume of 8 µL with nuclease-free water. The PCR cycling conditions were as follows: 95 °C for 5 min, followed by 35 cycles of 95 °C for 15 s, 55 °C for 30 s, and 72 °C for 60 s, with a final extension at 72 °C for 5 min. The PCR products were separated by 1% agarose gel electrophoresis and visualized using SYBR™ Green I Nucleic Acid Gel Stain (Invitrogen, Waltham, MA, USA).

3 Results

3.1 Viral community of V. destructor

Our study demonstrated the viral distribution and abundance of V. destructor in six different farms across South Korea using HTS (Supplementary Data 1 in Supplementary File 1). Of the 26 viruses known to be infected by V. destructor, 16 were identified in at least one of the six samples (Figure 1). The viruses were classified into three groups based on their major host: honey bee pathogenic viruses, Varroa destructor viruses, and other viruses with unknown hosts. The first group comprised six honey bee pathogenic viruses: DWV, SBV, IAPV, BQCV, ABPV, and CBPV. The second group included viruses with V. destructor as the primary host, namely VDV2, VDV3, VDV4, VDV5, VDV9, and Varroa orthomyxovirus 1 (VOV1). The third group comprised ARV1, ARV2, Lilac leaf chlorosis virus (LLCV), and Hubei partiti-like virus 34 (HPLV34), whose primary hosts remain unidentified.

Figure 1

Figure 1. Heatmap of the distribution and abundance of 16 viruses in the six samples. The Y-axis represents the 16 identified viruses in this study, grouped by their primary host (honey bee–pathogenic viruses, Varroa destructor viruses, and other viruses). The X-axis represents the six different farms where V. destructor samples were collected. The heatmap represents the relative abundance of virus-based TPM values, according to the color gradient legend in the right panel. The “+” mark indicates the sample that was confirmed by PCR. The white boxes indicate that no viral reads were detected in the corresponding samples.

3.2 Overview of viruses detected in V. destructor from South Korea

3.2.1 Distribution of viruses across six farms

We examined the presence of 16 viruses in V. destructor samples collected from six farms across South Korea. Each sample was found to be coinfected with at least eight viruses. Among honey bee-pathogenic viruses, DWV was detected in all six regions, and IAPV was identified in samples collected from farms 3 and 4. BQCV was exclusively detected in farm 1. ABPV and CBPV were detected in farms 2 and 4. V. destructor viruses, including VDV2, VDV3, VDV5, and VDV9, were consistently identified in all samples, whereas VDV4 was not detected in samples collected from farms 3 and 5. VOV1 was identified in all samples except in samples collected from farm 6. ARV1 and ARV2 were also detected in all samples. LLCV and HPLV34 were only identified in farms 2 and 5 (Figure 1). We performed RT-PCR to validate the HTS results, which revealed two discrepancies of 96 RT-PCR assays: CBPV and ABPV. CBPV was detected by HTS in farm 2 but not by PCR. In contrast, ABPV was detected by PCR but not by HTS. HTS and RT-PCR exhibited a low overall error rate of 0.01%.

3.2.2 Abundance of viruses across six farms

Analysis of the relative abundance of viruses revealed DWV as the predominant virus in five farms, whereas farm 2 displayed a different pattern of viral abundance (Figure 2).

Figure 2

Figure 2. Viral compositions of V. destructor from the six samples. Relative abundance (%) of viruses detected in Varroa mites from six farms. Each bar represents the viral composition of a single mite sample, with colors indicating the individual virus species. N = total number of co-infecting viruses detected per farm. (*) indicates discrepancies between RT-PCR and HTS results.

3.2.3 DWV-predominant samples: Farms 1, 3, 4, 5, and 6

DWV was the most abundant virus in samples collected from farms 1, 3, 4, 5, and 6, accounting for 75%–99% of the total viral reads. In three farms (1, 3, and 4), DWV accounted for 98%~99% of the total viral reads. The proportion of other viruses did not exceed >1% of the total viral reads. In farm 5, DWV accounted for 74%, with HPLV34 comprising for 23% of the total viral reads. Consistently, DWV was the most abundant virus in farm 6, accounting for 89%, followed by VDV9 at 9%.

3.2.4 DWV-low abundance sample: Farm 2

DWV exhibited high levels of abundance in most samples, whereas farm 2 demonstrated relatively low abundance. In farm 2, DWV accounted for 1% of the total viral reads. In contrast, VDV3 was the most abundant virus in farm 2, accounting for 48% of the total viral reads, followed by VDV9, which accounted for 39%.

3.3 Phylogenetic tree of three viruses (DWV, ARV1, and ARV2)

Phylogenetic analyses of three viruses (DWV, ARV1, and ARV2) whose genotypes and regional strains were associated with virulence revealed distinct phylogenetic groups according to the genetic differences among the viruses (Figure 3).

Figure 3

Figure 3. Phylogenetic tree of DWV. The DWV sequences were aligned using ClustalW. The phylogenetic tree was constructed using the neighbor-joining method, with bootstrap values based on 1,000 replicates. Samples from this study are indicated by black arrows.

