Dynamics of small RNAs in a red-fruited wine grape cultivar infected with Grapevine red blotch virus

Dynamics of small RNAs in a red-fruited wine grape cultivar infected with Grapevine red blotch virus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dynamics of small RNAs in a red-fruited wine grape cultivar infected with Grapevine red blotch virus Noah Ault, Shuchao Ren, David Payne, Yongfang Li, Asha Srinivasan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4803716/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2025 Read the published version in BMC Genomics → Version 1 posted 10 You are reading this latest preprint version Abstract Background Red blotch disease, caused by Grapevine red blotch virus (GRBV, genus Grablovirus , family Geminiviridae ), negatively impacts vine health, fruit yield, and quality, leading to substantial economic losses to growers. While recent studies have enhanced our understanding of the epidemiology of GRBV and its effects, little is known about the molecular basis of the host-virus interactions. Since small RNAs (sRNAs) are known to play a central role in host-virus interactions, this study was undertaken to investigate sRNA dynamics in leaves and berries at two phenological stages (asymptomatic pre- and symptomatic post-veraison) of GRBV-infected grapevines ( Vitis vinifera cv. Merlot). Results Among the 140 microRNAs (miRNAs) detected, 41 isoforms belonging to 18 miRNA families exhibited significant differential expression in response to GRBV infection. Furthermore, 50 miRNAs showed differential expression in samples from pre- and post-veraison stages. A total of 58 conserved and 41 novel targets for known V. vinifera miRNAs were validated using degradome sequencing data from leaf samples of pre- and post-veraison stages. Viroid-derived small-interfering RNAs (vdsiRNAs) specific to Grapevine yellow-speckle viroid-1 and Hop stunt viroid were also identified in all samples, while virus-derived siRNAs (vsiRNAs) specific to GRBV were present only in GRBV-positive samples. The vsiRNAs predominantly ranged from 19 to 24 nucleotides (nt), with the 21nt size being the most abundant. Mapping vsiRNAs across the GRBV genome revealed an uneven distribution, with vsiRNA-generating hotspots predominantly located in the V3 ORF. Of the 83 most abundant vsiRNAs, targets within the grapevine transcriptome were identified for eight of them. Significantly higher levels of HSVd RNAs were observed in GRBV-positive samples compared to GRBV-negative samples, suggesting a potential synergistic interaction between the two pathogens. Conclusions The predominance of 21-nt long vsiRNAs, as well as the predominance of those mapping to the V3 ORF compared to other ORFs, provide insight into both the biogenesis and methods of action of GRBV vsiRNAs. Target validations of vsiRNAs and differentially expressed miRNAs are indicative of pathways and mechanisms which may lead to the expression of Grapevine red blotch disease symptoms. This research serves as a foundation for future studies on the molecular interactions in this plant-geminivirus pathosystem. Grapevine Vitis vinifera Grapevine red blotch virus Geminiviridae Viroid small RNA microRNA microRNA Target High Throughput Sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Small RNAs (sRNAs) are the central component of all RNA silencing pathways in plants due to their regulatory roles in a multitude of developmental and physiological processes, as well as in response to biotic and abiotic stresses [ 1 , 2 , 3 , 4 , 5 ]. Among the different classes of sRNAs characterized in virus-infected plants, microRNAs (miRNAs) and small-interfering RNAs (siRNAs) are important contributors to both transcriptional (TGS) and post-transcriptional gene silencing (PTGS). In addition, plants have evolved sRNA-mediated silencing, or RNA interference (RNAi) as a natural defense strategy to counter viral infections [ 6 , 7 ]. RNAi is an evolutionarily conserved antiviral mechanism mediated by virus-derived siRNAs (vsiRNAs) [ 8 , 9 ]. Multiple studies have demonstrated that viral infections in plants are closely tied to the accumulation of vsiRNAs, which are consequently able to directly target and silence viral RNA [ 10 , 11 , 12 ]. Unlike siRNAs, miRNAs originate solely from an organism’s nuclear genome, either in dedicated MIR genes, or within introns of other specific genes. Following transcription, miRNA precursors form a stem loop structure and are processed by a Dicer or Dicer-like protein into an miRNA duplex. This duplex is then exported from the nucleus, after which it associates with an argonaut protein forming the miRNA-induced silencing complex (miRISC). The miRNA guides the miRISC to a complementary segment of a target plant mRNA. The miRISC then initiates PTGS and the resulting degradation of the target mRNA [ 13 ]. Sequencing of the plant degradome has been highly effective at identifying the resulting fragments of this degradation and can be used to validate miRNA targets within plant transcriptomes [ 14 , 15 , 16 , 17 ]. Extensive research into and characterization of miRNAs, including target identification, have revealed that miRNAs are not only important regulators in plant processes ranging from development to environmental stress responses [ 18 , 19 ], but that miRNA expression itself can vary significantly in response to different conditions, including viral disease [ 20 , 21 ]. Grapevine red blotch disease (GRBD), caused by Grapevine red blotch virus (GRBV, genus Grablovirus and family Geminiviridae ) [ 22 , 23 , 24 , 25 , 26 ] is an economically important viral disease affecting wine grapes ( Vitis vinifera L.) in different grapevine-growing regions [27, and cited references]. The disease affects both the yield and quality of grapes produced by infected vines leading to significant reduction in income to growers [ 28 , 29 , 30 , 31 ]. Virus-infected vines produce contrasting symptoms in red- and white-fruited V. vinifera cultivars. In red-fruited cultivars, initial symptoms on leaves consist of small, irregular, red-colored areas between major veins that expand and coalesce as the season advances to become reddish or reddish-purple irregular blotches. In addition, some red-fruited cultivars show red-colored primary, secondary, and tertiary veins. Interestingly, despite GRBV infecting grapevines systemically and being detectable throughout the season, visual symptoms only begin to appear after veraison (the onset of ripening). Following veraison in infected vines, disease symptoms begin to appear on mature leaves at the basal portion of the shoots. As the season progresses, symptoms spread, developing on leaves positioned higher on shoots as well. In contrast, white-fruited cultivars show mild symptoms that are less conspicuous and can involve irregular chlorotic areas between major veins, sometimes accompanied by necrosis around the leaf margins. GRBV has a circular single-stranded DNA genome of around 3,200 nucleotides and encodes six clearly defined open reading frames (ORFs), with three overlapping ORFs (C1, C2, and C3) in the complementary sense and the other three (V1, V2, and V3) in the viral sense (Fig. 1 ) [ 32 , 33 ]. ORF V1 encodes the predicted viral coat protein (CP), although CP was not detected in infected plants nor virions observed via electron microscopy. The V2 and V3 ORFs are suggested to encode movement proteins based on similarities with other monopartite geminiviruses [ 34 ]. The C1 ORF was predicted to encode RepA, and a spliced transcript encoded by C1 and C2 was predicted to encode Rep. The function of the C3 ORF, which is located internal to and within the same frame as the C1 ORF, is yet to be determined. Recently, the proteins encoded by the C2 and V2 ORFs were identified as suppressors of post-transcriptional gene silencing [ 35 ]. Figure 1 : Grapevine red blotch virus (GRBV) Genome Locations of viral (green) and complementary (red) sense ORFs in GRBV genome. Created with SnapGene software [ 36 ] and the GRBV RefSeq annotation [ 22 ]. In recent years, many studies utilizing sRNA profiling have been conducted in virus-infected plants to identify both plant- and virus-derived sRNAs and gather information on associated plant-viral interactions [ 37 , 38 , 39 , 40 ]. These studies were useful not only for building up an information base that can be used to interpret and contextualize various molecular interactions between plants and their viral pathogens, but also for discovering potential tools for treatment of viral plant diseases and the further molecular characterization and population genetics of viral/sub-viral genomes [ 9 ]. Many of these studies focused on annual plants in a defined set of controlled environments. This type of work has only more recently begun to be performed utilizing perennial crops such as grapevine [ 37 , 41 ]. Unlike annual crops, the dynamics of sRNAs in virus-infected perennial crops are influenced by seasonal and environmental changes. Although recent studies have advanced knowledge on the epidemiology and impacts of GRBV [ 27 , 42 , 43 , 44 ], little is known regarding compatible virus-host interactions at the molecular level. Because red blotch disease symptoms are produced in a phenological stage-specific manner during each season, it can be hypothesized that the dynamics of virus-host interactions are distinct between asymptomatic pre- and symptomatic post-veraison stages. Among these diverse molecular interactions, investigating the regulatory roles of small RNAs (sRNAs) with regard to both infection status and phenological stage could lead to deeper insights into the molecular pathways contributing to symptom development in grapevines grown under natural conditions. In this study, high-throughput sRNA sequencing was used to analyze the sRNAome in two different tissue types across two phenological stages from GRBV-infected grapevines ( Vitis vinifera cv. Merlot) from a commercial vineyard. Both plant miRNAs and viral/viroidal siRNAs were analyzed to identify patterns in abundance and differential expression, as well as to validate the transcriptomic targets of key sRNAs to gain insight into the molecular interactions underlying symptom expression. These findings provide a foundation for future research to further study the role of sRNA-induced silencing mechanisms in GRBV-grapevine interactions. Materials and Methods Plant Material Leaf and berry samples were collected from a commercial vineyard (45°52’07”N, 119°46’30”W) planted in 2008 with own-rooted Vitis vinifera cv. Merlot (clone 15) vines and maintained by the grower using standard viticultural practices. Based on grower feedback, the vineyard block was planted with virus-infected cuttings resulting in the introduction of virus into the block at the time of planting. Grapevines were selected for this study in pairs with each pair consisting of one vine showing GRBD symptoms and an adjacent, asymptomatic vine in the same row. They were selected such that each pair of grapevines is from a different row within the vineyard block. Candidate vines were tested initially for GRBV [ 45 ] to ensure that the symptomatic vines were positive for GRBV and that the non-symptomatic vines were negative. Three pairs of vines, with each pair consisting of one symptomatic, GRBV-positive vine and one asymptomatic, GRBV-negative vine, were selected for this study. Both leaf and berry samples were collected separately from individual vines at pre-veraison (early July 2015) and post-veraison (September 2015). This resulted in eight distinct sampling categories based on the combination of timepoint, tissue type, and infection status. These sampling categories have been denoted according to the abbreviations, P (pre-veraison), PO (post-veraison), L (leaves), B (berries), H (GRBV-negative), and D (GRBV-positive), such that “PLH” would reference pre-veraison leaf samples from GRBV-negative vines. The samples were