Article Characterisation of the Viral Community Associated with the Alfalfa Weevil (Hypera postica) and Its Host Plant, Alfalfa (Medicago sativa)

Sarah François 1,2,*, Aymeric Antoine-Lorquin 2, Maximilien Kulikowski 2, Marie Frayssinet 2, Denis Filloux 3,4, Emmanuel Fernandez 3,4, Philippe Roumagnac 3,4, Rémy Froissart 5 and Mylène Ogliastro 2,*

1 Peter Medawar Building for Pathogen Research, Department of Zoology, University of Oxford, South Park Road, Oxford OX1 3SY, UK 2 DGIMI Diversity, Genomes and Microorganisms–Insects Interactions, University of Montpellier, INRAE 34095 Montpellier, France; [email protected] (A.A.-L.); [email protected] (M.K.); [email protected] (M.F.) 3 CIRAD, UMR PHIM, 34090 Montpellier, France; [email protected] (D.F.); [email protected] (E.F.); [email protected] (P.R.) 4 PHIM Plant Health Institute, University of Montpellier, CIRAD, INRAE, Institut Agro, IRD 34090 Montpellier, France 5 MIVEGEC Infectious and Vector Diseases: Ecology, Genetics, Evolution and Control, University of Montpellier, CNRS, IRD 34394 Montpellier, France; [email protected] Citation: François, S.; * Correspondence: [email protected] (S.F.); [email protected] (M.O.) Antoine-Lorquin, A.; Kulikowski, M.; Frayssinet, M.; Abstract: Advances in viral metagenomics have paved the way of discovery by making the Filloux, D.; Fernandez, E.; exploration of in any ecosystem possible. Applied to agroecosystems, such an approach Roumagnac, P.; Froissart, R.; opens new possibilities to explore how viruses circulate between insects and plants, which may help Ogliastro, M. Characterisation of the to optimise their management. It could also lead to identifying novel entomopathogenic viral re- Viral Community Associated with sources potentially suitable for biocontrol strategies. We sampled the larvae of a natural population the Alfalfa Weevil (Hypera postica) of alfalfa weevils (Hypera postica), a major herbivorous pest feeding on legumes, and its host plant and Its Host Plant, alfalfa (Medicago sativa). Insect and plant samples were collected from a crop field and an adjacent Alfalfa (Medicago sativa). meadow. We characterised the diversity and abundance of viruses associated with weevils and al- Viruses 2021, 13, 791. https://doi.org/ falfa, and described nine putative new virus species, including four associated with alfalfa and five 10.3390/v13050791 with weevils. In addition, we found that trophic accumulation may result in a higher diversity of

Academic Editor: Miguel plant viruses in phytophagous pests compared to host plants. López-Ferber Keywords: insect pest; agroecosystem; viral metagenomics; virus diversity; biocontrol Received: 21 March 2021 Accepted: 26 April 2021 Published: 28 April 2021 1. Introduction Publisher’s Note: MDPI stays neu- Our knowledge of viruses infecting insects is largely skewed towards a small number tral with regard to jurisdictional of species of economical or health concerns in humans and their domesticated plants and claims in published maps and insti- animals [1]. Yet, insects belong to the most widespread and diversified group of animals tutional affiliations. on Earth, and despite their relatively small size, they represent a significant part of the terrestrial biomass [2]. In this context, and assuming that virus diversity correlates with the diversity of their hosts, insects could represent an extraordinary reservoir and a po-

Copyright: © 2021 by the authors. Li- tential “nursery” of virus diversity [3]. censee MDPI, Basel, Switzerland. Viral metagenomics has revolutionised virus discovery from samples of any nature This article is an open access article and at all scales, i.e., from the scale of an individual to populations living in the same distributed under the terms and con- environment [4–8]. Nowadays, a viral metagenomic approach combined with high- ditions of the Creative Commons At- throughput sequencing and dedicated bioinformatic tools allows the exploration of a tribution (CC BY) license (http://crea- large fraction of the diversity of viruses with no a priori knowledge. This approach also tivecommons.org/licenses/by/4.0/). allowed the “pathogen-centered” approach relying on virus amplification that had long

Viruses 2021, 13, 791. https://doi.org/10.3390/v13050791 www.mdpi.com/journal/viruses Viruses 2021, 13, 791 2 of 17

prevailed for virus discovery to be overcome [9]. Viral metagenomics can be now applied to the surveillance of agro-ecosystems (agrosystems), i.e., anthropized ecosystems dedi- cated to food production, and can help to understand how viruses, including pathogenic ones, circulate and impact their functioning [10]. Insects are integral components of agrosystems, where they fulfil crucial functions, if only to cite pollination. However, the abundance of food and favourable trophic condi- tions can make some herbivorous or sap-feeding species switch to real “pests of concern”. Management plans to reduce insect pests’ pressure on food production have long relied on chemical insecticides, which are easy to use and efficient, making made them success [11]. As a counterpart, chemicals have rapidly induced resistances in pest populations, and their lack of specificity has triggered overall stresses on ecosystems stability and re- silience capacity including detrimental effects on human health and on biodiversity [12]. All these reasons make the reduction in their use a great challenge for future decades and push policy makers to expand and diversify alternative methods to control pests. Biocontrol refers to the use of natural enemies to control pests, among which microbial pesticides and viruses have proven efficient and successful, although their use remains lim- ited to a few cropping systems [13]. Entomopathogenic viruses may represent a largely un- explored biocontrol resource [13], so exploring agrosystems using metagenomics may help to understand the diversity and structure of virus communities associated with insect pests. Such exploration may also help to find local, adapted viral solutions and design adapted management strategies for the sustainable control of insect pests. The weevil Hypera postica (Coleoptera, Curculionidae) is a major widespread pest that can reach high densities on leguminous plants. This pest particularly affects alfalfa (Medi- cago sativa L.) yield production, resulting in significant economic losses in Europe and the USA [14]. Damages to alfalfa plants are first caused by larvae, although adults feeding on alfalfa buds significantly affect the plant regrowth, which worsens the reduction in dry matter production [15]. Chemical management remains the main method to control H. postica, although few biocontrol trials with parasitic wasps (Bathyplectes species), general- ist predators (coccinalids) and fungal pathogens have been reported [16,17]. However, because our understanding of viruses infecting curculionids is scarce, only one ento- mopathogenic virus is used to control these beetles to our knowledge: an (Chilo iridescent virus) tested to control the cotton weevil (Anthonomus grandis) [18]. The objective of this work was to explore the diversity of viruses associated with the alfalfa weevil using a viral metagenomic approach. We characterized for the first time the diversity of viruses that can be found in this pest, including four new putative virus spe- cies infecting alfalfa and five new virus species infecting weevils.