3.3.1 Deformed wing virus

Five DWV sequences were obtained in this study, and the similarity between sequences was confirmed as 96%~97%. Phylogenetic analysis revealed that all five sequences were clustered in the DWV-A clade and were closely related to the DWV strain Korea-2 (JX878305) (Figure 3). Results showed that the DWV-A clade predominantly comprised isolates from Central and Southeast Asian countries, such as China and Vietnam. DWV-B and DWV-C were primarily detected in Europe and the United States. These results demonstrated that DWV-A is the predominant genotype in South Korea.

3.3.2 Sacbrood virus

Phylogenetic analysis was conducted based on SBV identified in farm 1. The phylogenetic tree clearly diverged into two main branches, corresponding to host species, specifically A. mellifera genotype (Am) and A. cerana genotype (Ac). The SBV isolates from A. mellifera comprised the Am genotype, whereas those from A. cerana comprised Am and Ac genotypes. Phylogenetic analysis revealed that SBV detected in this study was classified as the Am genotype and was closely related to SBV (JQ390591) isolated from A. mellifera in South Korea (Figure 4).

Figure 4

Figure 4. Phylogenetic tree of SBV. The SBV sequences were aligned using ClustalW. The phylogenetic tree was constructed using the neighbor-joining method, with bootstrap values based on 1,000 replicates. Samples from this study are indicated by black arrows.

3.3.3 Apis rhabdovirus

We compared ARV1 with 30 genomes available in GenBank to evaluate its regional characteristics. The genome of ARV1 obtained from farm 6 demonstrated high sequence similarity with an ARV1 isolate from China (MZ821788) (Figure 5). The phylogenetic analysis revealed that ARV1 demonstrated 99.4% similarity to A. mellifera isolated from South Korea (OR496404). These results suggest that the Korean ARV1 is genetically closely related to strains from China.

Figure 5

Figure 5. Phylogenetic tree of ARV1. The ARV1 sequences were aligned using ClustalW. The phylogenetic tree was constructed using the neighbor-joining method, with bootstrap values based on 1,000 replicates. Samples from this study are highlighted using a black arrow.

3.4 Identification of previously unreported viruses in V. destructor

The HTS analysis identified two viruses that had not been previously reported in V. destructor: LLCV in farm 2 and HPLV34 sequence in farm 5. HPLV34 had a genome length of 1,022 nt and contained a single open reading frame (ORF) (Supplementary Data 5A in Supplementary File 1). This ORF exhibited 99.67% nucleotide sequence similarity to the RNA-dependent RNA polymerase (RdRp) of HPLV34 (ON648754). RT-PCR was performed to validate the presence of virus in farm 5 (Supplementary Data 5B in Supplementary File 1). Furthermore, LLCV contains three RNA segments (RNA1, RNA2, and RNA3) (Figure 4). At 40× sequencing depth, the viral segments LLCV RNA1 and RNA3 were detected, and BLAST analysis revealed 88.67% similarity to LLCV RNA1 (NC025477) and 95% similarity to LLCV RNA3 (NC025481) (Figure 6A). To confirm the presence of RNA2, the same sample was analyzed at 90× sequencing depth, where the LLCV RNA2 sequence was identified (Figure 6A). BLAST hits also demonstrated 95% similarity to LLCV RNA2 (NC025478). RNA1 was predicted to encode helicase (Hel), and RNA2 was predicted to encode RdRp. RNA3 was identified as containing two ORFs, including the movement protein (MP) and the coat protein (CP) (Figure 6B). We performed RT-PCR to validate the presence of all three segments of LLCV, which were detected in farm 2 (Figure 6C). Hence, in this study, the genomes of HPLV34 and LLCV were identified, which had not been previously reported in V. destructor, and their presence was confirmed using RT-PCR.

Figure 6

Figure 6. Previously unreported LLCVs in V. destructor identified in this study. (A) Mapping coverage plot based on read mapping of the Lilac leaf chlorosis virus. Sequencing reads were mapped to the Lilac leaf chlorosis virus reference genome. The x-axis represents the genome position (bp), and the y-axis represents the sequencing depth (reads). The left panel shows coverage generated through 40× sequencing; the right panel shows coverage at a sequencing depth of 90×. (B) Genome of Lilac leaf chlorosis virus in V. destructor. The genome structure of the virus comprises three RNA segments: RNA1, RNA2, and RNA3. RNA1 encodes a helicase protein; RNA2 encodes a polyprotein including RNA-dependent RNA polymerase (RdRP); and RNA3 encodes two proteins, movement protein (MP) and coat protein (CP). (C) RT-PCR analysis of Lilac leaf chlorosis virus. Gel electrophoresis results confirmed the presence of RNA1, RNA2, and RNA3.

4 Discussion

This study conducted a comprehensive virome analysis of V. destructor, an ectoparasitic mite of honey bees. Varroa mites and viruses transmitted by V. destructor are the major causes of CCD. Of the 26 viruses known to infect V. destructor (11, 24), 16 were identified in our HTS data. The viruses identified from Varroa mites were classified into three groups: honey bee-pathogenic viruses, Varroa destructor viruses, and other viruses. This study described the viral community of V. destructor collected in six regions of South Korea in 2022 and emphasized the significant differences in viral composition and distribution across the regions, thereby contributing to our understanding of the viral epidemiology of V. destructor.