snap-frozen in liquid N 2 immediately after harvesting and transported in liquid N 2 to maintain the integrity of the samples until they could be stored at -80°C. Thus, individual leaf and berry samples collected from each vine are considered as one biological replicate for downstream applications. Preparation, Sequencing, and Mapping of sRNA and RNA Libraries Small RNAs were extracted from frozen leaf and berry tissues using a mirPremier® microRNA isolation kit (Sigma-Aldrich, MO) by following the protocol provided by the manufacturer. Separate RNA libraries were generated from these samples following the protocol discussed in Alabi et al. [ 41 ] and Li et al. [ 46 ]. The quantity of sRNA and RNA preparations was assessed based on 260nm/280nm OD values using a NanoDrop 2000c spectrophotometer. The integrity of sRNA and RNA preparations was measured using the 2100 Bioanalyzer system (Agilent Technologies, SantaClara, CA). Preparations with 260/280 absorbance ratio from 1.8-2.0 and an RNA integrity number (RIN) higher than 7.0 were used for library preparations. High quality sRNA and RNA samples were shipped to BGI Genomics [ 47 ] for library construction and Illumina 50SE sequencing using the Hiseq 4000 system (RRID:SCR_016386). Subsequently, low quality reads and adapter sequences were removed using Trimmomatic version 0.39 [ 48 ], and read quality was checked with FastQC. V. vinifera miRNAs were identified and mapped to the 12X.v2 grapevine reference genome assembly [ 49 ], alongside several other databases including premiRBase21, Rfam, Silva, TIGR Plant Repeat Databases, and Repbase, as described in Suo et al. [ 50 ]. Following mapping, all miRNAs without a minimum of 10 counts in at least one sample were removed. The remaining miRNAs were normalized to RPTM. Additionally, at this stage, one replicate (PLD2) was removed prior to further analysis due to quality concerns. sRNA reads were also mapped to a database containing all GenBank virus and viroid sequences under 23kb in length [ 51 ]. Two different methods for mapping the sRNA reads to the database were tested. In the first method, reads were first mapped to the Ensembl grapevine genome assembly (version 12X.v2), and then the unmapped sequences were mapped to the viral database. In the second method, the trimmed sequences were mapped directly to the viral database. There was minimal difference in the total number of mapped reads between these two methods. Additionally, there were none-to-minimal reads mapping to the GRBV genome in libraries from samples from GRBV-negative vines, even when reads were mapped directly to the viral database. The second method was ultimately used for all subsequent analyses. Mapping was performed requiring perfect matches and excluding reads which mapped non-specifically. The sRNA reads were also exclusively mapped to the grapevine genome with the same method for the determination of the length profile of sRNAs in the host. sRNA Length Distribution Analysis The abundances of different lengths of sRNAs which mapped to the Vitis vinifera , GRBV, HSVd, and GYSVd genomes were determined using a custom python script. R Statistical Coding Language version 3.6.3 [ 52 ] and the “dplyr”, “car”, “xtable”, and “gridExtra” packages were used for the statistical analysis. Read counts were normalized by sample via the ‘scale’ function. The ‘aov’ function was used to perform two-way ANOVAs looking at the effects of both infection and veraison status on the prevalence of each read length. Levene tests were performed using the ‘leveneTest’ function. Tukey post-hoc analyses were performed on all of the ANOVAs using the ‘TukeyHSD’ function. Results were organized and exported using the ‘xtable’, ‘rbind’, and ‘grid.table’ functions. Analysis of Viral and Viroidal RNA Abundance The RNA sequencing libraries, which were derived from the same samples as the sRNA sequencing libraries, were mapped to the Genbank viral/viroidal database [ 51 ] using CLC Genomics Workbench, to verify the presence of GRBV, HSVd, and GYSVd, as well as the absence of any other viruses or viroids. CLC Genomics Workbench was also used to assemble the RNA sequencing data de novo . Generated de novo assemblies were identified using NCBI BLAST [ 53 ]. Following de novo assembly, the counts per million mapped reads (CPM) for the de novo assemblies of GRBV, HSVd, and GYSVd-1 were calculated. Two-way ANOVAs, accompanied by Levene’s tests and Tukey’s post-hoc tests, were performed in R, using the same functions and packages described above, to compare viral and viroidal RNA abundance across sampling categories (leaf and berry, pre- and post-veraison, infected and healthy). Grapevine Degradome Sequencing and Target Identification Analysis The protocols described by Li et al. [ 15 , 54 ] were used to prepare and sequence four degradome libraries, representing GRBV-positive and GRBV-negative leaf samples taken at both pre- and post-veraison. Trimmomatic version 0.39 [ 48 ] was used to prepare the four libraries for analysis. The first six nucleotides of each read were cropped. Reads shorter than 23 nucleotides were excluded and reads over 28 nucleotides in length were trimmed down to 25 nucleotides. Additionally, an overrepresented sequence (> 25%) which mapped to V. vinifera chloroplastic DNA was removed prior to analysis. CleaveLand version 4.5 [ 55 ] was used to identify targets in the degradome libraries. GSTAr version 1.0 and RNAplex version 2.4.17 [ 56 ], Perl version 5.26.1 [ 57 ], R version 3.2.2 [ 58 ], Samtools version 1.1.0 [ 59 ], and Bowtie version 1.0.0 [ 60 ] were dependencies utilized within CleaveLand. The CleaveLand script was modified with regard to the section involving Bowtie, such that the default parameters (-k 1 -best) were altered (-a -m 12 -best -strata) to allow for reads to map to multiple loci, so long as they did not map to more than twelve separate loci. The cDNA annotation was obtained from Ensembl Plants version 54 [ 61 ]. The TAS3 sequences LOC100244732 and LOC104879803 were added manually from NCBI. Grape miRNA sequences were obtained from miRBase [ 62 ]. The vvi-tas3 sequences were derived from LOC100244732 and LOC104879803 based on their homology to known 21nt tas3 sequences. Additionally, a series of custom Python version 3.8.3 [ 63 ] scripts was used to parse the sRNAs which mapped to the GRBV genome, record the fifty most abundant unique sequences from each GRBV-positive sample, and combine them into a non-redundant list, which was then used in the CleaveLand pipeline. This was chosen as an alternative to running all of the unique mapped sequences due to time and computational constraints. In total, 83 unique sRNA sequences were included. Custom Python version 3.8.3 [ 63 ] scripts were used to determine statistics such as the 9nt, 10nt, and 11nt cleavage counts, the degradome peak ranks, valid reads, the total number of degradome reads per transcript, and percent reads valid. Due to the lack of gene descriptions on the V. vinifera annotation used, BLASTp (from BLAST + version 2.10.1) was used to match proteins from V. vinifera with those from A. thaliana with an e-value cutoff of 1e-10. Protein sequences for both species were obtained from Ensembl Plants version 54. Each V. vinifera gene was assigned a homologue from A. thaliana based on the strongest BLAST hit (if any). Annotations for the A. thaliana genes were derived from the Ensembl Plants version 54 annotations. For genes without A. thaliana homologues, target gene functions were determined through use of NCBI’s conserved domain search function. miRNA Differential Expression Analysis The differential expression analysis was performed in the R Statistical Coding Language version 3.6.3 [ 52 ] using the “edgeR” (version 3.28.1) package by Bioconductor [ 64 ], with a generalized likelihood ratio model. Prior to the analysis, miRNAs that did not have more than one read in at least six samples were removed. Instead of the RPTM normalized read counts, the raw read counts were used in conjunction with the ‘calcnormfactors’ function in edgeR. The ‘DGEList’, ‘glmFit’, and ‘glmLRT’ functions were used to conduct the differential expression analysis, which was performed as a series of pairwise comparisons between groups utilizing the ‘makecontrasts’ function. To determine significance, a p-value cutoff of |1.0| was used. Mapping sRNAs to GRBV Genome CLC Genomics Workbench was used to map the sRNA reads which had been previously mapped to the GRBV genome in the viral database to a gene-annotated version of the GRBV genome. This was done, as opposed to utilizing the entirety of the trimmed reads, to prevent the inclusion of the previously excluded non-specific reads. The results of this mapping were analyzed using the “edgeR” package by Bioconductor [ 64 ] in R. Reads were normalized using the ‘calcnormfactors’ function. Reads mapping to the GRBV ORFs were analyzed for differential expression using the ‘DGEList’, ‘glmFit’, amd ‘glmLRT’ functions. This was done via a series of pairwise comparisons between experimental groups set up using the ‘makecontrasts’ function. To determine significance, an FDR cutoff of |0.2| was used. Results RT-PCR Assays of Grapevine Samples The petioles of mature leaves were collected from Merlot vines exhibiting the symptoms of GRBD as well as adjacent, asymptomatic vines and tested for GRBV. Only symptomatic vines tested positive for the virus (GRBV). By contrast, both symptomatic and asymptomatic samples tested positive for two viroids, grapevine yellow-speckle viroid 1 (GYSVd-1) and hop stunt viroid (HSVd). High-throughput sequence analysis of small RNAs described below confirmed the presence of GRBV only in symptomatic vines and two viroid species (HSVd and GYSVd-1) in both symptomatic and non-symptomatic vines. None of the samples tested positive for other viruses or viroids. These results were used to select three symptomatic vines that tested positive for GRBV and three asymptomatic, GRBV-negative vines adjacent to GRBV-positive vines, for a total of six vines. sRNA Sequencing and Mapping A total of 24 sRNA libraries were constructed and sequenced using the Illumina sequencing platform. These 24 libraries represented three biological replicates for leaf and berry samples harvested at pre- and post-veraison stages from symptomatic, GRBV-positive vines and non-symptomatic, GRBV-negative vines. The clean and mapped read numbers in each individual library are shown in Table S1 . Per each of the eight sampling categories, there was an average of roughly 598,000 unique reads (Table S2 ). Across all libraries, there were a total of 560,479,564 raw reads. Following the removal of low-quality reads and trimming for adapter sequences, a total of 23,129,077 clean reads remained. These reads were mapped to the 12X.v2 grapevine reference genome assembly [ 49 ], alongside several other databases (Table S2 ), as described in Suo et al. [ 50 ]. Following the removal of reads shorter than 19 and longer than 24 nucleotides, 2,210,559 reads remained that were successfully mapped to miRNA sequences. After filtering out reads with less than 10 counts present in a single sample, the remaining reads were mapped to 140 unique miRNAs belonging to 42 miRNA families using the methodology described in Suo et al. [ 50 ]. Among the mapped reads, 21 and 23 nt-long reads were the most common (Fig. 2 A & B). Following normalization (RPTM), eight miRNAs had over 1,000 RPTM across all replicates (Fig. 2 C & D). One specific miRNA, miR3634a-3p, had a disproportionately high number of normalized counts relative to the other miRNAs identified. miR3634a-3p had 45,335 RPTM between all replicates, while the next most prevalent miRNA, miR3623a-3p, had 10,406 RPTM (Figure S1 ). Three miRNA families (miR166, miR319, and miR396) were represented by more than ten distinct miRNA isoforms. Six other miRNA families (miR156, miR159, miR162, miR167, miR395, and miR398) were represented by five or more isoforms (Fig. 2 E). Figure 2 : Abundances of miRNAs by length, family, and number of isoforms Length distribution of miRNAs identified from the (A) leaves and (B) berries from GRBV-positive and negative vines collected during pre- and post-veraison. Reads were normalized by RPTM. In leaf samples, 23nt reads were the most abundant during pre-veraison, but 21nt reads were the most abundant during post-veraison. In berries, 21nt reads were the most abundant in samples collected during pre- and post-veraison. Family-wise distribution of miRNAs in (C) leaves and (D) berries from GRBV-negative and GRBV-positive samples collected during pre- and post-veraison. Reads were normalized by RPTM. (E) Number of distinct miRNA isoforms detected from miRNA families. Reads from the miR3634 family were the most abundant in every sampling category except for berry samples at post-veraison from GRBV-positive vines, where miR3623 reads were more abundant. The overabundance of miR3634 family reads was due to the isoform miRNA3634a-3p. This overabundance also contributed to the high 23nt read abundance in leaves. Sampling categories are denoted according to the following abbreviations: pre-veraison [P], post-veraison [PO], leaves [L], berries [B], GRBV-negative [H], GRBV-positive [D]. The trimmed sRNA reads from all 24 libraries were also mapped to a GenBank viral/viroidal database containing all viral and viroidal genomes less than 23kb in length using CLC Genomics Workbench 2021. Mapping parameters were set to exclude any nonspecific results and disallow for mismatches. The results are summarized in Table S3. For reads mapped to the GRBV genome, 25nt and longer reads made up minimal (< 2%) portions of the overall read distribution. 