2. Materials and Methods 2.1. Sampling Alfalfa and Alfalfa Weevils Larvae of alfalfa weevils (H. postica) and alfalfa leaves (M. sativa) species were col- lected on the 1st April 2015 in Domaine de Restinclières, Prades-le-Lez, France (43.705043 °N, 3.863410 °W). The sampling took place in an experimental plot including an alfalfa field that did not receive any insecticide and an adjacent meadow. Insects were collected with nets on a surface of 100 m2/sample in both crop and meadow. Alfalfa leaves were randomly collected from plants located in the crop field and in the neighbouring meadow. One leaf was sampled per individual. Samples were maintained and transported on ice to the lab and stored at −80 °C until processing. Supplementary Table S1 summarizes all the samples and their associated metadata.

2.2. Insect Identification Alfalfa weevils were identified morphologically and molecularly by PCR barcoding using the COI gene primers Uni-MinibarF1: 5′-TCC ACT AAT CAC AAR GAT ATT GGT AC-3′ and Uni-MinibarR1: 5′- GAA AAT CAT AAT GAA GGC ATG AGC-3′ [19]. Insect

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DNA was extracted from 10 grinded individuals using a Wizard DNA purification kit (Promega, Madison, WI, USA). PCR amplification was performed using a HotStarTaq Master Mix Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. The following cycling conditions were used: one cycle of 95 °C for 2 min, 5 cycles of 95 °C for 1 min, 46 °C for 1 min, 72 °C for 30 sec and 35 cycles of 95 °C for 1 min, 53 °C for 1 min and 72 °C for 30 sec. An additional final extension was then performed for 5 min at 72 °C. The yield of the PCR products was verified by the migration of 8 µl of PCR products to 2% agarose gel and visualised by staining with ethidium bromide. Amplicons were cleaned up using a Wizard SV Gel and PCR Clean-Up kit (Promega) according to the manufacturer′s protocol. Purified PCR products were sequenced by Sanger sequencing (Beckman Coulter Genomics, France). Chromatograms were checked for disparities be- tween the forward and reverse reads, and consensus sequences for each amplicon, then taxonomic assignment was performed using BLASTn [20] against the BOLD database [21].

2.3. Sample Preparation and Sequencing Viromes were obtained from 14 alfalfa weevil samples (each sample being a pool of individuals weighting from 250 to 1900 mg and containing between 50 and 500 larvae) and 2 alfalfa plant samples (each sample being a pool of leaves from 10 individuals) as described previously [22] (Supplementary Table S1). Briefly, approximately one gram of insect or plant material was processed using a virion-associated nucleic acid (VANA) based metagenomics approach to screen for the presence of DNA and RNA viruses. Insect samples were grounded and centrifuged to recover supernatants that were next filtered through a 0.45 µm filter and centrifuged at 140000 g for 2.5 hours to concentrate viral parti- cles. The resulting pellets were resuspended, and nucleic acids not protected in virus-like particles (VLPs) were degraded by DNase and RNase incubation at 37 °C for 1.5 h. Total RNA and DNA were then extracted using a NucleoSpin kit (Macherey Nagel, Bethlehem, PA, USA). Reverse transcription was performed by SuperScript III reverse transcriptase (Invitrogen), cDNAs were purified by a QIAquick PCR Purification Kit (Qiagen, Hilden, Germany) and complementary strands synthesised by Klenow DNA polymerase I. Double- stranded DNA was amplified by random PCR amplification. Samples were barcoded dur- ing reverse transcription and PCR steps using homemade 26-nt Dodeca Linkers and PCR multiplex identifier primers. PCR products were purified using NucleoSpin gel and PCR clean-up (Macherey Nagel, Bethlehem, PA, USA). Three negative controls were added dur- ing (1) viral purification, (2) nucleic acid extraction, and (3) cDNA purification. Finally, li- braries were prepared from purified amplicons and sequenced on an Illumina MiSeq V3 chemistry to generate 300 nt paired-end (PE) reads (Genewiz, South Plainfield, NJ, USA).

2.4. Viral Genome Reconstruction Illumina adaptors were removed, and reads were filtered for quality (q30 quality and read length >45 nt) using cutadapt 1.18 [23]. Cleaned reads were assembled de novo into contigs using MEGAHIT 1.2.8 [24]. Taxonomic assignment was achieved on contigs of length >900 nt through searches against the NCBI gbvrl viral database using DIAMOND 0.9.22 with an e-value cutoff of <10−5 [25]. All contigs that matched virus sequences were selected and used as queries to perform reciprocal searches on NCBI non-redundant protein sequence database with an e-value cutoff of <10−3 in order to eliminate likely false positives. Viral contigs completion and coverage was assessed by iterative mapping using BOWTIE2 2.3.4.3 with the options end−to-end and very-sensitive [26]. Putative Open Reading Frames (ORFs) were identified using ORF finder (length cutoff >300 nt) on Geneious prime 2021.0.3 [27]. In all subsequent analyses, we focused only on full or near full coding sequences (>90% of CDS) based on the alignments of genomes with their closest relatives combined with ORF completeness, thus discarding contigs with partial CDS. The viral isolates that belonged to already described species were reconstructed as follows: after mapping against the closest reference isolate deposited in the NCBI nucleotide database, consensus sequences were

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generated using samtools 1.2 [28]. Mutations were called at depth ≥5 if they differed from the reference isolate; otherwise, sites were kept as those of the reference isolate.

2.5. Virus Discovery and Taxonomic Assignment To determine if viral contigs belonged to new species, their nucleotidic sequences or their predicted protein sequences were aligned and compared with the 10 closest related viral genomes found by similarity searches performed above using MAFFT v7.450 with the G-INS-i algorithm [29,30] or MUSCLE 3.8.425 (16 iterations) [31] using default settings. Alignment results were visualised using SDT 1.2 [32], and genomes were classified as new virus strain versus new virus species according to the species demarcation thresholds rec- ommended within the online reports of the International Committee on Taxonomy of Vi- rus (ICTV) (https://talk.ictvonline.org/).