The seven viruses: DWV, SBV, IAPV, BQCV, ABPV, CBPV, and KBV, are pathogenic to A. mellifera (13, 21, 45–47). Of the seven honey bee–pathogenic viruses, six were detected in our samples, with KBV absent from all regions. DWV was detected in all six regions, whereas the other viruses were detected in only one or two regions; therefore, the geographic distribution of DWV was compared with that of the other viruses. DWV, the most prevalent virus in global beekeeping, exhibits increasing prevalence when the ectoparasite V. destructor is present within colonies (48, 49). The abundance of DWV accounted for 76%–99% of the total viral reads in all farms, except farm 2, where its relative abundance was low. DWV has been classified into three primary genotypes: DWV-A, DWV-B, and DWV-C, among which DWV-A was the most prevalent (50, 51). In contrast, the DWV-B genotype is highly pathogenic and has recently become dominant in the United States and Europe (52). According to phylogenetic analysis, the DWVs identified in our samples were categorized as DWV-A (Figure 3). This result indicated DWV-A as the predominant genotype of V. destructor in South Korea in 2022. This finding is consistent with previous studies that highlighted DWV-A as the most prevalent genotype in East Asia, including South Korea (50, 52). Among the six regions, SBV was detected only in farm 1. SBV was classified into two types: AmSBV, which infects only western honey bees (A. mellifera), and AcSBV, which infects both western honey bees (A. mellifera) and eastern honey bees (A. cerana) (53, 54). According to our genome analysis, the SBV detected in our study was the AmSBV type, consistent with previous research that reported an identical genotype isolated from A. mellifera in South Korea (53). IAPV and BQCV were detected only in certain farms (Figure 1). Interestingly, there were discrepancies between the results of HTS and PCR as shown in Figure 1: only two discrepancies were observed among the 96 analyses (16 viruses in six regions). CBPV was identified by PCR, whereas ABPV was detected only by HTS. This result suggests that HTS sometimes fails to detect low-abundance viral sequences or underestimates the viral load (55). In contrast, due to technical limitations such as the primer conditions, sample concentration, and amplicon quality, some viruses may not be detected by PCR (56, 57). According to Supplementary Data 3 in Supplementary File 1, CBPV showed the lowest TPM value among the 11 viruses in farm 2, with an extremely low read count of viral reads, indicating relatively low viral abundance. Detecting such low-abundance viruses by PCR would require more amplification cycles, which could result in nonspecific amplification and a risk of false-positive results (58). KBV exhibits sequence homology of up to 70% with IAPV (59); however, none of the PCR and HTS results indicated the presence of KBV. Considering that we confirmed the distribution and presence of the virus using two methods, PCR and HTS, further studies using small RNA sequencing to detect viral siRNAs within mites are required to confirm whether honey bee-pathogenic viruses replicate within V. destructor (24).

We identified several Varroa destructor viruses in this study, including VDV2, VDV3, VDV4, VDV5, VDV9, and VOV1, of which four viruses: VDV2, VDV3, VDV5, and VDV9, were detected in all six regions, whereas VDV4 was detected in three regions. In a previous study, Eliash et al. (11) investigated the distribution of these viruses in 66 V. destructor samples and detected VDV2 in all SRA samples, with a 100% prevalence. VDV5 was detected in 67% of samples, whereas VDV3 and VDV4 had lower prevalence rates of 35% and 17%, respectively (11). VDV9 was not found in the analysis because its genome sequence was unknown at that time (24). Consistent with these previous findings, when we evaluated the abundance of various VDVs in Varroa mites and quantitatively compared their abundances within the samples, our results confirmed the presence of VDV2 in all six regions, with the lowest prevalence observed for VDV4. Moreover, we conducted a quantitative comparison of VDVs within each sample using TPM values to determine the relative abundance (Figure 2, Supplementary Data 3 in Supplementary File 1). In most samples, VDV3 showed the highest abundance, followed by VDV5, VDV9, and VDV2, except in farm 6. Irrespective of whether DWV was dominant, the relative abundance of VDV3 was consistently highest across the samples. A similar study in New Zealand investigated the relative abundance of VDVs in 27 bee hives with V. destructor infestation and reported a distribution pattern of VDV2 > VDV9 > VDV3 > VDV5 (34). Unlike the results from New Zealand, our study showed a relatively low abundance of VDV2 in all regions. In another study, Lester et al. (34) found a negative relationship between VDV2 and DWV, where a high abundance of VDV2 in V. destructor mites was associated with a reduced abundance of DWV in the mites. In our study, the hypothesis did not fit that farm 2, which had an extremely low abundance of DWV, exhibited VDV3 as the predominant virus, whereas VDV2 was relatively less abundant. This result suggests a lack of correlation between the abundances of VDV2 and DWV, thus supporting the results of Herrero et al. (2019) (60), who also found no significant associations between the two viruses. Various VDV strains are widely distributed within V. destructor populations; however, there are limited studies on their potential virulence in mite populations and the dynamics of viral transmission. Therefore, additional research is required to examine the distribution and abundance of VDVs in V. destructor, thereby gaining a better understanding of the interactions between viruses and their hosts.