21nt reads were the most common in all sampling categories. Notably, 21nt sRNAs were significantly less abundant in post-veraison berries than in pre-veraison berries (Fig. 3 A). 20nt and 22nt reads showed an inverse relationship, where they were more abundant in post-veraison berry samples. There was also a relatively low abundance of 24nt vsiRNAs compared to 21nt and 22nt vsiRNAs. Figure 3 : Virus- and viroid-derived siRNA abundance by length (A) Length distribution of sRNA reads mapping to GRBV genome with no mismatches and no nonspecific reads. Length distribution for (B) HSVd and (C) GYSVd from leaf (left) and berry (right) samples. Plotted as the percentage of total reads mapped. Significant comparisons were made in R between sampling categories for each sRNA. Asterisks show significant differences (p < 0.05) between sample categories based on veraison in A and infection status in B and C. Significant differences based on tissue type are not shown. sRNA reads ranging from 25–28 nt long are not shown due to low abundance. Sampling categories are denoted according to the following abbreviations: pre-veraison [P], post-veraison [PO], leaves [L], berries [B], GRBV-negative [H], GRBV-positive [D]. Additionally, all samples had substantial quantities of reads that mapped with high coverage to both HSVd and GYSVd-1. Substantial quantities of reads from sRNA libraries from GRBV-positive vines mapped to the GRBV genome with high coverage, while reads from GRBV-negative samples had negligible amounts of reads mapped to GRBV and poor coverage. No other viruses or viroids had substantial read abundance and high coverage in libraries from any samples. As in the GRBV-derived vsiRNAs, reads 25nt and longer constituted a negligible portion of the overall read distributions in both HSVd and GYSVd. In leaves, 21nt reads were the most abundant, and 24nt reads were the second-most (Fig. 3 B & C). In berries, 21nt and 24nt read abundances were roughly equal. There were no notable significant differences between sampling categories in either berries or leaves in HSVd. For GYSVd, 23nt reads were significantly more abundant in post-veraison berries than in pre-veraison berries in GRBV-negative samples. In GRBV-positive samples, there was no significant difference between pre- and post-veraison berries, nor in any of the leaf samples. There was a trend towards 23nt reads being higher in GRBV-positive leaves at post-veraison than at pre-veraison, but this trend was not observed in GRBV-negative samples. Estimation and Analysis of Viral and Viroidal Titer from RNAseq Data 24 RNA libraries were constructed and sequenced from the same samples as the sRNA libraries (unpublished data). The RNA sequencing libraries were mapped to the Genbank viral and subviral database to confirm the presence of GRBV in GRBV-positive samples, its absence in GRBV-negative samples, the presence of GYSVd-1 and HSVd in all samples, and the absence of any other viral or subviral entities in any of the samples. All three genomes were also successfully assembled de novo in all relevant samples, except for one sample (PLH1), in which the HSVd genome was not successfully assembled, despite having a comparable number of reads which mapped in the previous step. GRBV RNAs were significantly more abundant in leaf tissue than in berry tissue, but there was no significant difference between pre-veraison and post-veraison timepoints in either tissue type (Table S4). GYSVd-1 RNA abundance was not significantly affected by phenological stage or by GRBV infection in either tissue type. HSVd RNA was significantly more abundant in post-veraison leaves than pre-veraison leaves, but significantly less abundant in post-veraison berries than pre-veraison berries. Additionally, in both pre- and post-veraison berries, HSVd RNAs were significantly more abundant in GRBV-positive samples than in GRBV-negative samples (Figure S2 , Table S4). A similar trend was observed in post-veraison leaves, but it was not statistically significant. This indicates a potential synergistic interaction between GRBV and HSVd, but not between GRBV and GYSVd-1. Small RNA Target identification using Degradome Analysis Four degradome libraries were prepared and sequenced from GRBV-positive and GRBV-negative leaf samples taken at both pre- and post-veraison. Across all four libraries, there were 66,492,906 raw reads. The first six nucleotides were trimmed from all of the raw reads with Trimmomatic 0.39 [ 48 ]. Additionally, reads with fewer than 23 nucleotides were removed, and reads longer than 28nt were trimmed to 25 nt. An overrepresented sequence mapping to chloroplastic DNA was also removed, resulting in a total of 38,204,856 clean reads. The cleaned degradome reads were used to identify miRNA targets within the Vitis vinifera transcriptome using CleaveLand version 4.5 [ 55 ]. This was performed using the Ensembl Plants version 54 [ 61 ] annotation of the V. vinifera transcriptome. In the pooled degradome from leaves of GRBV-negative vines, a total of 9,241,719 reads were reported to have at least one alignment to the transcriptome. In the degradome from leaves of GRBV-positive vines, this total was 14,059,040 reads. Between both pools, only 75,081 reads were omitted due to the use of the (-m) argument. Target gene annotations were determined by assigning homologues from the Ensembl Plants version 54 A. thaliana annotation. This allowed for the identification of a total of 58 conserved targets for 18 miRNA families. Additionally, three targets for tasiRNA3 were identified (Table 1 ). T-plots for select targets are shown in Figure S3. Table 1 Degradome validation of miRNA family targets and target functions. Family Transcript Site Allen GRBV- Leaves GRBV + leaves Target function Valid % Cat. Valid % Cat miR156 Vitvi10g04328_t001 885 2 112 14.58 2 167 10.9 2 squamosa_promoter_binding_protein-like_3 Vitvi12g00280_t001 704 0 1 0.82 4 NA NA NA squamosa_promoter_binding_protein-like_4 Vitvi11g00909_t001 1116 0.5 1 6.67 4 1 7.69 4 squamosa_promoter_binding_protein-like_2 Vitvi17g00473_t001 1242 0.5 1 25 4 NA NA NA Squamosa_promoter-binding_protein Vitvi01g01837_t001 1330 0.5 3 25 1 1 5.56 4 Squamosa_promoter-binding_protein Vitvi01g01660_t001 1354 0.5 1 6.25 4 2 10.53 4 squamosa_promoter_binding_protein-like_2 Vitvi01g01678_t001 891 0.5 NA NA NA 1 100 4 Squamosa_promoter-binding_protein Vitvi17g00100_t001 1223 0.5 NA NA NA 1 33.33 4 Squamosa_promoter-binding_protein miR159 Vitvi13g01266_t001 1732 2.5 13 48.15 0 13 44.83 0 myb_domain_protein_33 miR160 Vitvi18g00337_t001 1304 0.5 2 1.36 2 4 1.71 2 auxin_response_factor_17 Vitvi13g02058_t001 1337 1 3 4.05 2 2 1.67 2 auxin_response_factor_16 Vitvi06g00272_t001 1719 1 1 3.57 4 NA NA NA auxin_response_factor_10 Vitvi08g01033_t001 2190 0.5 NA NA NA 1 0.71 4 auxin_response_factor_16 miR162 Vitvi15g00864_t001 3261 2 3 0.45 2 1 0.1 4 dicer-like_1 miR164 Vitvi17g00622_t001 997 1 3 21.43 0 4 12.9 0 NAC_domain_containing_protein_100 Vitvi19g01484_t001 809 2 3 6.52 2 3 3.61 2 NAC_domain_containing_protein_1 miR166 Vitvi09g00310_t001 562 1 7 3.45 0 23 7.28 0 Homeobox-leucine_zipper_family_protein Vitvi06g00276_t001 574 1.5 16 6.5 2 28 6.97 2 Homeobox-leucine_zipper_family_protein Vitvi13g00609_t001 763 1.5 15 23.81 0 11 13.58 0 Homeobox-leucine_zipper_family_protein Vitvi10g00913_t001 806 1.5 19 9.79 0 36 12.46 0 Homeobox-leucine_zipper_family_protein Vitvi04g00287_t001 1036 1.5 7 11.11 0 23 19.49 0 Homeobox-leucine_zipper_family_protein miR167 Vitvi04g00824_t001 1841 4 165 35.56 2 226 34.35 2 auxin_response_factor_8 Vitvi10g00854_t001 3613 4 8 4.88 0 14 6.48 0 auxin_response_factor_8 Vitvi12g00102_t001 3855 4 14 20 0 21 22.58 0 auxin_response_factor_6 miR168 Vitvi17g01218_t001 702 4 129 11.92 0 190 13.59 0 argonaute 1 miR169 Vitvi08g01883_t001 1510 3 1 2.5 4 2 3.45 2 nuclear_factor_Y,_subunit_A1 Vitvi09g00133_t001 1373 1.5 2 1.4 3 NA NA NA nuclear_factor_Y,_subunit_A3 Vitvi08g00292_t001 1357 3 9 2.24 2 21 3.4 2 nuclear_factor_Y,_subunit_A10 miR171 Vitvi04g01247_t001 599 0 61 15.1 0 70 14.4 2 GRAS_family_transcription_factor Vitvi15g00680_t001 1601 0 53 4.22 0 83 4.32 0 GRAS_family_transcription_factor Vitvi02g00536_t001 1664 1 128 10.48 0 114 6.75 0 GRAS_family_transcription_factor miR172 Vitvi13g00529_t001 1348 1 18 17.14 0 17 9.24 0 related_to_AP2.7 Vitvi06g00360_t001 1825 1.5 3 9.38 1 1 3.13 4 related_to_AP2.7 Vitvi07g01706_t001 1934 1 22 8.12 0 26 4.96 0 APETALA2 miR319 Vitvi06g01139_t001 1254 4 20 41.67 0 36 48.65 0 myb_domain_protein_33 Vitvi12g00219_t001 786 4.5 1 0.13 4 4 0.38 3 TCP_family_transcription_factor_4 miR393 Vitvi00g04585_t001 1516 1 2 18.18 1 4 26.67 0 F-box/RNI-like_superfamily_protein Vitvi07g00248_t001 1637 1 29 37.18 0 33 31.73 0 F-box/RNI-like_superfamily_protein Vitvi14g04156_t001 2014 1 2 13.33 1 4 20 0 F-box/RNI-like_superfamily_protein Vitvi14g01482_t001 2172 1 98 7.52 0 116 5.54 0 auxin_signaling_F-box_3 miR395 Vitvi18g00363_t001 77 1.5 NA NA NA 4 1.47 2 sulfate_transporter_2;1 miR396 Vitvi02g00796_t001 250 2.5 6 1.32 2 7 1.03 2 leucine_zipper_transcription_factor_16 Vitvi08g01498_t001 548 3 4 100 3 7 58.33 0 growth-regulating_factor_4 Vitvi02g00239_t001 671 3 3 13.64 1 3 17.65 1 growth-regulating_factor_8 miR398 Vitvi14g02607_t005 69 4 367 21.79 0 625 22.84 0 copper/zinc_superoxide_dismutase_1 Vitvi06g01349_t001 477 6.5 4 1.57 2 5 1.32 2 copper/zinc_superoxide_dismutase_2 Vitvi02g00444_t001 773 7 8 2.4 2 14 2.71 2 copper_chaperone_for_SOD1 Vitvi11g01445_t001 77 4 18 9.78 2 11 5.76 2 blue-copper-binding_protein miR828 Vitvi17g00822_t001 360 4 2 33.33 1 4 80 0 myb_domain_protein_66 Vitvi02g01732_t001 478 1 23 88.46 0 23 88.46 0 myb_domain_protein_66 Vitvi14g03020_t001 492 4 3 75 0 7 100 3 myb_domain_protein_23 miR858 Vitvi14g00974_t001 347 5.5 4 2.58 2 2 0.57 4 myb_domain_protein_4 Vitvi11g00097_t001 362 2.5 22 25.88 0 50 36.5 0 myb_domain_protein Vitvi06g00414_t001 369 4.5 1 4.55 4 NA NA NA myb_domain_protein_59 Vitvi07g00393_t001 433 5 14 10.85 0 13 6.57 0 myb_domain_protein_12 Vitvi09g00112_t001 451 4.5 41 28.47 0 66 27.16 0 myb_domain_protein_7 Vitvi04g00160_t001 303 4.5 NA NA NA 3 75 0 myb_domain_protein_66 Vitvi08g01797_t001 324 5 NA NA NA 3 0.55 3 myb_domain_protein_5 tasiRNA3 Vitvi01g01759_t001 