2.6. Phylogenetic Analyses Phylogenetic trees were built using maximum likelihood methods for all recon- structed genomes corresponding to new species in order to place them within the cur- rently known viral diversity and to infer their possible host range. Representative sets of replication and proteins, or polyproteins, were extracted from the NCBI GenBank non-redundant (nr) database for each taxonomic group in which the genomes were clas- sified (analysis performed on the 22 January 2021). Amino acid sequences were aligned using MUSCLE 3.8.425 (16 iterations) with default settings [31]. Sequences that were not reliably aligned due to high amino acid divergence were removed and the dataset subse- quently realigned. Phylogenetic trees were constructed in RAxML 8.2.11 [33] using the LG + I + G protein evolution model. Tree branch support was estimated using 1000 boot- strapped replicates, except for the and family trees for which 100 bootstrapped replicates were used. Trees were mid-point rooted and visualised with FigTree 1.4 (http://tree.bio.ed.ac.uk/software%20/figtree/).

2.7. Screening for Virus Presence in Samples Using PCR The presence of the most abundant virus species found in weevils (Hypera postica associated iflavirus 1) was confirmed in Hypera postica samples using specific PCR and Sanger sequencing. Purified DNA was screened for the presence of this virus using a spe- cific primer pair targeting a 1127 nt long region of the polyprotein gene (8007F: 5′-GCT GGC TTT TCA GAC GGC TCT A-3′, 9134R: 5′-TGG ATT ACC GCT AGG CAT CCC A- 3′). A PCR mix was prepared using the HotStarTaq Master Mix kit (Qiagen, Hilden, Ger- many) according to the manufacturer′s protocol. Amplification conditions were as fol- lows: 95 °C for 2 min, then 35 cycles of 95 °C for 2 min, 55 °C for 1 min, 72 °C for 2 min and a final extension step of 72 °C for 5 min. Amplicons were Sanger sequenced (Beckman Coulter Genomics, Marseille, France), and sequences were aligned with the reference ge- nome sequence.

2.8. Statistical Analyses Statistical analyses were performed using R and RStudio 1.2.5019 software [34,35]. Data from contingency tables were standardised to allow inter-sample comparisons: tax- onomic binning artefacts and potential inter-sample contamination were restricted by (1) applying an abundance threshold >1/10,000 reads/taxon/sample, and (2) for taxa that con- taminated negative controls, by removing them from sample datasets where their abun- dance was equal or inferior to their abundance in controls. Inter-virus taxa standardisation was performed by dividing, for each virus taxa, the number of virus reads by the length of viral contigs (kb). Viral diversity accumulation curves of H. postica viromes were made using the Vegan package [36]. Differences in viromes richness and composition of H. pos- tica and M. sativa were visualised using barplots and a heatmap.

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3. Results 3.1. Virome Composition and Virus Share between Alfalfa Weevils and Alfalfa Plants The taxonomic assignment of contigs obtained from 16 samples (14 insect samples, each sample being a pool of 50 to 500 larvae; two alfalfa plant samples, each sample being a pool of leaves from 10 individuals—see Supplementary Table S1) to viral families was highly variable among samples, ranging between 10% and 75% (average 41%) of the total number of cleaned reads (Figure 1). However, the sequencing depth was not sufficient to recover the entire viral communities as indicated by the rarefaction curve of 3/16 samples that did not reach the asymptote (Supplementary Figure S1). This feature might be due to the stringent abundance threshold we used to eliminate inter-sample contamination and artefactual taxonomic assignments (see materials and methods), which probably contrib- uted to discarding rare viral taxa. In addition, our sampling effort is not sufficient to re- cover the entire diversity of viruses that were circulating in the targeted population of alfalfa weevils (Figure 2).

Figure 1. Proportion of reads classified (red) and unclassified (blue) at the family level. Hp: Hypera postica samples; Ms: Medicago sativa samples.

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0 2 5 1 s e i l i m a f

f o

r e b m 0 u 1 N 5

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Number of samples

Figure 2. Accumulation curves of viral communities recovered from Hypera postica samples at the family level.

Overall, we found 23 virus families associated with the alfalfa agrosystem (Figure 3), including plant or fungus-associated viruses belonging to 11 virus families (Alphaflexiviri- dae, , , , , , Luteoviridae, , , and ) and arthropod-infecting virus be- longing to nine families (, Iflaviridae, , , Permutotet- raviridae, , Qinviridae, and Sinhaliviridae). Sequences belonging to three families of (, and ) were also de- tected but at low abundance (Supplementary Table S2). Remarkably, all viromes were dominated by RNA viruses, both in terms of family richness and abundance of reads, classified in 18 virus families corresponding to (+) ssRNA viruses that infect arthropods and plants. Regarding weevil samples, viral sequences corresponding to families of arthropod- infecting viruses were the most abundant, representing on average 76% of the reads (the remaining 24% correspond to putative plant/fungus-infecting viruses) (Supplementary Figure S2). More precisely, the weevil viromes were dominated (in read occurrence and abundance) by viruses belonging to the Iflaviridae family (Figure 3). They represent high read counts in all the samples. By contrast, viruses belonging to other families of arthro- pod-infecting viruses, i.e., Parvoviridae (subfamily Densovirinae), , Sin- haliviridae, Phenuiviridae, Qinviridae, Reoviridae, Birnaviridae and Mesoniviridae, were de- tected at low abundance and low frequency, in one or two samples only (Figures 3 and 4). Plant/fungus-infecting viruses belonging to seven families were only detected in weevils (i.e., Solemoviridae, Secoviridae, Luteoviridae, Geminiviridae, Endornaviridae, Caulimoviridae and Alphaflexiviridae) (Figure 3, Supplementary Table S2), where they can represent high read counts, like members of the family Partitiviridae (>1000 reads), in five weevil samples (Figure 4 and Supplementary Table S2). On the other hand, plant/fungal viruses belonging to four families were found in alfalfa samples (Amalgaviridae, Bromoviridae, Partitiviridae and Tymoviridae); this result is likely to be due to the low number of collected plants (20

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individuals) compared to collected arthropods. No arthropod-infecting virus sequences were found in the viromes of alfalfa plant samples. Interestingly, members of families Partitiviridae and Alphaflexiviridae were present in most, if not all, weevil samples, although at various abun- dances. Members of the family Partitiviridae were found to be also highly abundant in alfalfa samples, unlike Alphaflexiviridae family members that were not detected in alfalfa samples, which might be explained by their high and low prevalence in the agrosystem, respectively. Finally, viruses belonging to the family Amalgaviridae displayed higher read counts in the al- falfa collected from the meadow compared to the crop. The low abundance and presence of the members of this virus family in the weevils sampled in the crop further support that amal- gaviruses displayed a low prevalence and/or cause covert infection in alfalfa (Figure 4).