Our analysis revealed the presence of ARV1, ARV2, HPLV34, and LLCV, whose major hosts have not been clearly identified. ARV1 was detected in all six regions. This virus has also been reported in A. mellifera populations in Africa, Asia, Europe, and the Pacific region (61). Phylogenetic analysis revealed that our ARV1 strain belonged to the same branch as the Chinese genetic lineages (Figure 5). ARV2, which shared approximately 50% nucleotide identity with ARV1, was confirmed as a distinct virus and was also detected in all regions. Nevertheless, due to the limited availability of ARV2 genome sequences in NCBI, we were unable to construct a comprehensive phylogenetic tree. Furthermore, we detected HPLV34 and LLCV, neither of which has been previously reported in V. destructor. HPLV34 was also previously identified in honey bees (33, 62). LLCV is a member of the genus Ilarvirus (family Bromoviridae) and contains tripartite genome segments RNA1, RNA2, and RNA3 (63). Analysis at 40× sequencing depth consistently detected LLCV RNA1 and RNA3 segments in full, whereas the RNA2 segment was not detected. Hence, we hypothesized that increasing the sequencing depth would enable us to obtain a complete set of viral genomes. We reanalyzed the same sample at 90× sequencing depth and successfully identified the previously undetected LLCV RNA2 segment (Figure 6). These results demonstrated that increased sequencing depth improved the accuracy of viral genome detection and the identification of genomic variations (64). Our results confirmed the presence of LLCV in Varroa mites; therefore, we raised the question: how do plant viruses infect Varroa mites? Such previously unreported sequences may derive from HTS artifacts. This phenomenon, known as index hopping, occurs when multiple samples are processed within a single sequencing lane (65). To exclude the possibility of HTS artifacts, we conducted RT-PCR to amplify the viral sequence and confirmed the presence of the virus in the RNA sample. The presence of plant viruses in V. destructor indicates two hypotheses: (1) contamination by pollen on the surface or inside of V. destructor, and (2) V. destructor acting as a new host for plant-associated viruses. Lee et al. (2023) (66) reported the presence of LLCV in pollen and bee bread metagenomes, supporting the hypothesis of contamination via pollen. Pollen is a major route for the transmission of plant viruses to honey bees; however, limited knowledge exists about the dynamics of cross-species transmission to Varroa mites via pollen (67). Regarding the second hypothesis that V. destructor could serve as a new host for plant-associated viruses, previous research has supported the hypothesis that tobacco ringspot virus, which replicates in honey bees, was present in V. destructor (68). To summarize, both hypotheses could explain the presence of plant viruses in V. destructor, and further studies are necessary to determine whether these viruses actively replicate within V. destructor.

Based on previous studies, 26 types of viruses have been identified in Varroa mites (11, 24). However, in our samples collected in 2022, 10 of the 26 viruses were not detected. To confirm the absence of these viruses, the sequencing reads were aligned to each reference genome as a means of resequencing. Our results showed no evidence for the presence of the 10 viruses that had been previously reported in V. destructor, including slow bee paralysis virus (SBPV), bee macula-like virus (BMLV), Lake Sinai virus (LSV), Apis flavivirus (AFV), Apis mellifera filamentous virus (AmFV), Varroa mite associated genomovirus 1 isolate VPVL_46 (VPVL 46), KBV, Varroa Tymo-like virus (VTLV), Apis mellifera nora virus 1 (ANV), and Varroa mite associated genomovirus 1 isolate VPVL_36 (VPVL 36). Although virome studies have primarily focused on honey bees due to their economic importance in agriculture (19, 33, 34), considering the potential role of Varroa mites as a vector for honey bee viruses, continuous monitoring of the viral composition and relative abundance of Varroa mites according to region and time could provide critical insights into interactions between the parasite and virus, as well as their impact on the honey bee ecosystem.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Author contributions

JK: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing. KM: Formal analysis, Investigation, Methodology, Resources, Writing – review & editing. YK: Formal analysis, Investigation, Methodology, Resources, Writing – review & editing. NE: Formal analysis, Investigation, Methodology, Writing – review & editing. JY: Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was supported by National University Development Project at Jeonbuk National University in 2025.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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References

1. Khalifa SA, Elshafiey EH, Shetaia AA, El-Wahed AAA, Algethami AF, Musharraf SG, et al. Overview of bee pollination and its economic value for crop production. Insects. (2021) 12:688. doi: 10.3390/insects12080688

PubMed Abstract | Crossref Full Text | Google Scholar

2. Goulson D, Nicholls E, Botías C, and Rotheray EL. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science. (2015) 347:1255957. doi: 10.1126/science.1255957

PubMed Abstract | Crossref Full Text | Google Scholar

3. Potts SG, Biesmeijer JC, Kremen C, Neumann P, Schweiger O, and Kunin WE. Global pollinator declines: trends, impacts and drivers. Trends Ecol evolution. (2010) 25:345–53. doi: 10.1016/j.tree.2010.01.007