1203 2 4 0.48 2 5 0.42 2 auxin_response_factor_2 Vitvi10g00510_t001 1431 0 3 0.08 3 7 0.13 3 auxin-responsive_factor 1638 0 4 0.11 3 11 0.2 2 auxin-responsive_factor Vitvi17g00036_t001 1738 0.5 17 2.79 2 15 1.62 2 auxin_response_factor_2 Cleavage position (site) and Allen score (Allen) for miRNA targets. Number of valid reads, percentage of total reads that are valid, and category value for degradome validation in both GRBV-negative (GRBV-) and GRBV-positive (GRBV+) leaves. Target functions based on A. thaliana annotation and further supported by NCBI BLAST results. Table 1 Currently located at the end of the document. To find potential novel targets, the remaining identifications were screened according to strict criteria of having greater than 10 total valid reads and greater than 5% valid reads. In this manner, 41 novel targets of 19 miRNA families were identified, over half of which exhibited greater than 25% valid reads in one or both pooled degradomes (Table 2 , Table S5). Table 2 Target function annotations for novel targets. Query Transcript Arabidopsis homologue Gene description miR159 Vitvi12g00209_t001 AT2G42570.1 TRICHOME_BIREFRINGENCE-LIKE_39 miR167 Vitvi03g00206_t001 AT3G12500.1 basic_chitinase miR168 Vitvi09g02081_t001 ATMG00510.1 *Complex 1 49kDa superfamily; respiratory-chain NADH dehydrogenase, 49 Kd subunit (cl21493) Vitvi09g02090_t001 ATMG00510.1 Interval 16-1155; E 2.01e-62; Bit 205 miR3476 Vitvi03g00706_t001 AT1G04945.2 HIT-type_Zinc_finger_family_protein Vitvi13g01803_t001 AT1G04945.2 Vitvi15g04391_t001 AT1G04945.4 Vitvi15g04396_t001 AT1G04945.4 Vitvi15g04403_t001 AT1G04945.4 Vitvi16g00437_t001 AT3G06530.2 ARM_repeat_superfamily_protein miR3623 Vitvi18g00591_t001 AT5G49650.1 xylulose_kinase-2 Vitvi16g00880_t001 AT2G45590.1 Protein_kinase_superfamily_protein miR3624 Vitvi10g00241_t001 AT4G16380.3 Heavy_metal_transport/detoxification_superfamily_protein Vitvi10g00245_t002 AT4G16380.2 Vitvi08g01651_t001 AT3G57000.1 nucleolar_essential_protein-like_protein Vitvi13g01167_t001 AT3G57000.1 miR3629 Vitvi13g01758_t001 AT5G56710.1 Ribosomal_protein_L31e_family_protein miR3633 Vitvi03g00203_t001 AT4G35250.1 NAD(P)-binding_Rossmann-fold_superfamily_protein Vitvi03g04485_t001 NA *Protein of Unknown Function Vitvi10g04333_t001 NA *Protein of Unknown Function Vitvi07g01217_t001 AT4G02600.2 Seven_transmembrane_MLO_family_protein miR3634 Vitvi19g04623_t001 NA *Ank 2 superfamily; Ankyrin repeats (3 copies); cl39094; Interval 212–370; E 3.27e-04; Bit 38.95 miR3635 Vitvi03g01290_t001 AT3G12750.1 zinc_transporter_1 miR395 Vitvi06g01295_t001 AT2G28000.1 chaperonin-60alpha miR396 Vitvi01g00876_t001 AT1G59640.1 transcription_factor_BIG_PETAL_P_(BPE) Vitvi06g04411_t001 AT2G27970.1 CDK-subunit_2 Vitvi06g01133_t001 AT1G50420.1 scarecrow-like_3 Vitvi02g01038_t001 AT5G26742.3 DEAD_box_RNA_helicase_(RH3) miR397 Vitvi09g00019_t001 AT1G72330.1 alanine_aminotransferase_2 miR477 Vitvi07g02021_t001 AT1G04290.1 Thioesterase_superfamily_protein Vitvi17g00218_t001 AT1G18210.2 Calcium-binding_EF-hand_family_protein Vitvi11g00481_t001 AT5G20200.1 nucleoporin-like_protein miR482 Vitvi18g02996_t001 NA *Protein of Unknown Function Vitvi09g01354_t001 AT1G51580.1 RNA-binding_KH_domain-containing_protein Vitvi13g04689_t001 AT3G14470.1 NB-ARC_domain-containing_disease_resistance_protein Vitvi14g01187_t001 AT5G16120.4 alpha/beta-Hydrolases_superfamily_protein miR5139 Vitvi18g01220_t001 AT3G14430.1 GRIP/coiled-coil_protein miR828 Vitvi14g04439_t001 NA *Protein of Unknown Function miR894 Vitvi14g04415_t001 AT3G18280.1 Bifunctional_inhibitor/lipid-transfer_protein/ seed_storage_2S_albumin_superfamily_protein miR894 Vitvi14g04418_t001 AT3G18280.2 Bifunctional_inhibitor/lipid-transfer_protein/ seed_storage_2S_albumin_superfamily_protein Target functions are based on A. thaliana annotation and further supported by NCBI BLAST results. Target functions marked with an asterisk (*) either lacked an annotated function in the A. thaliana annotation of the A. thaliana homologue or lacked an A. thaliana homologue altogether. In this case, functions were annotated using the NCBI Conserved Domain Search function. In some cases, no matching domains were found, in which case they were marked as proteins of unknown function. Table 2 Currently located at the end of the document. Using the methods described above, putative targets were also identified for a list of sRNAs which had specifically mapped to the GRBV genome. Valid targets were manually curated based on relaxed cutoffs (> 2% Valid Reads and > 4 10nt reads). The t-plots and transcript alignments of the manually selected targets were then considered before the final list of valid targets was chosen (Figure S4). These analyses were performed twice, once using the degradome data from healthy samples, and once using the degradome data from infected samples (Table S6). This was done to determine if cleavage events from GRBV-specific sRNAs also had support in GRBV-negative samples, which could indicate that the observed degradome support may not be tied to the identified cleavage event. Degradome support for the identified targets did vary based on which degradome data was used. The overwhelming majority of potential targets either lacked substantial alignment at the 5’ end or had poor degradome support. In the end, only fourteen targets of eight different virus-derived sRNAs were selected. Table 3 GRBV-derived siRNAs with putative targets in V. vinifera transcriptome sRNA Sequence Start-Stop ORF grbvasRNA1 AAACGACGTGTCTGGTGGAGG 1246–1266 V1 grbvasRNA2 ACGACTGGGAGGAGTTCTGCC 761–781 V2 grbvasRNA3 AGGTGTTGTGCTTCCGTCGGA 946–966 V1 grbvasRNA4 ATGATGGGTTAGGGGATGAGG 389–409 V2 grbvasRNA5-3p ATGGGCTATATCATTGGGAAT 2222 − 2202 C2 grbvasRNA6-3p ATGTGGCAATGACTCCTGCGG 1192 − 1172 V1 grbvasRNA7a TTGTGGTGATGATGATGGGTT 378–398 V2 grbvasRNA7b TTTGTGGTGATGATGATGGGT 377–397 V2 Sequences of identified grbvasRNAs with potential targets in the V. vinifera genome and locations within the GRBV genome. The “-3p” suffix indicates that the detected vsiRNA sequence was the reverse complement of the associated sequence in the GRBV reference genome. The other identified vsiRNAs aligned normally in the 5’-3’ direction. For ease of reference, the eight identified vsiRNAs were assigned identifiers grbvasRNA1-7. Instead of using grbvasRNA8, a grbvasRNA7a and grbvasRNA7b were used, since the two sequences differed only by a single nucleotide (Table 3 ). Interestingly, all but one of these eight vsiRNAs are mapped to ORFs V1 and V2, while only grbvasRNA5 is located within the complementary-sense C1 ORF. It is worth noting that, among the eight identified vsiRNAs, it is the 3p forms of grbvas5 and grbvas6 with identified targets. This is expected for grbvas5, as it is within the C2 ORF, which is normally transcribed in the 3’-5’ direction. However, grbvas6 is derived from the V1 ORF, which is transcribed in the 5’-3’ direction. This indicates that for this particular vsiRNA to accumulate, it would need to have a source other than the normal transcription of the V1 ORF, such as read-through transcription in the 3’-5’ direction. For the selected vsiRNA targets, gene annotations were identified using NCBI’s Conserved Domain Search function [ 65 ]. These targets included proteins involved in intracellular transport, photosynthesis, apoptosis, transcription, translation, DNA/RNA recognition, binding, and maintenance (Table 4 , Table S7). Table 4 Identified targets of GRBV-derived siRNAs Query Target Target Function (Conserved Domain Search) grbvasRNA1 Vitvi01g00128_t001 Mu homology domain (MHD) of adaptor protein (AP) coat protein I (COPI) delta subunit, cd09254; delta subunit of the F-COPI complex, N-terminal domain, cd14830 Vitvi01g00128_t002 C-terminal domain of adaptor protein (AP) complexes medium mu subunits and its homologs, cl10970, member cd09254: AP_delta-COPI_MHD; delta subunit of the F-COPI complex, N-terminal domain, cd14830 grbvasRNA2 Vitvi06g00398_t001 Ribosomal L29 protein, pfam00831 Vitvi07g01275_t001 photosystem II oxygen-evolving enhancer protein 1, PLN00037 grbvasRNA3 Vitvi19g00148_t001 Apoptosis inhibitory protein 5 (API5), pfam05918 grbvasRNA4 Vitvi09g00707_t001 RNA recognition motif (RRM) superfamily, cl17169, member cd12288: RNA recognition motif (RRM) found in plant proteins related to the La autoantigen; inosine-5'-monophosphate dehydrogenase, cl33447, member PLN02274, inosine-5'-monophosphate dehydrogenase; inosine-5'-monophosphate dehydrogenase cl36546, member PTZ00314, inosine-5'-monophosphate dehydrogenase, Provisional grbvasRNA5 Vitvi15g01427_t001 Zn-finger in Ran binding protein and others, pfam00641 Vitvi15g01427_t002 Zn-finger in Ran binding protein and others, pfam00641 grbvasRNA6 Vitvi19g01704_t001 Superfamily II DNA and RNA helicase [Replication, recombination and repair], COG0513 grbvasRNA7a Vitvi17g00483_t001 PPR repeat family, pfam13041; maturation of RBCL 1, cl33664 member PLN03218; Uncharacterized protein cl23818 member PRK00976: methanogenesis marker 12 protein Vitvi18g01006_t001 Chloroplast import apparatus Tic20-like, cl15935 member TIGR00994: 3a0901s05TIC20 chloroplast protein import component, Tic20 family Vitvi18g01006_t002 Chloroplast import apparatus Tic20-like, cl15935 member TIGR00994: 3a0901s05TIC20 chloroplast protein import component, Tic20 family Vitvi18g01006_t003 Chloroplast import apparatus Tic20-like, cl15935 member TIGR00994: 3a0901s05TIC20 chloroplast protein import component, Tic20 family grbvasRNA7b Vitvi11g00285_t001 DNA-binding domain in plant proteins such as APETALA2 and EREBPs, smart00380 Differential Expression of miRNA Between Samples To compare miRNA expression levels between GRBV-infected and noninfected samples and between the two phenological stages, read counts were analyzed in R Statistical Language [ 52 ] using the package ‘edgeR’ by Bioconductor. Due to a large number of miRNAs that were detected in very low counts and only in certain categories or replicates, miRNAs lacking at least one count in a minimum of six different samples were excluded from the analysis, leaving 99 unique miRNAs. Significant differential expression was determined through use of the Likelihood Ratio Model. After applying cutoffs (P-Value 1.0), a total of 63 unique miRNAs belonging to 28 different families were significantly differentially expressed between the comparisons made. P-value was chosen as the cutoff metric for this analysis, instead of the more conservative FDR value, due to the extremely high variance in the data (common dispersion > 0.8). In the following sections, the term “differentially expressed” is used to indicate miRNAs that passed these cutoffs regarding a specific comparison (i.e., GRBV + vs. GRBV- or pre- vs. post-veraison). Differences in miRNA Expression Between GRBV-negative and GRBV-positive Samples When comparing the miRNA expression patterns in GRBV-positive samples relative to GRBV-negative samples, a total of 41 miRNAs across 18 different miRNA families showed differential expression (Table S8). Three miRNAs, miR166ax, miR3624a-3p, and miR482aw, were differentially expressed in both pre- and post-veraison berries. Similarly, miR396j was differentially expressed in both pre- and post-veraison leaves. Including miR396j, twelve miRNAs were only differentially expressed in the leaves, while 24 were only differentially expressed in berries. Pre-veraison berries and post-veraison leaves each had differentially expressed miRNAs belonging to a single family that was uniquely represented within the corresponding category. They are miR156 and miR2950, respectively, in berry and leaf samples. Post-veraison berries exhibited three such families, miR159, miR162, and miR166. The miRNA3624 family was uniquely differentially expressed within berry samples. Of the 30 different miRNAs that were significantly differentially regulated, seven miRNA families were upregulated and 16 miRNAs from three different families were downregulated in response to