Figure 3. Abundance of arthropod-infecting viruses (red) and plant- or fungus-infecting viruses (blue) found in alfalfa weevils and in alfalfa. The horizontal axis represents the log10 number of reads attributed to each family.

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Figure 4. Heatmap representing the abundance of virus families in alfalfa (Ms) and alfalfa weevils (Hp). The colours rep- resent the log10 number of reads attributed to each family.

3.2. Virus Discovery We reconstructed 17 contigs from the 16 samples, ranging from 1712 to 12,255 nt and we obtained the full coding sequences for all but one, for which we recovered > 95% of its coding sequence (Supplementary Tables S3 and S4). The depth of coverage across all con- tigs is high, having an average depth ranging from 56 to 28,625 reads, representing a total of 2433,637 mapped reads (Supplementary Table S4). Eight of these contigs correspond to six virus species infecting alfalfa that have al- ready been reported [37]. Their genomes share high levels of genome identity with their closest relatives (98.2% to 100% nt identity). These viruses likely represent new isolates belonging to six species (Supplementary Table S4, Supplementary Figures S3– S6), namely, Alfalfa (Bromoviridae, Alfamovirus genus), Medicago sativa amalga- virus 1 (Amalgaviridae, Amalgavirus), Medicago sativa alphapartitiviruses 1 and 2 (Partitiviri- dae, Alphapartitivirus), Bean leafroll virus (Luteoviridae, ) and Alfalfa virus F (Tymoviridae, ) [37]. By contrast, nine genomes displayed a low identity, at the amino acid level, with their closest relatives (from 36.6% to 57.2% aa identity in the most conserved proteins with their closest relatives; Supplementary Table S4). According to the ICTV species demarcation

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guideline, we propose to classify three of these genomes as novel virus species and tenta- tively named them Hypera postica-associated alphaflexivirus (abbreviated to HpaAV) (Alphaflexiviridae, Platypuvirus; species demarcation is < 80% aa sequence identity in the capsid or polymerase proteins [38]) and Hypera postica-associated iflavirus 1 and 2 (HpaIV1 and HpaIV2) (Iflaviridae, Iflavirus; species demarcation is < 90% aa sequence iden- tity in the capsid proteins [39]) (Supplementary Table S4). HpaIV1 was highly abundant and present in all weevil samples. We designed specific primers, and we could confirm their presence in insect samples by PCR. Five virus sequences belong to virus families with no current species demarcation threshold (Solemoviridae, Sinhaliviridae and Permuto- tetraviridae) and could not be proposed for classification, although they likely represent new species given their low aa identity with their closest relatives (Supplementary Table S4). Finally, we found one contig displaying low sequence identity with RNA viruses that are not yet classified at the family level. We tentatively named this virus the Hypera pos- tica-associated virus 1 (HpaV1) (Supplementary Table S4).

3.3. Virus Phylogenetic Analysis We next built phylogenetic trees using maximum likelihood methods for these nine genomes in order to place them within currently known viral diversity and to explore their possible host range. First, we analysed HpaAV (Alphaflexiviridae, Platypuvirus) and three Hypera postica-associated (Solemoviridae, ). The Alphaflex- iviridae family phylogenetic tree indicates that HpaAV clustered with viruses isolated from plants (Figure 5), but there is no experimental proof that they are plant infecting. The host range of this virus family currently only includes plants and plant-infecting fungi [38]. The Solemoviridae family phylogenetic tree shows that the three H. postica-associated sobemoviruses cluster with groups of viruses isolated from (Figure 6); these insects and beetles are known vectors of sobemoviruses [40]. These results suggest either that HpaAV- and H. postica-associated sobemoviruses could infect alfalfa and their presence in weevils is likely due to trophic contamination and/or that they might be transmitted by weevils. With the exception of the HpaAV that displayed high read counts in some weevil samples (e.g., Hp04, Hp10 and Hp11), these viruses displayed low abundance/absence in alfalfa, which could suggest that their infection level in the plant and/or their prevalence in the crop is probably low (Figures 3 and 4).