PubMed Abstract | Crossref Full Text | Google Scholar

4. Neov B, Georgieva A, Shumkova R, Radoslavov G, and Hristov P. Biotic and abiotic factors associated with colonies mortalities of managed honey bee (Apis mellifera). Diversity. (2019) 11:237. doi: 10.3390/d11120237

Crossref Full Text | Google Scholar

5. Di Prisco G, Annoscia D, Margiotta M, Ferrara R, Varricchio P, Zanni V, et al. A mutualistic symbiosis between a parasitic mite and a pathogenic virus undermines honey bee immunity and health. Proc Natl Acad Sci. (2016) 113:3203–8. doi: 10.1073/pnas.1523515113

PubMed Abstract | Crossref Full Text | Google Scholar

6. Traynor KS, Mondet F, de Miranda JR, Techer M, Kowallik V, Oddie MA, et al. Varroa destructor: A complex parasite, crippling honey bees worldwide. Trends parasitology. (2020) 36:592–606. doi: 10.1016/j.pt.2020.04.004

PubMed Abstract | Crossref Full Text | Google Scholar

7. Ramsey SD, Ochoa R, Bauchan G, Gulbronson C, Mowery JD, Cohen A, et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc Natl Acad Sci. (2019) 116:1792–801. doi: 10.1073/pnas.1818371116

PubMed Abstract | Crossref Full Text | Google Scholar

8. DeGrandi-Hoffman G and Chen Y. Nutrition, immunity and viral infections in honey bees. Curr Opin Insect science. (2015) 10:170–6. doi: 10.1016/j.cois.2015.05.007

PubMed Abstract | Crossref Full Text | Google Scholar

9. Morfin N, Goodwin PH, and Guzman-Novoa E. Varroa destructor and its impacts on honey bee biology. Front Bee Science. (2023) 1:1272937. doi: 10.3389/frbee.2023.1272937

Crossref Full Text | Google Scholar

10. Chang F-M, Chen Y-H, Hsu P-S, Wu T-H, Sung I-H, Wu M-C, et al. RNA metagenomics revealed insights into the viromes of honey bees (Apis mellifera) and Varroa mites (Varroa destructor) in Taiwan. J Invertebrate Pathol. (2025) 211:108341. doi: 10.1016/j.jip.2025.108341

PubMed Abstract | Crossref Full Text | Google Scholar

11. Eliash N, Suenaga M, and Mikheyev AS. Vector-virus interaction affects viral loads and co-occurrence. BMC Biol. (2022) 20:284. doi: 10.1186/s12915-022-01463-4

PubMed Abstract | Crossref Full Text | Google Scholar

12. Posada-Florez F, Childers AK, Heerman MC, Egekwu NI, Cook SC, Chen Y, et al. Deformed wing virus type A, a major honey bee pathogen, is vectored by the mite Varroa destructor in a non-propagative manner. Sci Rep. (2019) 9:12445. doi: 10.1038/s41598-019-47447-3

PubMed Abstract | Crossref Full Text | Google Scholar

13. Ai H, Yan X, and Han R. Occurrence and prevalence of seven bee viruses in Apis mellifera and Apis cerana apiaries in China. J invertebrate pathology. (2012) 109:160–4. doi: 10.1016/j.jip.2011.10.006

PubMed Abstract | Crossref Full Text | Google Scholar

14. Highfield AC, El Nagar A, Mackinder LC, Noël LM-L, Hall MJ, Martin SJ, et al. Deformed wing virus implicated in overwintering honeybee colony losses. Appl Environ Microbiol. (2009) 75:7212–20. doi: 10.1128/AEM.02227-09

PubMed Abstract | Crossref Full Text | Google Scholar

15. Gusachenko ON, Woodford L, Balbirnie-Cumming K, Ryabov EV, and Evans DJ. Evidence for and against deformed wing virus spillover from honey bees to bumble bees: A reverse genetic analysis. Sci Rep. (2020) 10:16847. doi: 10.1038/s41598-020-73809-3

PubMed Abstract | Crossref Full Text | Google Scholar

16. Minaud É, Rebaudo F, Davidson P, Hatjina F, Hotho A, Mainardi G, et al. How stressors disrupt honey bee biological traits and overwintering mechanisms. Heliyon. (2024) 10:e34390. doi: 10.1016/j.heliyon.2024.e34390

PubMed Abstract | Crossref Full Text | Google Scholar

17. De Miranda JR, Cordoni G, and Budge G. The acute bee paralysis virus–Kashmir bee virus–Israeli acute paralysis virus complex. J invertebrate pathology. (2010) 103:S30–47. doi: 10.1016/j.jip.2009.06.014

PubMed Abstract | Crossref Full Text | Google Scholar

18. Di Prisco G, Pennacchio F, Caprio E, Boncristiani HF Jr., Evans JD, and Chen Y. Varroa destructor is an effective vector of Israeli acute paralysis virus in the honeybee, Apis mellifera. J Gen Virology. (2011) 92:151–5. doi: 10.1099/vir.0.023853-0