viral infection (Table S8). There was no overlap between leaf and berry sample categories in down regulated miRNAs. No downregulated miRNAs were observed in leaf samples from the pre-veraison stage. Pre-veraison berries had two down-regulated miRNAs, miR166be and miR6478a, while post-veraison berries and post-veraison leaves had seven each. Isoforms of miR156, miR395, and miR3624 were exclusively down-regulated in post-veraison berries. The miR396 family had down-regulated members in all post-veraison samples. miR166 had members that were down-regulated in all categories except in pre-veraison leaves. Additionally, isoforms of miR159 and miR319 were only downregulated in post-veraison leaves. Upregulation of some miRNAs occurred in samples from both pre- and post-veraison (Fig. 4). Eleven miRNAs were upregulated in infected plants relative to healthy plants exclusively in the pre-veraison berry samples. This includes miR3624a-3p, which was down-regulated in post-veraison berries. Eight miRNAs were differentially expressed in post-veraison berries only. One isoform, miR482aw, was upregulated in both pre-and post-veraison berries. The previously mentioned miR396j was upregulated in both pre- and post-veraison leaves. Aside from miR482aw and miR396j, pre-veraison leaves showed six upregulated miRNAs, belonging to miR166, miR395, and miR3630, while post-veraison leaves showed upregulation of three miRNAs, namely miR166l, miR167an, and miR2950-3c. Members of miR166 and miR396 were upregulated in all four categories of samples in GRBV-positive vines relative to GRBV-negative vines. Members of miR395 were upregulated in all pre-veraison samples. miRNAs belonging to miR3630 and miR167 families were exclusively upregulated in leaf samples taken at pre- and post-veraison, respectively. Additionally, members of miR156, miRR165, miR3624, and miR3633 were exclusively upregulated in pre-veraison berry samples, while members of miR159, miR162, miR3476, miR3637, and miR7505 were exclusively upregulated in post-veraison berries (Fig. 4). The fold change, p-values, and FDR values for differential expression of miRNAs are shown in Table S8. Figure 4 Differential expression of miRNAs in GRBV-negative vs. GRBV-positive vines Number of miRNAs differentially expressed in response to infection by GRBV in leaves and berries, either in pre-veraison, post-veraison, or both (venn diagram). The number, direction, and identity of differentially expressed miRNAs in response to infection by GRBV, either in pre-veraison, post-veraison, or both (bar chart). miRNAs in red were downregulated in GRBV-positive relative to GRBV-negative samples, and miRNAs in green were up-regulated GRBV-positive relative to GRBV-negative samples. miRNAs in grey were differentially expressed in different directions in pre- compared to post-veraison samples. Both miR166ax and miR3624a-3p were up-regulated during pre-veraison and down-regulated during post-veraison. Differences in miRNA Expression Between Pre- and Post-Veraison Leaves and Berries When comparing the miRNA expression patterns in leaf and berry samples collected from pre-veraison relative to samples collected post-veraison, there were 50 differentially expressed miRNAs across twenty different miRNA families (Table S9). Of the 50 differentially expressed miRNAs, nine miRNAs belonging to six different families were only differentially expressed in the leaves. Of these six families, miR398 and miR408 only had differentially expressed members in leaf samples. Six miRNAs were differentially expressed in both leaves and berries, while the remaining 35 miRNAs were exclusively differentially expressed in the berries. Additionally, six miRNAs belonging to the families miR156, miR162, miR165, miR396, and miR6478, were differentially expressed only in GRBV-positive samples. Eight miRNAs were exclusively differentially expressed in GRBV-negative samples (Fig. 5). Despite the focus of this study being the interactions between GRBV infection and the V. vinifera cv. ‘Merlot’ miRNA profile, this post- minus pre-veraison DE analysis was performed to provide a more comprehensive picture of the normal shift in the grapevine miRNA profile across the growing season. This additional analysis allowed for the identification miRNAs which were differentially expressed in response to veraison in either GRBV-negative or GRBV-positive samples (not both) which did not exhibit significant differential expression with regard to infection status (miR156ao, miR159ak, miR166ag,ai,aj,h, miR168b, miR319ag, miR396h, miR398b, miR408ab, miR3627e-3p, and miR3633a). Despite the lack of differential expression in the viral status comparison, the presence of differential expression in healthy samples, paired with its lack in diseased samples, or vice versa, is still a meaningful finding. Figure 5 Differential Expression of miRNAs during pre-veraison vs. post-veraison Number of miRNAs differentially expressed in post-veraison relative to pre-veraison in leaves and berries, either in GRBV-negaitve plants, GRBV-positive plants, or both (venn diagram). The number, direction, and identity of miRNAs differentially expressed in post-veraison relative to pre-veraison either in GRBV-negative plants, GRBV-positive plants, or both (bar chart). miRNAs in red were downregulated post- relative to pre-veraison, and miRNAs in green were up-regulated post- relative to pre-veraison. miRNAs in grey were differentially expressed in different directions in GRBV-negative compared to GRBV-positive samples. Both miR395o and miR3624a-3p were upregulated in GRBV-negative berries and downregulated in GRBV-positive berries. Differences in abundance of vsiRNAs specific to GRBV ORFs To determine if the GRBV-derived sRNAs were particularly associated with specific regions of the GRBV genome, sRNA reads were mapped to the NCBI gene-annotated version of the GRBV genome (accession NC_022002.1). To gauge whether tissue type and phenological stage had an impact on the relative abundance of sRNAs mapping to each ORF, they were analyzed using the differential expression pipeline described above, utilizing the “EdgeR” package by Bioconductor in the R Statistical Coding Language [ 52 ]. Out of 1,360,633 reads that mapped to the GRBV genome, 1,302,547 sRNA reads (> 95%) mapped to known ORFs. The number of reads that mapped to individual ORFs in each sample are shown in Table S6. The highest number of vsiRNAs mapped to the V3 ORF, which had almost double the normalized reads (TPM) when compared to the other ORFs. The high abundance of V3-specific vsiRNAs was consistent in leaves and berries from both pre- and post-veraison. Four different ORFs exhibited differential sRNA abundance between pre- and post-veraison in berry samples, while only a single ORF exhibited differential expression between pre-and post-veraison in leaf samples. In berries, sRNAs that mapped to C1, C2, and C3 ORFs were more abundant in post-veraison relative to pre-veraison, while the sRNAs specific to the V1 ORF were less abundant in post-veraison relative to pre-veraison. In leaves, sRNAs specific to the C2 ORF were significantly more abundant in post-veraison relative to pre-veraison (Fig. 6). Figure 6 Abundance of vsiRNAs specific to GRBV ORFs Abundance (TPM) of sRNAs which mapped to specific GRBV ORFs. Asterisks are indicative of a significant difference between pre- and post-veraison within a tissue type. In berries, sRNAs mapping to V1 were significantly less abundant and sRNAs mapping to C1-3 were significantly more abundant. In leaves, sRNAs mapping to C2 were significantly more abundant in post- relative to pre- veraison. In V2 and C3, the general trend in leaves lined up with what was observed in berries but was not significant. A linearized depiction of the circular GRBV genome (5’ to 3’) is displayed above to illustrate ORF locations within the GRBV genome. This depiction was created using SnapGene software [ 36 ] and the RefSeq GRBV annotation [ 22 ]. Sampling categories are denoted according to the following abbreviations: pre-veraison [P], post-veraison [PO], leaves [L], berries [B], GRBV-positive [D]. Discussion GRBV is a monopartite geminivirus and is one of only four DNA viruses known to infect grapevines [ 66 ]. The biogenesis and action mechanisms of RNA silencing in DNA viruses are more complex and less well understood compared with RNA viruses. The size of vsiRNAs can provide context related to their biogenesis and putative functional roles. Regarding their biogenesis, the length of a given vsiRNA is dependent upon the DCL protein that processed its precursor. The DCLs responsible for vsiRNAs of different lengths are known: DCL2 for 22nt, DCL3 for 24nt, and DCL4 for 21nt [ 9 , 67 ]. The DCLs involved in the biogenesis of 20nt and 23nt vsiRNAs are not confirmed. Available evidence suggests that 20nt vsiRNAs are primarily generated by DCL2 but can also be generated by DCL 4, and that 23nt vsiRNAs are generated by both DCL2 and DCL3 [ 68 ]. With regard to mode of action, there is a general trend throughout sRNA-mediated silencing towards long sRNAs (~ 24nt) being likely to act via chromatin deposition (DNA methylation), and smaller sRNAs (20-22nt) tending to act via RNA degradation [ 1 ]. Indeed, the production of 24nt siRNAs by DCL3 has been directly tied to the RNA-directed DNA methylation (RdDM) pathway [ 69 , 70 ]. Among the GRBV-derived siRNAs, 21nt reads were the most common in all sampling categories, which is consistent with previous observations from other viral pathosystems, including Grapevine leafroll-associated virus 3 [ 41 ]. This also suggests that DCL4 could be the primary dicer involved in the generation of GRBV-derived vsiRNAs. Additionally, the observation that 20nt and 22nt vsiRNAs were significantly more abundant during post-veraison compared to pre-veraison, alongside a decrease in the abundance of 21nt vsiRNAs, could be indicative of a shift in DCL-2 activity across the growing season. The greater abundance of 21nt and 22nt vsiRNAs, relative to the low abundant 24nt vsiRNAs, may suggest that the majority of antiviral silencing targets GRBV transcripts and that the action of the RdDM pathway does not play a major role in silencing GRBV genomic DNA. This contrasts with observations made in geminivirus-infected Arabidopsis , which have demonstrated the importance of RdDM in antiviral defense [ 71 , 72 ]. Our findings may suggest that the RdDM pathway is not always the primary defense strategy against DNA viruses in plants, as has been previously suggested [ 9 ]. Regarding the length distributions of siRNAs derived from GYSVd-1 and HSVd, the observed predomination of 21nt and 24nt vdsiRNAs supports what has previously been observed from these two viroids infecting V. vinifera cv. Merlot [ 41 ]. In contrast to observations with GRBV vsiRNAs, relatively high levels of 24nt vdsiRNAs from both viroids suggest a higher level of interaction with the RdDM pathway. While these viroids do not have genomic DNA that could be methylated by this pathway, these 24nt vdsiRNAs may be involved in the methylation of host DNA. RdDM of host DNA has been observed in viroid-infected plants, including those infected with HSVd [ 73 ], but the exact mechanisms involved are unknown. It has been suggested that 24nt vdsiRNAs are involved in the formation of longer dsRNAs which trigger RdDM, as opposed to triggering RdDM directly. This would explain observations that complementarity between vdsiRNAs and target host DNA is not necessary for viroid-induced RdDM to occur [ 74 , 75 ]. The abundances of GRBV transcripts in leaf and berry samples at pre- and post-veraison were analyzed to examine the correlation between viral transcript levels and observed changes in the vsiRNA profile of two phenological stages. However, we did not find any significant differences in GRBV transcript levels between pre- and post-veraison samples, indicating that significant differences in the vsiRNA