Insect-associated alphaflexivirus 1, MN203143 a) 100 Agave 1, MW328740 Hypera postica associated alphaflexi virus b) 94 Phaius virus X, NC_010295 Donkey orchid symptomless virus, NC_022894 96 100 Mint virus X, NC_006948 Platypuvirus Cymodocea alphaflexivirus 1, MW328741 Botrytis virus X, NC_005132 Botryosphaeria dothidea 1, MT103578 Botrexvirus 78 100 Hypera postica associated alphaflexi virus Botrytis virus X, NC_005132 85 Botrexvirus Donkey orchid symptomless virus, NC_022894 Alfalfa virus S, NC_034622 Platypuvirus Vanilla latent virus, NC_035204 , NC_011620 100 82 Arachis pintoi virus, NC_032104 Indian citrus ringspot virus, NC_003093 71 100 Cassia mild mosaic virus, MN031278 Citrus yellow vein clearing virus, NC_026592 73 Phyllanthus potexvirus 1, MW328725 100 Blackberry virus E, NC_015706 Garlic virus A, NC_003375 Euonymus yellow vein virus, NC_035190 100 Shallot virus X, NC_003795 Nerine virus X, NC_007679 100 Garlic virus E, NC_004012 100 Cassava virus X, NC_034375 Garlic virus C, NC_003376 82 Dioscorea potexvirus 1, MW328724 Garlic virus X, NC_001800 59 Potexvirus Galium odoratum potexvirus, MN814316 100 Garlic virus B, NC_025789 White clover mosaic virus, NC_003820 Sclerotinia sclerotiorum debilitation-associated RNA virus, NC_007415 , NC_001812 latent virus, NC_010434 100 Potato aucuba mosaic virus, NC_003632 Yucca alphaflexivirus 1, MW328742 100 Alstroemeria virus X, NC_007408 Agave potexvirus 1, MW328740 98 Potato virus X, NC_011620 Pepino mosaic virus, NC_004067 Bamboo mosaic virus, NC_001642 97 Lettuce virus X, NC_010832 100 Foxtail mosaic virus, NC_001483 Actinidia virus X, NC_028649 100 Turtle grass virus X, NC_040644 70 Malva mosaic virus, NC_008251 78 68 Clover yellow mosaic virus, NC_001753 mosaic virus, NC_001441 100 Allium virus X, NC_012211 Asparagus virus 3, NC_003400 Potexvirus 84 94 Tamus red mosaic virus, NC_016003 86 Asparagus virus 3, NC_010416 91 virus X, NC_011544 Garlic yellow virus, MN059394 82 Hydrangea ringspot virus, NC_006943 Ferula potexvirus 1, MW328726 62 87 72 Cassava common mosaic virus, NC_001658 Cnidium virus X, LC460456 60 Tulip virus X, NC_004322 54 100 Yam virus X, NC_025252 Nandina mosaic virus, AY800279 Potexvirus 68 100 Vanilla virus X, NC_035205 Plantago asiatica mosaic virus, NC_003849 , NC_001748 Strawberry mild yellow edge virus, NC_003794 100 Alternanthera mosaic virus, NC_007731 Lolium latent virus, NC_010434 Lolavirus Senna mosaic virus, NC_030746 Arachis pintoi virus, NC_032104 51 64 Vanilla latent virus, NC_035204 Babaco mosaic virus, NC_036587 98 Potexvirus sp., NC_040842 Allexivirus Alfalfa virus S, NC_034622 70 100 Opuntia virus X, NC_006060 Blackberry virus E, NC_015706 100 Zygocactus virus X, NC_006059 100 Cassia mild mosaic virus, MN031278 , NC_002815 100 Garlic virus C, NC_003376 Pitaya virus X, NC_024458 51 99 Garlic virus X, NC_001800 Schlumbergera virus X, NC_011659 60 100 Garlic virus B, NC_025789 Nerine virus X, NC_007679 Shallot virus X, NC_003795 Phyllanthus potexvirus 1, MW328725 87 Potexvirus 75 Garlic virus E, NC_004012 Euonymus yellow vein virus, NC_035190 89 Garlic virus A, NC_003375 Dioscorea potexvirus 1, MW328724 Yam virus X, NC_025252 Hosta virus X, NC_011544 100 Vanilla virus X, NC_035205 Clover yellow mosaic virus, NC_001753 Mint virus X, NC_006948 Tamus red mosaic virus, NC_016003 100 85 79 , NC_007192 Allium virus X, NC_012211 100 Potexvirus Phaius virus X, NC_010295 Hydrangea ringspot virus, NC_006943 Cassava virus X, NC_034375 59 Cassava common mosaic virus, NC_001658 70 Strawberry mild yellow edge virus, NC_003794 95 Tulip virus X, NC_004322 97 Ferula potexvirus 1, MW328726 100 Nandina mosaic virus, AY800279 100 99 Cnidium virus X, LC460456 Plantago asiatica mosaic v irus, NC_003849 100 Citrus yellow vein clearing virus, NC_026592 Babaco mosaic virus, NC_036587 Indian citrus ringspot virus, NC_003093 100 Mandarivirus Alternanthera mosaic virus, NC_007731 Galium odoratum potexvirus, MN814316 Senna mosaic virus, NC_030746 100 White clover mosaic virus, NC_003820 Cymbidium mosaic virus, NC_001812 Papaya mosaic virus, NC_001748 98 97 Potexvirus Potato aucuba mosaic virus, NC_003632 Opuntia virus X, NC_006060 100 Pepino mosaic virus, NC_004067 Schlumbergera virus X, NC_011659 100 99 Alstroemeria virus X, NC_007408 Cactus virus X, NC_002815 95 58 90 Lettuce virus X, NC_010832 Pitaya virus X, NC_024458 , NC_001441 100 99 Actinidia virus X, NC_028649 100 0.5 Malva mosaic virus, NC_008251 54 Asparagus virus 3, NC_010416 1.0 100 Asparagus virus 3, NC_003400 Figure 5. Maximum likelihood phylogenetic tree based on (a) the RNA-dependent RNA polymerase protein of 71 al- phaflexiviruses, and (b) on the capsid protein of 61 alphaflexiviruses. The virus reported in this study is marked in red.

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The tree was mid-point rooted. Bootstrap values (100 replicates) superior to 50% are indicated at each node. Scale bar corresponds to amino acid substitutions per site.

Poinsettia latent virus, NC_011543 Polemovirus Ginkgo biloba sobemo-like virus, MN831440 100 Hubei sclerotinia RNA virus 1, MK889164 68 Physalis rugose mosaic virus, MK681145 79 Solanum nodiflorum mottle virus, NC_033706 57 85 Sobemovirus Velvet tobacco mottle virus, NC_014509 , NC_002618 95 38 Imperata yellow mottle virus, NC_011536 98 , NC_001575 Lucerne transient streak virus, NC_001696 99 Subterranean clover mottle virus, NC_004346 52 Turnip rosette virus, NC_004553 68 Pistacia sobemo-like virus, MT334602 Papaya lethal yellowing virus, NC_018449 99 Cymbidium chlorotic spot virus, KR996515 Artemisia virus A, NC_017914 99 Ryegrass mottle virus, DQ481606 100 Rottboellia yellow mottle virus, NC_027198 Sowbane mosaic virus, NC_011187 79 Soybean thrips sobemo-like virus 11, MW039356 72 Southern cowpea mosaic virus, NC_001625 84 Soybean yellow common mosaic virus, NC_016033 100 Southern bean mosaic virus, MN326873 74 Sesbania mosaic virus, NC_002568 Hypera postica associated sobemovirus 1 100 Hypera postica associated sobemovirus 2