PubMed Abstract | Crossref Full Text | Google Scholar

19. Li N, Li C, Hu T, Li J, Zhou H, Ji J, et al. Nationwide genomic surveillance reveals the prevalence and evolution of honeybee viruses in China. Microbiome. (2023) 11:6. doi: 10.1186/s40168-022-01446-1

PubMed Abstract | Crossref Full Text | Google Scholar

20. Šimenc L, Knific T, and Toplak I. The comparison of honeybee viral loads for six honeybee viruses (ABPV, BQCV, CBPV, DWV, LSV3 and SBV) in healthy and clinically affected honeybees with TaqMan quantitative real-time RT-PCR assays. Viruses. (2021) 13:1340. doi: 10.3390/v13071340

PubMed Abstract | Crossref Full Text | Google Scholar

21. Li J, Wang T, Evans JD, Rose R, Zhao Y, Li Z, et al. The phylogeny and pathogenesis of sacbrood virus (SBV) infection in European honey bees, Apis mellifera. Viruses. (2019) 11:61. doi: 10.3390/v11010061

PubMed Abstract | Crossref Full Text | Google Scholar

22. Ribière M, Olivier V, and Blanchard P. Chronic bee paralysis: a disease and a virus like no other? J invertebrate Pathol. (2010) 103:S120–S31. doi: 10.1016/j.jip.2009.06.013

PubMed Abstract | Crossref Full Text | Google Scholar

23. Chen G, Wang S, Jia S, Feng Y, Hu F, Chen Y, et al. A new strain of virus discovered in China specific to the parasitic mite Varroa destructor poses a potential threat to honey bees. Viruses. (2021) 13:679. doi: 10.3390/v13040679

PubMed Abstract | Crossref Full Text | Google Scholar

24. Damayo JE, McKee RC, Buchmann G, Norton AM, Ashe A, and Remnant EJ. Virus replication in the honey bee parasite, Varroa destructor. J Virology. (2023) 97:e01149–23. doi: 10.1128/jvi.01149-23

PubMed Abstract | Crossref Full Text | Google Scholar

25. Levin S, Sela N, and Chejanovsky N. Two novel viruses associated with the Apis mellifera pathogenic mite Varroa destructor. Sci Rep. (2016) 6:37710. doi: 10.1038/srep37710

PubMed Abstract | Crossref Full Text | Google Scholar

26. Cornman RS, Schatz MC, Johnston JS, Chen Y-P, Pettis J, Hunt G, et al. Genomic survey of the ectoparasitic mite Varroa destructor, a major pest of the honey bee Apis mellifera. BMC Genomics. (2010) 11:1–15. doi: 10.1186/1471-2164-11-602

PubMed Abstract | Crossref Full Text | Google Scholar

27. Levin S, Galbraith D, Sela N, Erez T, Grozinger CM, and Chejanovsky N. Presence of Apis rhabdovirus-1 in populations of pollinators and their parasites from two continents. Front Microbiol. (2017) 8:2482. doi: 10.3389/fmicb.2017.02482

PubMed Abstract | Crossref Full Text | Google Scholar

28. Moon K, Cho S, Lee J, Lee SH, Seong KM, and Kim YH. Molecular detection and phylogenetic analysis of six bee viruses from Varroa destructor in Korea. J Apicultural Res. (2024) 64:106387. doi: 10.1080/00218839.2024.2411769

Crossref Full Text | Google Scholar

29. Yang S and Rothman RE. PCR-based diagnostics for infectious diseases: uses, limitations, and future applications in acute-care settings. Lancet Infect diseases. (2004) 4:337–48. doi: 10.1016/S1473-3099(04)01044-8

PubMed Abstract | Crossref Full Text | Google Scholar

30. Van Aerle R and Santos E. Advances in the application of high-throughput sequencing in invertebrate virology. J invertebrate pathology. (2017) 147:145–56. doi: 10.1016/j.jip.2017.02.006

PubMed Abstract | Crossref Full Text | Google Scholar

31. Galbraith DA, Fuller ZL, Ray AM, Brockmann A, Frazier M, Gikungu MW, et al. Investigating the viral ecology of global bee communities with high-throughput metagenomics. Sci Rep. (2018) 8:8879. doi: 10.1038/s41598-018-27164-z

PubMed Abstract | Crossref Full Text | Google Scholar

32. 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 Evolution. (2024) 8:947–59. doi: 10.1038/s41559-024-02365-0

PubMed Abstract | Crossref Full Text | Google Scholar

33. Kwon M, Jung C, and Kil E-J. Metagenomic analysis of viromes in honey bee colonies (Apis mellifera; Hymenoptera: Apidae) after mass disappearance in Korea. Front Cell Infection Microbiol. (2023) 13:1124596. doi: 10.3389/fcimb.2023.1124596

PubMed Abstract | Crossref Full Text | Google Scholar

34. Lester PJ, Felden A, Baty JW, Bulgarella M, Haywood J, Mortensen AN, et al. Viral communities in the parasite Varroa destructor and in colonies of their honey bee host (Apis mellifera) in New Zealand. Sci Rep. (2022) 12:8809. doi: 10.1038/s41598-022-12888-w