profile between the two phenological stages cannot be attributed to a change in the transcription of GRBV RNAs. The abundances of viroidal RNA, on the other hand, were analyzed primarily to determine if there was any evidence of synergism between GRBV and the two-viroids. Viruses and viroids are known to participate in such synergistic interactions, but the majority of research has focused on virus-virus and viroid-viroid interactions [ 76 , 77 ]. Other studies have investigated the possibility of virus-viroid synergisms [ 78 ]. To our knowledge, only two such interactions have been described to date, one of which was between GYSVd-1 and Grapevine fanleaf virus (GFLV), a nepovirus responsible for fanleaf degeneration in grapevines [ 79 , 80 ]. Similar to what was observed between GYSVd-1 and GFLV, we observed an increase in the titer of HSVd RNAs in plants co-infected with GRBV, making this study yet another example of a virus-viroid synergistic interaction. In recent research, there have been multiple cases in which vsiRNAs have been shown to target host mRNA transcripts, leading to symptom expression [81, and cited references]. In this study, we identified eight distinct vsiRNAs with a total of 14 targets in the grapevine transcriptome. Of the fourteen putative target transcripts in the grapevine transcriptome, five possess functional domains directly tied to chloroplast function. An additional sixth target, containing a pentatricopeptide repeat (PPR), may also be connected to chloroplast function, as PPR proteins are known to be involved in the posttranscriptional regulation (RNA maturation, editing, intron splicing) of chloroplastic and mitochondrial genes [ 82 ]. In white grape cultivars, chlorosis is the major visual symptom, rather than the characteristic red blotches. Additionally, GRBV has been demonstrated to lower photosynthetic rates in infected vines [ 27 ]. In other viral systems, including Rice stripe virus and Southern rice black-streaked dwarf virus, vsiRNAs targeting host genes encoding chloroplastic proteins have been implicated as a mechanism leading to the development of chlorotic symptoms [ 83 , 84 ]. While more work needs to be done to make any definitive claims, it is possible that grbvasRNA2 and grbvasRNA7a are at least in part responsible for the lowered rate of photosynthesis in GRBV-infected grapevines, and the development of chlorosis in virus-infected, white-fruited cultivars. Regarding the identification of the most abundant miRNAs and miRNA families, one that attracted our attention was the highly abundant miR3634a-3p, which was expressed at high levels in all samples (Figure S5) but were notably more abundant in pre-veraison leaf samples, regardless of infection by GRBV (Fig. 2 C). miR3634-3p miRNAs have previously been observed to be among the most abundant miRNAs in grapevine [ 85 , 86 ], though not to the extent observed here. This study also found that miR3634a-3p targets the transcript Vitvi19g04623_t001 (Table 2 ), which contains 3 copies of an ankyrin repeat domain from the Ank 2 superfamily. Ankyrin repeat domains are involved in a wide array of different protein functions, including transport, cell-cell signaling, and various regulatory processes, although none have been observed to have any enzymatic function [ 87 ]. Previously, an miR3634 isoform in grapevine was found to target a transcript for an E3 ubiquitin ligase [ 88 ]. The discovery of an additional target of a completely unrelated nature suggests that the miR3634 family may regulate a variety of processes, which could be a contributing factor to its high abundance. The miR159, miR166, miR395, miR396, and miR3623 families were also highly expressed across all samples (Fig. 2 ), which is supported by previous studies of grapevine miRNAs [ 41 , 88 , 89 ]. The miR166 family target identification (Table 1 ) supports its previously established role in regulating the expression of a class III Homeodomain leucine-zipper protein tied to wood formation [ 41 , 90 ]. Previous studies have shown miR166 displaying upregulation in response to viral infection [ 21 , 41 ]. Here, miR166 family members displayed a mixture of responses to infection with GRBV. One in particular (miR166ax) even displayed a directional shift in its response to GRBV infection between pre- and post-veraison (Table S8). One miRNA which responded the same way to GRBV infection during both pre- and post-veraison was miR396j, which was upregulated in leaves (Table 2.3a). Overall, differential expression of miR396 family members in response to GRBV infection was highly varied, but predominantly followed the pattern of up-regulation in the berries and down-regulation in the leaves, making miR396j somewhat of an outlier relative to related miRNAs. This varied response of miR396 expression is supported by other studies, which have observed both up- and down-regulation of miR396 in response to various biotic and abiotic stresses [ 89 , 91 , 92 , 93 ]. It is widely known that miRNAs from the miR396 family target growth regulating factors (GRFs), which are involved in many developmental processes, ranging from general cell proliferation to flower, fruit, and seed formation [ 89 , 91 , 92 , 94 ]. The degradome analysis in this study validated two cleavage events for GRF transcripts, specifically those for GRF4 and GRF8 (Table 1 ). These two GRF proteins are primarily involved in cell proliferation of leaf tissue [ 94 ]. miR396j’s consistent response to GRBV infection in leaf tissue could indicate that miR396j is specifically involved with these two GRF factors, leading to reduced leaf growth in GRBV-infected plants, which could then contribute to the observed overall reduction in plant vigor [ 27 ]. We also identified novel targets for the miR396 family, including the transcription factor Big Petal P (BPEp), Cyclin-Dependent Kinase (CDK) subunit 2, and Scarecrow-like protein 3 (Table 2 ), all of which are involved in one or more aspects of plant development/growth [ 95 , 96 , 97 ]. Scarecrow-like protein 3, specifically, promotes gibberellin signaling, which in turn induces an array of different plant growth and development processes [ 97 , 98 ]. The up-regulation of different miR396-family miRNAs in both pre- and post-veraison berries in response to GRBV infection warrants further investigation, as it could be acting to lower the activity of the gibberellin signaling pathway, which may be a contributing factor to the reduction of fruit yield and slower fruit maturation. The miR156 family is also known to regulate proteins involved in plant development, specifically the SQUAMOSA promoter-binding protein-like (SPL) transcription factors, which have been shown to play roles in both fruit ripening and stress response [ 99 , 100 ]. In this study, miRNAs belonging to the miR156 family exhibited up-regulation in response to GRBV infection in berries during pre-veraison, and in one case, down-regulation in post-veraison berries (Table S8). Prior studies investigating the grapevine sRNA profile have detected up-regulation [ 85 , 91 ] and down-regulation [ 41 , 88 , 89 , 93 , 101 ] in response to a variety of other pathogens and stresses. Our study detected an inverse response of the miR156 family as berries mature, and this regulation may contribute to the impeded berry development observed in GRBV-infected vines. Also tied to plant development, miR160, miR167, miR393, and tasiRNA3 were all found to target auxin response genes. Several isoforms of the miR167 family were significantly differentially expressed in response to GRBV infection, with miR167an being upregulated during post-veraison (Table S8). miR167 is known to target auxin response factors 6 and 8 (ARF6 and ARF8) (Table 1 ) [ 102 ]. ARF6 is a positive regulator of photosynthetic processes, sugar accumulation, and fruit ripening [ 103 ], and both ARFs are positive regulators of jasmonic acid biosynthesis [ 104 ]. Increased expression of miR167 has been shown to lead to defective development of flowers in tomato [ 102 ]. However, in our analysis, we only detected significantly increased expression of miR167an at post-veraison, long after flower formation. Based on the processes that ARF6 and ARF8 are involved in, this up-regulation could lead to an overall reduction in photosynthetic activity, fruit ripening, and jasmonic acid synthesis. Decreases in photosynthetic levels and fruit ripening are known symptoms of GRBV [ 27 ], and down-regulation of jasmonic acid has been observed in grapevines infected with Grapevine fabavirus [ 105 ], so the increase in miR167 in GRBV-infected plants could play a role in symptom development. Contrastingly, silencing of ARF6 by miR167 would lead to an overall reduction in sugar accumulation, and yet sugar accumulation in leaves is known to increase due to GRBV infection. It is currently believed that GRBV interferes with the transport of sugars from leaf to berry [ 27 ], which may explain the accumulation of sugar despite increased miR167 expression. The miR3623 family was found to target a protein homologous to the A. thaliana Xylulose kinase-2 protein (Table 2 ), which is integral to the isoprenoid biosynthesis pathway [ 106 ]. The miR3623 family has also been linked to the regulation of disease resistance genes involved in the regulation of phasiRNA production and has been suggested to be related to the miR482 family, which is also known to target disease resistance proteins [ 107 ]. It has also been found to exhibit up-regulation in grapes infected with the phytoplasma disease, Flavescence dorée [ 85 ]. Our findings show that miR3623-family miRNAs were not differentially expressed in response to GRBV-infection, while miR482aw was upregulated in GRBV-infected berries, which could suggest that, in this plant-pathogen system, the expression of miR3623 is tied more closely to the regulation of isoprenoid biosynthesis and that the regulation of disease resistance proteins may be a secondary function. Interestingly, miR482aw was only one of two miRNAs, the other being miR396j, which responded the same way to GRBV infection regardless of veraison stage, being upregulated in the berries at both timepoints (Table S8). The fact that miRNAs in the miR482 family negatively regulate disease resistance proteins [ 108 , 109 ] makes the increased expression of these miRNAs in response to disease appear counterintuitive, though this same pattern has also been observed in other plant-pathogen systems [ 110 ]. It has also been suggested that members of the miR482 family serve as an evolutionary strategy, reducing the fitness costs of inefficient or non-functioning R genes, protecting against their misexpression, and allowing more freedom for genetic variation [ 111 ]. This would explain the upregulation of miR482aw in diseased berries, as it may be suppressing resistance genes that are being expressed because of GRBV infection but do not offer any functional resistance to it. The miR395 family is known to inhibit both ATP sulfurylases and sulfate transporter 2;1. The inhibition of these proteins leads to the accumulation of sulfate in plant tissue [ 112 , 113 ]. Sulfate and sulfate-derived compounds are important contributors to stress tolerance in plants and play a role in plant defense strategies against pathogens [ 114 ]. In grapevine, miR395 has previously been observed to exhibit increased expression in vines infected with Grapevine leafroll-associated virus 3, suggesting it may play a role in a defense strategy against viral disease [ 37 ]. We observed that miR395-family miRNAs were only up-regulated in response to GRBV infection during pre-veraison, and that some were even down-regulated during post-veraison. The miRNA family miR159 is known to target the MYB33 and MYB65 genes (Table 1 ) [ 115 ], and is closely related to the miR319 family. miR319 is generally recognized as silencing TCP genes. While both miRNA families have been found to target both MYB and TCP