90 Soybean thrips sobemo-like virus 4, MW023866 100 Neohydatothrips associated sobemo-like virus 1, MN714676 63 Scaphoideus titanus sobemo-like virus 2, MN982391 70 Teucrium fruticans sobemo-like virus, MN831443 Scaphoideus titanus sobemo-like virus 1, MN982389 Skokie sobemo-like virus, MT747990 Soybean thrips sobemo-like virus 5, MW023867 100 Gerbera anandria sobemo-like v irus, MN831441 Sobemovirus Cycas revoluta sobemo-like virus, MN831442 61 100 Teucrium fruticans sobemo-like virus, MN831444 100 Leveillula taurica associated sobe mo-like virus 1, MN609861 89 Stapleton virus, MN167489 100 Mycroft virus, MN167478 69 Amygdalus persica sobemo-like virus, MN831439 100 Mlepnos solemo-like virus, MT129763 54 Soybean thrips sobemo-like virus 8, MW039354 96 Hypera postica associated sobemovirus 3 94 59 Soybean thrips sobemo-like virus 3, MW023865 100 Soybean thrips sobemo-like virus 6, MW039352 Xi'an sobemo-like virus, MT757478 Rosadiaz solemo-like virus, MT129761 95 82 Jeffords solemo-like virus, MT129762 99 Soybean thrips sobemo-like virus 7, MW039353 Soybean thrips sobemo-like virus 9, MW039355 92 55 Plasmopara viticola lesion as sociated sobemo-like 1, MN551127 94 Yongsan sobemo-like virus 1, MH703049 100 Aedes sobemo-like virus, MT896210 79 Guangzhou sobemo-like virus, MT361057 1.0 Figure 6. Maximum likelihood phylogenetic tree based on the RNA-dependent RNA polymerase protein of 54 sobe- moviruses. Viruses reported in this study are marked in red. The tree was mid-point rooted. Bootstrap values (1000 repli- cates) superior to 50% are indicated at each node. Scale bar corresponds to amino acid substitutions per site.

We further analysed the phylogenetic position of the five novel genomes assigned to insect-infecting virus families. HpaIV1 and HpaIV2 are members of the family Iflaviridae (genus Iflavirus), which comprises viruses that only infect arthropods. Both iflaviruses clustered with viruses discovered in Coleoptera (Aulacophora lewisii and Lampirys nocti- luca), their closest relative being Aulacophora lewisii iflavirus 1 that was isolated from a beetle species (Figure 7). Similarly, Hypera postica-associated permutotetravirus clus- tered in the family Permutotetraviridae, which is composed of two genera with two virus species infecting lepidopteras recognised to date by the ICTV (Figure 8). It is noteworthy that other currently unclassified permutotetraviruses have been isolated from flies (Dro- sophila melanogaster) and leafhoppers (Scaphoideus titanus), suggesting that the genetic di- versity and the host range of this virus family are probably more diverse than currently recognised. Hypera postica-associated sinaivirus is placed at the base of the three species members of the Sinaivirus genus (Sinhaliviridae) currently reported to infect bees (family Apidae; Supplementary Figure S7). Finally, we found one taxon, HpaV1, clustering with unclassified (+) ssRNA viruses, whose closest relatives belong to the order. Like members in this virus or- der (e.g., families Iflaviridae and ), HpaV1 encodes for a long single polypro- tein (12,204 nt in length). The closest relatives of this virus are the Wuhan house centipede virus 3 (NC_033458) and the Diabrotica undecimpunctata virus 1 (MN646770), which have been isolated recently from two arthropod species, including the house centipede (Scutigera coleoptrata) and the spotted cucumber beetle (Diabrotica undecimpunctata). Since these viruses all infect arthropods, we speculate that HpaV1 likely infects H. postica (Sup- plementary Figure S8).

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Vespa velutina associated ifla-like virus, MN565044 100 Culex Iflavi-like virus 4, NC_040574 100 Aedes Ifla-like virus, MT577805 100 Culex Iflavi-like virus 1, NC_040646 100 Armigeres iflavirus, NC_036585 100 Yongsan iflavirus 1, NC_040587 Reineckia carnea iflaviridae, MN823679 100 La Jolla virus, NC_027128 100 Lygus lineolaris virus 1, NC_038301 Fitzroy Crossing iflavirus 1, MT498832 Sacbrood virus, NC_002066 Halyomorpha halys virus, NC_022611 Infectious flacherie virus, NC_003781 100 100 Spodoptera exigua iflavirus 1, NC_016405 100 Opsiphanes invirae iflavirus 1, NC_027917 Solenopsis invicta virus 11, MH727528 99 Lampyris noctiluca iflavirus 1, MH620811 66 Spodoptera exigua iflavirus 2, NC_023676 100 100 Diamondback moth iflavirus, NC_034384 100 Ectropis obliqua picorna-like virus, NC_005092 100 Perina nuda virus, NC_003113 Plasmopara viticola lesion associated iflavirus 1, MN551117 75 Xi'an Ifla-like virus, MT757479 100 Ganwon-do Ifla-like virus, MT757509 100 83 Haemaphysalis flava iflavirus, LC483655 100 Ixodes holocyclus iflavirus, KY020412 100 Iflavirus IricIV-5, MT050464

100 Iflavirus IricIV-2, MT008331 Ixodes scapularis iflavirus, LC094426 Iflavirus IricIV-1, MT008330 Soybean thrips iflavirus 1, MT195546 100 Teucrium fruticans iflaviridae, MN823680 99 98 Thrips tabaci associated iflavirus 1, MN714668 100 Frankliniella occidentalis assoc iated iflavirus 1, MN714670 100 88 Soybean thrips iflavirus 2, MT195547 Chequa iflavirus, NC_036389 96 Dinocampus coccinellae paralysis virus, NC_025835 100 Lysiphlebus fabarum rna-virus type B, MK682510 100 Lysiphlebus fabarum rna-virus type A, MK682509 72 Aedes vexans iflavirus, MN314969 100 Isahaya Culex iflavirus, LC5 13835 Varroa destructor virus 2, NC_040601 93 Yonago Culex iflavirus, LC513836 99 Lampyris noctiluca iflavirus 2, MH620812 100 Aulacophora lewisii iflavirus 1, MT860988 77 Hypera postica associated iflavirus 2 100 Hypera postica associated iflavirus 1 Langfang leafhopper iflavirus, MN866905 100 Amygdalus persica iflaviridae, MN823678 68 Nilaparvata lugens ho neydew virus-2, NC_021566 100 Nilaparvata lugens honeydew virus 1, NC_038302 100 Sogatella furcifera honeydew virus, MG818986 100 Laodelphax striatellus picorna-like v irus 2, NC_025788 100 Laodelphax striatella honeydew virus 1, NC_023627 Brevicoryne brassicae virus - UK, NC_009530 92 73 Pityohyphantes rubrofasciatus iflavirus, NC_034217 Soybean thrips iflavirus 3, MT195548 96 Slow bee paralysis virus, NC_014137 100 Bat iflavirus, NC_033823 100 Moku virus, NC_031338 100 52 100 Vespa velutina Moku virus, MF346349 Euscelidius variegatus virus 1, NC_032087 100 Graminella nigrifrons virus 1, NC_026733 100 Psammotettix alienus iflavirus 1, NC_040724 100 Scaphoideus titanus iflavirus 1, MN982380 100 74 Scaphoideus titanus iflavirus 2, MN982387 King virus, NC_031749 Nilaparvata lugens honeydew virus-3, NC_021567 83 100 Laodelphax striatellus iflavirus 1, MG815140 66 Formica exsecta virus 2, NC_023022 100 100 Varroa destructor virus 1, NC_006494 100 Deformed wing virus, NC_004830 84 Kakugo virus, AB070959 Rondonia iflavirus 1, MN560635 100 Helicoverpa armigera iflavirus, NC_033619 100 Heliconius erato iflavirus, NC_024016 100 Lymantria dispar iflavirus 1, NC_024497 Bombyx mori iflavirus, NC_027713 100 76 Thaumetopoea pityocampa iflavirus 1, NC_026250 81 Antheraea pernyi iflavirus, NC_023483 100 Antheraea mylitta iflavirus, MW115117