PubMed Abstract | Crossref Full Text | Google Scholar

35. Charlebois RL, Sathiamoorthy S, Logvinoff C, Gisonni-Lex L, Mallet L, and Ng SH. Sensitivity and breadth of detection of high-throughput sequencing for adventitious virus detection. NPJ Vaccines. (2020) 5:61. doi: 10.1038/s41541-020-0207-4

PubMed Abstract | Crossref Full Text | Google Scholar

36. Lee J, Lee JH, Lim Y, Cho S, Moon K, Kim S, et al. Rapid spread of Amitraz resistance linked to a unique T115N mutation in the octopamine receptor of Varroa mites in Korea. Pesticide Biochem Physiol. (2025) 106387. doi: 10.1016/j.pestbp.2025.106387

PubMed Abstract | Crossref Full Text | Google Scholar

37. Truong A-T, Yoo M-S, Yun B-R, Kang JE, Noh J, Hwang TJ, et al. Prevalence and pathogen detection of Varroa and Tropilaelaps mites in Apis mellifera (Hymenoptera, Apidae) apiaries in South Korea. J Apicultural Res. (2023) 62:804–12. doi: 10.1080/00218839.2021.2013425

Crossref Full Text | Google Scholar

38. Andrews S. FastQC: a quality control tool for high throughput sequence data. Cambridge, United Kingdom (2010).

Google Scholar

39. Chen S, Zhou Y, Chen Y, and Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. (2018) 34:i884–i90. doi: 10.1093/bioinformatics/bty560

PubMed Abstract | Crossref Full Text | Google Scholar

40. Langmead B and Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. (2012) 9:357–9. doi: 10.1038/nmeth.1923

PubMed Abstract | Crossref Full Text | Google Scholar

41. Antipov D, Raiko M, Lapidus A, and Pevzner PA. Metaviral SPAdes: assembly of viruses from metagenomic data. Bioinformatics. (2020) 36:4126–9. doi: 10.1093/bioinformatics/btaa490

PubMed Abstract | Crossref Full Text | Google Scholar

42. Bray NL, Pimentel H, Melsted P, and Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. (2016) 34:525–7. doi: 10.1038/nbt.3519

PubMed Abstract | Crossref Full Text | Google Scholar

43. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, et al. Clustal W and clustal X version 2.0. bioinformatics. (2007) 23:2947–8. doi: 10.1093/bioinformatics/btm404

PubMed Abstract | Crossref Full Text | Google Scholar

44. Tamura K, Stecher G, and Kumar S. MEGA11: molecular evolutionary genetics analysis version 11. Mol Biol evolution. (2021) 38:3022–7. doi: 10.1093/molbev/msab120

PubMed Abstract | Crossref Full Text | Google Scholar

45. Chen YP, Pettis JS, Corona M, Chen WP, Li CJ, Spivak M, et al. Israeli acute paralysis virus: epidemiology, pathogenesis and implications for honey bee health. PloS pathogens. (2014) 10:e1004261. doi: 10.1371/journal.ppat.1004261

PubMed Abstract | Crossref Full Text | Google Scholar

46. Bailey L, Gibbs A, and Woods R. Two viruses from adult honey bees (Apis mellifera Linnaeus). Virology. (1963) 21:390–5. doi: 10.1016/0042-6822(63)90200-9

PubMed Abstract | Crossref Full Text | Google Scholar

47. Dall D. Multiplication of Kashmir bee virus in pupae of the honeybee, Apis mellifera. J Invertebrate Pathology. (1987) 49:279–90. doi: 10.1016/0022-2011(87)90060-7

Crossref Full Text | Google Scholar

48. Benaets K, Van Geystelen A, Cardoen D, De Smet L, de Graaf DC, Schoofs L, et al. Covert deformed wing virus infections have long-term deleterious effects on honeybee foraging and survival. Proc R Soc B: Biol Sci. (2017) 284:20162149. doi: 10.1098/rspb.2016.2149

PubMed Abstract | Crossref Full Text | Google Scholar

49. Piou V, Schurr F, Dubois E, and Vétillard A. Transmission of deformed wing virus between Varroa destructor foundresses, mite offspring and infested honey bees. Parasites Vectors. (2022) 15:333. doi: 10.1186/s13071-022-05463-9

PubMed Abstract | Crossref Full Text | Google Scholar

50. Hasegawa N, Techer MA, Adjlane N, Al-Hissnawi MS, Antúnez K, Beaurepaire A, et al. Evolutionarily diverse origins of deformed wing viruses in western honey bees. Proc Natl Acad Sci. (2023) 120:e2301258120. doi: 10.1073/pnas.2301258120

PubMed Abstract | Crossref Full Text | Google Scholar

51. Zhang Z, Villalobos EM, Nikaido S, and Martin SJ. Seasonal variability in the prevalence of DWV strains in individual colonies of european honeybees in hawaii. Insects. (2024) 15:219. doi: 10.3390/insects15040219

PubMed Abstract | Crossref Full Text | Google Scholar

52. Paxton RJ, Schäfer MO, Nazzi F, Zanni V, Annoscia D, Marroni F, et al. Epidemiology of a major honey bee pathogen, deformed wing virus: potential worldwide replacement of genotype A by genotype B. Int J Parasitology: Parasites Wildlife. (2022) 18:157–71.