genes, their sequence differences have been shown to make them more effective at silencing their primary targets [ 116 ]. Interestingly, the expression levels of miRNAs from both miR159 and miR319 families showed down-regulation in response to GRBV infection in post-veraison leaf samples. This appears to be a somewhat unique interaction, as previous studies have observed both groups exhibiting increased expression in response to viral infection in other plant systems [ 21 , 117 , 118 ]. MYB33 and MYB65 are associated with several aspects of plant growth and development [ 119 , 120 ]. TCP-4 (Table 1 ) is tied to the regulation of leaf morphogenesis and phytohormone levels [ 121 ]. Interestingly, MYB33 and MYB65 have also been associated with drought tolerance responses and their repression has been shown to increase the impact of drought on Arabidopsis [ 122 ]. GRBV infected vines have been shown to have increased symptom severity when receiving limited water, which is problematic due to the frequent use of deficit irrigation to increase fruit quality in vineyards [ 123 ]. In their study, Levin and KC only witnessed adverse effects of water limitation in GRBV-infected vines during post-veraison. This supports the possibility that the downregulation of miR159 post-veraison witnessed in this study could be an effort by the plant to mitigate drought stress by increasing MYB33 and MYB65 levels. Additionally, some MYBs, such as VvMYB114, have been shown to regulate anthocyanin accumulation in grapevines [ 124 ]. VvMYB114 is known to be regulated by miR828 and miR858. These miRNAs also target MYB4, MYB5, MYB7, MYB12, MYB23, MYB59, and MYB66 family proteins (Table 1 ), but were not found to be significantly differentially expressed in this study. The lack of differential expression, particularly between pre- and post-veraison stages, was unexpected due to the important shifts that occur in anthocyanin production during veraison. However, these miRNAs were not detected in all samples and possessed very small read counts, which could have contributed to the lack of statistical significance. Conclusions In summary, the results of this study lay a foundation for future research into the mechanisms of the interactions between Vitis vinifera and GRBV. The length distribution of GRBV-derived vsiRNAs suggests that the primary action of vsiRNAs is through mRNA silencing and not RNA-dependent DNA methylation, contrary to observations in other plant-DNA virus pathosystems. Future research into the actual levels of RdDM in GRBV-infected vines could provide insight into a potential method of increasing grapevine resistance to the virus. The observed synergism between GRBV and HSVd warrants further investigation, as it is unclear if the synergistic relationship is mutual, or if coinfection impacts host gene expression differently relative to what would be observed in single infections with HSVd or GRBV. More work is needed with regard to the biogenesis of vsiRNAs derived from DNA viruses, as well. The observed overabundance of vsiRNAs originating from the V3 ORF indicates that this particular ORF in GRBV may play an important role in this process, which requires further study. Additionally, the results of this study align with the ever-increasing body of evidence supporting the idea that viral infection can modulate plant miRNAs [ 37 , 41 , 117 , 125 ]. Transcripts involved in various areas of grapevine development, metabolism, and defense are targeted by many of the miRNAs we identified to be differentially expressed in response to infection by GRBV. This study identified 41 miRNAs which were differentially expressed in response to GRBV infection (Table S8), in addition to 50 miRNAs that were differentially expressed in response to veraison (Table S9). We also found 58 targets of conserved miRNAs (Table 2 .4), as well as 40 novel targets of grapevine miRNAs (Table 2 ), all supported by cleavage events in the grapevine degradome. It is possible that some of the identified novel targets are a result of the use of grapevine specific sequences and may not be valid targets in other plant systems. Nevertheless, the differentially expressed miRNAs and their targets identified from this study in own-rooted V. vinifera cv. Merlot vines infected with GRBV can be helpful towards an improved understanding of virus-host interactions. This research also provides further support for the recent discovery that vsiRNAs can also target host transcripts. While the cleavage events were supported by evidence in the degradome, additional validation needs to be done to verify the occurrence of these interactions. Despite this, however, the identified targets do align with what has been observed in other viral systems and represent an interesting potential mechanism for symptom development. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author Contribution N.A., R.S., and R.N. conceived and designed the research project. N.A., D.P., Y.L, and A.S. performed experiments, analyzed, interpreted, and visualized data. S.R. and Y.Z. processed the initial data. N.A. and R.N. wrote the manuscript. S.R., Y.Z., and R.S. edited the manuscript. All authors read and approved the final manuscript. Acknowledgement We thank Drs. 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Supplementary Files SupplementaryFigures.docx SupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 08 Nov, 2024 Reviews received at journal 23 Oct, 2024 Reviewers agreed at journal 11 Oct, 2024 Reviews received at journal 12 Sep, 2024 Reviewers agreed at journal 30 Aug, 2024 Reviewers invited by journal 01 Aug, 2024 Editor invited by journal 30 Jul, 2024 Editor assigned by journal 28 Jul, 2024 Submission checks completed at journal 28 Jul, 2024 First submitted to journal 25 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4803716","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337030343,"identity":"33b04c6f-0993-438e-b787-6602fd125e00","order_by":0,"name":"Noah Ault","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYHACNhAhw8DeAOYxNhCrhYeH5wDJWiQSiNRizt787AFDzWEee8k3xp95GGxkNxwgoMWy55i5AcOxwzw80jlm0jwMacYEtRjcSDCTYGADacndxszDcDiRCC3p3yQY/gG1SJ7dDHTYf2K05JhJMLYBtUjwbgA67AARWs6cKZNI7Evn4TmT/01yjkGy8UyCWo63b5P48M1ajr39WPKHNxV2sn2EtIBBAsIEYpSPglEwCkbBKCAIAFEdPL0UvhnvAAAAAElFTkSuQmCC","orcid":"","institution":"Washington State University - Irrigated Agriculture Research and Extension Center","correspondingAuthor":true,"prefix":"","firstName":"Noah","middleName":"","lastName":"Ault","suffix":""},{"id":337030345,"identity":"545ea4b9-60a0-4c02-ba77-176d570eb0bf","order_by":1,"name":"Shuchao Ren","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shuchao","middleName":"","lastName":"Ren","suffix":""},{"id":337030346,"identity":"75af720b-3bc8-46a1-aa46-628fedd599f6","order_by":2,"name":"David Payne","email":"","orcid":"","institution":"Oklahoma State University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Payne","suffix":""},{"id":337030348,"identity":"e574e07e-e445-4fda-a7ed-337f56be98b9","order_by":3,"name":"Yongfang Li","email":"","orcid":"","institution":"Oklahoma State University","correspondingAuthor":false,"prefix":"","firstName":"Yongfang","middleName":"","lastName":"Li","suffix":""},{"id":337030349,"identity":"e722b736-79a3-40d8-8ec5-84c712796267","order_by":4,"name":"Asha Srinivasan","email":"","orcid":"","institution":"Oklahoma State University","correspondingAuthor":false,"prefix":"","firstName":"Asha","middleName":"","lastName":"Srinivasan","suffix":""},{"id":337030350,"identity":"15dd8549-c87b-40fa-9832-2cca4eec7c7a","order_by":5,"name":"Yun Zheng","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Zheng","suffix":""},{"id":337030352,"identity":"42c426c4-3d68-464f-a912-352e73b1b774","order_by":6,"name":"Ramanjulu Sunkar","email":"","orcid":"","institution":"Oklahoma State University","correspondingAuthor":false,"prefix":"","firstName":"Ramanjulu","middleName":"","lastName":"Sunkar","suffix":""},{"id":337030354,"identity":"df0545ac-6148-45cc-bd32-70ef53de42b5","order_by":7,"name":"Rayapati Naidu","email":"","orcid":"","institution":"Washington State University - Irrigated Agriculture Research and Extension Center","correspondingAuthor":false,"prefix":"","firstName":"Rayapati","middleName":"","lastName":"Naidu","suffix":""}],"badges":[],"createdAt":"2024-07-25 18:21:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4803716/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4803716/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-11539-4","type":"published","date":"2025-04-29T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63101473,"identity":"0bb58ac9-9ff1-4d59-821c-071764d77321","added_by":"auto","created_at":"2024-08-23 07:03:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41257,"visible":true,"origin":"","legend":"\u003cp\u003eGrapevine red blotch virus (GRBV) Genome\u003c/p\u003e\n\u003cp\u003eLocations of viral (green) and complementary (red) sense ORFs in GRBV genome. Created with SnapGene\u003csup\u003e \u003c/sup\u003esoftware [36] and the GRBV RefSeq annotation\u003csup\u003e \u003c/sup\u003e[22].\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4803716/v1/be6564aa348c20ec5462f66c.jpg"},{"id":63102628,"identity":"52593a30-cc1b-4e72-aeca-230c47ddba9e","added_by":"auto","created_at":"2024-08-23 07:11:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57972,"visible":true,"origin":"","legend":"\u003cp\u003eAbundances of miRNAs by length, family, and number of isoforms\u003cbr\u003e\nLength distribution of miRNAs identified from the (A) leaves and (B) berries from GRBV-positive and negative vines collected during pre- and post-veraison. Reads were normalized by RPTM. In leaf samples, 23nt reads were the most abundant during pre-veraison, but 21nt reads were the most abundant during post-veraison. In berries, 21nt reads were the most abundant in samples collected during pre- and post-veraison. Family-wise distribution of miRNAs in (C) leaves and (D) berries from GRBV-negative and GRBV-positive samples collected during pre- and post-veraison. Reads were normalized by RPTM. (E) Number of distinct miRNA isoforms detected from miRNA families. Reads from the miR3634 family were the most abundant in every sampling category except for berry samples at post-veraison from GRBV-positive vines, where miR3623 reads were more abundant. The overabundance of miR3634 family reads was due to the isoform miRNA3634a-3p. This overabundance also contributed to the high 23nt read abundance in leaves. Sampling categories are denoted according to the following abbreviations: pre-veraison [P], post-veraison [PO], leaves [L], berries [B], GRBV-negative [H], GRBV-positive [D].\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4803716/v1/f8688edc08a14c85b9c0b29b.jpg"},{"id":63101475,"identity":"5fee08d1-596e-4854-857c-5d6d1fcd44f2","added_by":"auto","created_at":"2024-08-23 07:03:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92710,"visible":true,"origin":"","legend":"\u003cp\u003eVirus- and viroid-derived siRNA abundance by length\u003cbr\u003e\n(A) Length distribution of sRNA reads mapping to GRBV genome with no mismatches and no nonspecific reads. Length distribution for (B) HSVd and (C) GYSVd from leaf (left) and berry (right) samples. Plotted as the percentage of total reads mapped. Significant comparisons were made in R between sampling categories for each sRNA. Asterisks show significant differences (p \u0026lt; 0.05) between sample categories based on veraison in A and infection status in B and C. Significant differences based on tissue type are not shown. sRNA reads ranging from 25-28 nt long are not shown due to low abundance. Sampling categories are denoted according to the following abbreviations: pre-veraison [P], post-veraison [PO], leaves [L], berries [B], GRBV-negative [H], GRBV-positive [D].