0.5 Figure 7. Maximum likelihood phylogenetic tree based on the polyprotein of 81 iflaviruses. Viruses reported in this study are marked in red. The tree was mid-point rooted. Bootstrap values (100 replicates) superior to 50% are indicated at each node. Scale bar corresponds to amino acid substitutions per site.

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Euprosterna elaeasa virus, NC_003412

55 Beihai permutotetra-like virus 2, NC_032122 100 Hubei permutotetra-like virus 10, NC_033245

Hubei permutotetra-like virus 8, NC_033216

50 Hubei permutotetra-like virus 9, NC_033251

Scaphoideus titanus permutotetra-like virus 1, MN982394

Hubei permutotetra-like virus 4, NC_033140

Hubei permutotetra-like virus 3, NC_033179 100 64 Culex permutotetra-like virus, LC505019

Hubei permutotetra-like virus 1, KX883448 100

100 Hubei permutotetra-like virus 1, KX883446

86 Hubei permutotetra-like virus 1, NC_033105 100

Sanxia permutotetra-like virus 1, NC_033188

100 Hubei permutotetra-like virus 2, NC_033121 100 Hubei permutotetra-like virus 2, KX883455

Hubei permutotetra-like virus 6, NC_033114

Hubei permutotetra-like virus 5, NC_033233 100 55 Hypera postica associated permutotetravirus

Hubei permutotetra-like virus 7, NC_033132

Smithfield permutotetra-like virus, MN784080 100 Smithfield permutotetra-like virus, MN784079

52 Hubei permutotetra-like virus 11, NC_033097

93 Drosophila A virus, NC_012958

100 Vespa velutina associated permutotetra-like virus 2, MN565052 98 Vespa velutina associated permutotetra-like virus 1, MN565051

0.5 Figure 8. Maximum likelihood phylogenetic tree based on the RNA-dependent RNA polymerase protein of 25 permuto- tetraviruses. The virus reported in this study is marked in red. The tree was mid-point rooted. Bootstrap values (1000 replicates) superior to 50% are indicated at each node. Scale bar corresponds to amino acid substitutions per site.

4. Discussion The aim of this study was to explore the diversity of viruses circulating between an insect pest and its host plant using viral metagenomics. We collected samples of the alfalfa weevil pest and its host plant, both from a crop and a nearby meadow. The viral sequences we found in weevil larvae represented arthropod viruses belonging to nine families and plant/fungal viruses belonging to 11 families, while only plant viruses belonging to four families were detected in alfalfa. Viral sequences were non-uniformly distributed across samples: some were highly abundant in most samples, while others displayed more viral richness but a low number of reads. Although the method we used supposedly enables a substantial viral enrichment in libraries compared to other viral metagenomic studies [41– 43], several methodological caveats can be indicated. First, our sampling effort and the sequencing depth were not sufficient to recover a comprehensive representation of the diversity of viruses that circulates in the sampled population of alfalfa weevils. This as- sumption is supported by the virus family accumulation curve, expected to plateau when only rare virus families remain to be described. Here, the accumulation curve did not reach an asymptote, thus supporting that we did not capture the full virus diversity and that the sampling effort should be increased for this aim. However, we cannot exclude that the stringent abundance threshold we determined also eliminated rare virus families, also adding a bias in the analysis of viral diversity. Second, it is noticeable that most of the virus sequences reported here correspond to small RNA viruses (16 out of 20), with viral particle ranging in size from 30 to 80 nm, except for the members of the Alphaflexiviridae and Phenuiviridae families that have larger viral particles. The majority of these virus families correspond to (+) ssRNA viruses. We found reads classified in only three families of DNA viruses, two belonging to virus families whose host range comprises plants (Geminiviridae and Caulimoviridae) and one to a family