PubMed Abstract | Google Scholar

53. Choe SE, Nguyen LT, Noh JH, Kweon CH, Reddy KE, Koh HB, et al. Analysis of the complete genome sequence of two Korean sacbrood viruses in the Honey bee, Apis mellifera. Virology. (2012) 432:155–61. doi: 10.1016/j.virol.2012.06.008

PubMed Abstract | Crossref Full Text | Google Scholar

54. Gong H-R, Chen X-X, Chen YP, Hu F-L, Zhang J-L, Lin Z-G, et al. Evidence of Apis cerana Sacbrood virus Infection in Apis mellifera. Appl Environ Microbiol. (2016) 82:2256–62. doi: 10.1128/AEM.03292-15

PubMed Abstract | Crossref Full Text | Google Scholar

55. Quer J, Colomer-Castell S, Campos C, Andrés C, Piñana M, Cortese MF, et al. Next-generation sequencing for confronting virus pandemics. Viruses. (2022) 14:600. doi: 10.3390/v14030600

PubMed Abstract | Crossref Full Text | Google Scholar

56. Gibbs RA. DNA amplification by the polymerase chain reaction. Analytical Chem. (1990) 62:1202–14. doi: 10.1021/ac00212a004

PubMed Abstract | Crossref Full Text | Google Scholar

57. Sachse K. Specificity and performance of PCR detection assays for microbial pathogens. Mol Biotechnol. (2004) 26:61–79. doi: 10.1385/MB:26:1:61

PubMed Abstract | Crossref Full Text | Google Scholar

58. Borst A, Box A, and Fluit A. False-positive results and contamination in nucleic acid amplification assays: suggestions for a prevent and destroy strategy. Eur J Clin Microbiol Infect diseases. (2004) 23:289–99.

PubMed Abstract | Google Scholar

59. Sabath N, Price N, and Graur D. A potentially novel overlapping gene in the genomes of Israeli acute paralysis virus and its relatives. Virol J. (2009) 6:1–7. doi: 10.1186/1743-422X-6-144

PubMed Abstract | Crossref Full Text | Google Scholar

60. Herrero S, Millán-Leiva A, Coll S, González-Martínez RM, Parenti S, and González-Cabrera J. Identification of new viral variants specific to the honey bee mite Varroa destructor. Exp Appl Acarology. (2019) 79:157–68.

PubMed Abstract | Google Scholar

61. Remnant EJ, Shi M, Buchmann G, Blacquière T, Holmes EC, Beekman M, et al. A diverse range of novel RNA viruses in geographically distinct honey bee populations. J virology. (2017) 91(16):e00158-17. doi: 10.1128/jvi.00158-17

PubMed Abstract | Crossref Full Text | Google Scholar

62. Šimenc Kramar L and Toplak I. Identification of Twenty-Two New Complete Genome Sequences of Honeybee Viruses Detected in Apis mellifera carnica Worker Bees from Slovenia. Insects. (2024) 15:832. doi: 10.3390/insects15110832

PubMed Abstract | Crossref Full Text | Google Scholar

63. James D, Varga A, Leippi L, Godkin S, and Masters C. Sequence analysis of RNA 2 and RNA 3 of lilac leaf chlorosis virus: a putative new member of the genus Ilarvirus. Arch virology. (2010) 155:993–8. doi: 10.1007/s00705-010-0673-5

PubMed Abstract | Crossref Full Text | Google Scholar

64. Zuryn S and Jarriault S eds. Deep sequencing strategies for mapping and identifying mutations from genetic screens. In: Worm. Taylor & Francis 2(3):e25081.

PubMed Abstract | Google Scholar

65. Costello M, Fleharty M, Abreu J, Farjoun Y, Ferriera S, Holmes L, et al. Characterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platforms. BMC Genomics. (2018) 19:1–10. doi: 10.1186/s12864-018-4703-0

PubMed Abstract | Crossref Full Text | Google Scholar

66. Lee E, Vansia R, Phelan J, Lofano A, Smith A, Wang A, et al. Area wide monitoring of plant and honey bee (Apis mellifera) viruses in blueberry (Vaccinium corymbosum) agroecosystems facilitated by honey bee pollination. Viruses. (2023) 15:1209. doi: 10.3390/v15051209

PubMed Abstract | Crossref Full Text | Google Scholar

67. Singh R, Levitt AL, Rajotte EG, Holmes EC, Ostiguy N, Vanengelsdorp D, et al. RNA viruses in hymenopteran pollinators: evidence of inter-taxa virus transmission via pollen and potential impact on non-Apis hymenopteran species. PloS One. (2010) 5:e14357. doi: 10.1371/journal.pone.0014357

PubMed Abstract | Crossref Full Text | Google Scholar

68. Li JL, Cornman RS, Evans JD, Pettis JS, Zhao Y, Murphy C, et al. Systemic spread and propagation of a plant-pathogenic virus in European honeybees, Apis mellifera. MBio. (2014) 5(1):e00898-13. doi: 10.1128/mbio.00898-13

PubMed Abstract | Crossref Full Text | Google Scholar