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comprising arthropod-infecting viruses (Parvoviridae), although they represent low num- bers of reads so we could not reconstruct any of their full coding sequences. We cannot ex- clude that the greater abundance of small viruses could be due to the sample preparation method, based on virus-like particle enrichment and involving filtration and ultracentrifu- gation steps that are known to bias virome composition toward small viruses [44,45]. Meth- odological biases must be kept in mind when analysing the richness of viral communities. The results we obtained from this field screening allowed us to identify arthropod- and plant/fungus-infecting viruses that potentially correspond to nine novel virus species. Interestingly, arthropod viruses were more divergent from their closest relatives than the putative plant/fungus-infecting viruses that we found in weevils. Moreover, six plant-in- fecting virus species described in this paper belong to already known species. These find- ings illustrate the poor knowledge we have of the diversity of arthropod-infecting viruses including from “well known” insect pests that threaten major cropping systems [1,46]. The presence of arthropod-infecting viruses in weevil larvae does not provide evidence that these viruses are indeed infectious for weevils [47]. An argument against this would be the close relatedness of these viruses to other virus species isolated from beetles. Two novel RNA virus species (i.e., HpaIV1 and HpaV1) were relatively abundant in most of the insect samples, suggestive of a covert or an overt infection. Interestingly, the alfalfa weevil larvae complete their whole development on the same plant. This biological feature would make virus horizontal transmission and spreading to the population poorly efficient, limited to the few individuals living on the same plant, which is an argument for persistent infections and virus vertical transmission. Indeed, iflaviruses can often result in covert, sublethal infections in their insect hosts [48], although some can be lethal [49]. Cov- ert infections often rely on the vertical transmission of the pathogen, which makes its transmission depend on the reproduction of their host [50]. Many insect viruses have mixed modes of transmission, and switching from one mode to another may occur, de- pending on environmental stressors and host life strategies [51]. Integrating viruses with mixed transmission modes in pest management requires understanding the molecular mechanisms involved in the disease, including the impact of co-infection. In addition to arthropod viruses, this dataset highlights that the weevil samples also contain a higher diversity, although a low abundance, of putative plant/fungal viruses compared to plant samples. The richness of plant/fungal viruses in weevils can be at- tributed to sampling bias, both in the number of samples from pooled individuals (14 weevils versus two plant samples) and the number of individuals per sample (5–10-fold more individuals in weevil samples than in plant samples). However, the higher richness of plant viruses in insects can also be explained by (1) trophic accumulation, as already reported in dragonflies [52], or by (2) their transmission using insect vectors, as already described in sobemoviruses [40]. If we assimilate the virome of one plant to one weevil (plausible considering the “sedentariness” of larvae), we can extrapolate that weevil sam- ples (14 samples, each made of >50 individuals) correspond roughly to the screening of 700–1000 plant equivalents in the area. Thus, we can estimate that trophic accumulation could increase plant virus richness in weevils by a factor >5 compared to alfalfa sampling. Hence, sampling phytophagous insects, particularly vector species, can provide a better proxy of phytopathogenic virus richness in their host plant than sampling plants, and should be included in surveys of plant pathogenic viruses in crops [53–55]. Today, viral metagenomics and high-throughput sequencing allow us to fully inte- grate viruses into the dynamics of agrosystems. Such approach allows us to detect infections in insect communities that have largely been ignored to date, in particular, concerning in- sects of concern, such as pests and vectors. Major challenges remain ahead, both conceptual and methodological: first, in basic , to integrate co-infections and virus communities into the understanding of the functioning of agrosystems; second, in applied virology, to better integrate “beneficial” viruses into sustainable management programmes.

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Supplementary Materials: The following are available online at www.mdpi.com/1999- 4915/13/5/791/s1. Supplementary Figure S1: Rarefaction curves of viral communities at the family level. Supplementary Figure S2: Proportion of reads classified into arthropod-infecting families (red) and plant or fungus-infecting families (blue). Hp: Hypera postica samples; Ms: Medicago sativa samples. Supplementary Figure S3: Maximum likelihood phylogenetic tree based on (a) the first segment and on (b) the third segment of alfalfa mosaic virus genomes, including the one isolated during this study (in red). The tree was mid-point rooted. Bootstrap values (1000 replicates) superior to 50% are indicated at each node. Scale bar corresponds to nucleotide substitutions per site. Sup- plementary Figure S4: Maximum likelihood phylogenetic tree based on (a) the RNA-dependent RNA polymerase protein and on (b) the capsid protein of 31 amalgaviruses, including the one iso- lated during this study (in red). The tree was mid-point rooted. Bootstrap values (1000 replicates) superior to 50% are indicated at each node. Scale bar corresponds to amino acid substitutions per site. Supplementary Figure S5: Maximum likelihood phylogenetic tree based on (a) the first segment and on (b) the second segment of alfalfa infecting partitiviruses, including the ones isolated during this study (in red). The tree was mid-point rooted. Bootstrap values (1000 replicates) superior to 50% are indicated at each node. Scale bar corresponds to nucleotide substitutions per site. Supplemen- tary Figure S6: Maximum likelihood phylogenetic tree based on the whole genome of 6 Bean leafroll virus isolates, including the one isolated during this study (in red). The tree was mid-point rooted. Bootstrap values (1000 replicates) superior to 50% are indicated at each node. Scale bar corresponds to nucleotide substitutions per site. Supplementary Figure S7: Maximum likelihood phylogenetic tree based on (a) the RNA-dependent RNA polymerase protein and on (b) the capsid protein of 38 sinaiviruses. The virus reported in this study is marked in red. The tree was mid-point rooted. Boot- strap values (1000 replicates) superior to 50% are indicated at each node. Scale bar corresponds to amino acid substitutions per site. Supplementary Figure S8: Maximum likelihood phylogenetic tree based on the polyprotein of 9 unclassified RNA viruses. The virus reported in this study is marked in red. The tree was mid-point rooted. Bootstrap values (1000 replicates) superior to 50% are indi- cated at each node. Scale bar corresponds to amino acid substitutions per site. Supplementary Table S1: Collected samples and their associated metadata. Supplementary Table S2: Viral read counts contingency tables (family scale). The reads were classified at the family level using DIAMOND taxonomic assignment, as described in the materials and methods section. Supplementary Table S3: Viral read counts contingency tables (species scale). The reads were classified at the family level using mapping (BOWTIE2), as described in the materials and methods section. Supplementary Ta- ble S4: List of viruses reported in this study, associated information and accession numbers. Author Contributions: Conceptualization, S.F., R.F., P.R. and M.O.; methodology, S.F., A.A.-L., M.K., D.F., E.F., M.F. and P.R.; validation, S.F., A.A.-L. and M.O.; formal analysis, S.F, A.A.-L. and M.O.; investigation, S.F. and M.O.; resources, S.F. and M.O.; data curation, S.F. and A.A.-L.; writ- ing—original draft preparation, S.F. and M.O.; writing—review and editing, S.F., D.F., P.R. and M.O.; supervision, S.F., R.F. and M.O.; project administration, M.O.; funding acquisition, M.O. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by a scholarship (S.F.) from the National Institute of Agro- nomical Research (INRA) and from the University of Montpellier and by the European Union’s Horizon 2020 research and innovation program (A.A-L. and M.O.) under grant agreement no. 773567. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The reconstructed genomes were deposited in GenBank under the accession numbers MW676127 to MW676142. The metagenomic datasets used in this study were deposited on the SRA database with the accession numbers SRR13786451 to SRR13786466 under the BioProject PRJNA704818. Acknowledgments: We are grateful to the Domaine du Département de Restinclières and the Département de l’Hérault for providing us with the full access to the domain for sampling insects and plants. Conflicts of Interest: The authors declare no conflicts of interest.

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