\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4803716/v1/a581c14a81d9ecf1d3b5cc90.jpg"},{"id":63101478,"identity":"20a38f95-f9a1-47e4-b07b-c9933a74127e","added_by":"auto","created_at":"2024-08-23 07:03:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106160,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of miRNAs in GRBV-negative vs. GRBV-positive vines\u003cbr\u003e\nNumber of miRNAs differentially expressed in response to infection by GRBV in leaves and berries, either in pre-veraison, post-veraison, or both (venn diagram). The number, direction, and identity of differentially expressed miRNAs in response to infection by GRBV, either in pre-veraison, post-veraison, or both (bar chart). miRNAs in red were downregulated in GRBV-positive relative to GRBV-negative samples, and miRNAs in green were up-regulated GRBV-positive relative to GRBV-negative samples. miRNAs in grey were differentially expressed in different directions in pre- compared to post-veraison samples. Both miR166ax and miR3624a-3p were up-regulated during pre-veraison and down-regulated during post-veraison.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4803716/v1/438c211d7447b6a9ad57a930.png"},{"id":63102630,"identity":"2a58ce1d-3ae2-4b43-85e7-0cdc93e283bf","added_by":"auto","created_at":"2024-08-23 07:11:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":114301,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Expression of miRNAs during pre-veraison vs. post-veraison\u003cbr\u003e\nNumber of miRNAs differentially expressed in post-veraison relative to pre-veraison in leaves and berries, either in GRBV-negaitve plants, GRBV-positive plants, or both (venn diagram). The number, direction, and identity of miRNAs differentially expressed in post-veraison relative to pre-veraison either in GRBV-negative plants, GRBV-positive plants, or both (bar chart). miRNAs in red were downregulated post- relative to pre-veraison, and miRNAs in green were up-regulated post- relative to pre-veraison. miRNAs in grey were differentially expressed in different directions in GRBV-negative compared to GRBV-positive samples. Both miR395o and miR3624a-3p were upregulated in GRBV-negative berries and downregulated in GRBV-positive berries.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4803716/v1/21c29b7848ac11902ff0753f.png"},{"id":63101480,"identity":"fb8b131a-184a-454e-8e54-269860378ce2","added_by":"auto","created_at":"2024-08-23 07:03:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":80458,"visible":true,"origin":"","legend":"\u003cp\u003eAbundance of vsiRNAs specific to GRBV ORFs\u003cbr\u003e\n Abundance (TPM) of sRNAs which mapped to specific GRBV ORFs. Asterisks are indicative of a significant difference between pre- and post-veraison within a tissue type. In berries, sRNAs mapping to V1 were significantly less abundant and sRNAs mapping to C1-3 were significantly more abundant. In leaves, sRNAs mapping to C2 were significantly more abundant in post- relative to pre- veraison. In V2 and C3, the general trend in leaves lined up with what was observed in berries but was not significant. A linearized depiction of the circular GRBV genome (5’ to 3’) is displayed above to illustrate ORF locations within the GRBV genome. This depiction was created using SnapGene software [36] and the RefSeq GRBV annotation [22]. Sampling categories are denoted according to the following abbreviations: pre-veraison [P], post-veraison [PO], leaves [L], berries [B], GRBV-positive [D].\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4803716/v1/f0f39d851940ba95847c1382.png"},{"id":81987843,"identity":"ad82c418-e9ad-4379-b3d8-cff2bc15e7ae","added_by":"auto","created_at":"2025-05-05 16:06:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2159644,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4803716/v1/efaf7832-df37-46cc-bca4-e493aa9ecbd3.pdf"},{"id":63102629,"identity":"cc4e863c-6c23-40e1-ac73-2d5007e158cd","added_by":"auto","created_at":"2024-08-23 07:11:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1117657,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4803716/v1/c13070bd854f8cb1f2bf2a29.docx"},{"id":63101479,"identity":"b52748e8-e9f6-47b3-9744-46ce6946a3c1","added_by":"auto","created_at":"2024-08-23 07:03:27","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":43398,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4803716/v1/946f1ecff01d81ac0e675f04.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamics of small RNAs in a red-fruited wine grape cultivar infected with Grapevine red blotch virus","fulltext":[{"header":"Background","content":"\u003cp\u003eSmall RNAs (sRNAs) are the central component of all RNA silencing pathways in plants due to their regulatory roles in a multitude of developmental and physiological processes, as well as in response to biotic and abiotic stresses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among the different classes of sRNAs characterized in virus-infected plants, microRNAs (miRNAs) and small-interfering RNAs (siRNAs) are important contributors to both transcriptional (TGS) and post-transcriptional gene silencing (PTGS). In addition, plants have evolved sRNA-mediated silencing, or RNA interference (RNAi) as a natural defense strategy to counter viral infections [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. RNAi is an evolutionarily conserved antiviral mechanism mediated by virus-derived siRNAs (vsiRNAs) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Multiple studies have demonstrated that viral infections in plants are closely tied to the accumulation of vsiRNAs, which are consequently able to directly target and silence viral RNA [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnlike siRNAs, miRNAs originate solely from an organism\u0026rsquo;s nuclear genome, either in dedicated \u003cem\u003eMIR\u003c/em\u003e genes, or within introns of other specific genes. Following transcription, miRNA precursors form a stem loop structure and are processed by a Dicer or Dicer-like protein into an miRNA duplex. This duplex is then exported from the nucleus, after which it associates with an argonaut protein forming the miRNA-induced silencing complex (miRISC). The miRNA guides the miRISC to a complementary segment of a target plant mRNA. The miRISC then initiates PTGS and the resulting degradation of the target mRNA [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Sequencing of the plant degradome has been highly effective at identifying the resulting fragments of this degradation and can be used to validate miRNA targets within plant transcriptomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExtensive research into and characterization of miRNAs, including target identification, have revealed that miRNAs are not only important regulators in plant processes ranging from development to environmental stress responses [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], but that miRNA expression itself can vary significantly in response to different conditions, including viral disease [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGrapevine red blotch disease (GRBD), caused by Grapevine red blotch virus (GRBV, genus \u003cem\u003eGrablovirus\u003c/em\u003e and family \u003cem\u003eGeminiviridae\u003c/em\u003e) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] is an economically important viral disease affecting wine grapes (\u003cem\u003eVitis vinifera\u003c/em\u003e L.) in different grapevine-growing regions [27, and cited references]. The disease affects both the yield and quality of grapes produced by infected vines leading to significant reduction in income to growers [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Virus-infected vines produce contrasting symptoms in red- and white-fruited \u003cem\u003eV. vinifera\u003c/em\u003e cultivars. In red-fruited cultivars, initial symptoms on leaves consist of small, irregular, red-colored areas between major veins that expand and coalesce as the season advances to become reddish or reddish-purple irregular blotches. In addition, some red-fruited cultivars show red-colored primary, secondary, and tertiary veins. Interestingly, despite GRBV infecting grapevines systemically and being detectable throughout the season, visual symptoms only begin to appear after veraison (the onset of ripening). Following veraison in infected vines, disease symptoms begin to appear on mature leaves at the basal portion of the shoots. As the season progresses, symptoms spread, developing on leaves positioned higher on shoots as well. In contrast, white-fruited cultivars show mild symptoms that are less conspicuous and can involve irregular chlorotic areas between major veins, sometimes accompanied by necrosis around the leaf margins.\u003c/p\u003e \u003cp\u003eGRBV has a circular single-stranded DNA genome of around 3,200 nucleotides and encodes six clearly defined open reading frames (ORFs), with three overlapping ORFs (C1, C2, and C3) in the complementary sense and the other three (V1, V2, and V3) in the viral sense (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. ORF V1 encodes the predicted viral coat protein (CP), although CP was not detected in infected plants nor virions observed via electron microscopy. The V2 and V3 ORFs are suggested to encode movement proteins based on similarities with other monopartite geminiviruses [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The C1 ORF was predicted to encode RepA, and a spliced transcript encoded by C1 and C2 was predicted to encode Rep. The function of the C3 ORF, which is located internal to and within the same frame as the C1 ORF, is yet to be determined. Recently, the proteins encoded by the C2 and V2 ORFs were identified as suppressors of post-transcriptional gene silencing [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Grapevine red blotch virus (GRBV) Genome\u003c/p\u003e \u003cp\u003eLocations of viral (green) and complementary (red) sense ORFs in GRBV genome. Created with SnapGene software [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and the GRBV RefSeq annotation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, many studies utilizing sRNA profiling have been conducted in virus-infected plants to identify both plant- and virus-derived sRNAs and gather information on associated plant-viral interactions [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These studies were useful not only for building up an information base that can be used to interpret and contextualize various molecular interactions between plants and their viral pathogens, but also for discovering potential tools for treatment of viral plant diseases and the further molecular characterization and population genetics of viral/sub-viral genomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany of these studies focused on annual plants in a defined set of controlled environments. This type of work has only more recently begun to be performed utilizing perennial crops such as grapevine [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Unlike annual crops, the dynamics of sRNAs in virus-infected perennial crops are influenced by seasonal and environmental changes. Although recent studies have advanced knowledge on the epidemiology and impacts of GRBV [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], little is known regarding compatible virus-host interactions at the molecular level. Because red blotch disease symptoms are produced in a phenological stage-specific manner during each season, it can be hypothesized that the dynamics of virus-host interactions are distinct between asymptomatic pre- and symptomatic post-veraison stages. Among these diverse molecular interactions, investigating the regulatory roles of small RNAs (sRNAs) with regard to both infection status and phenological stage could lead to deeper insights into the molecular pathways contributing to symptom development in grapevines grown under natural conditions.\u003c/p\u003e \u003cp\u003eIn this study, high-throughput sRNA sequencing was used to analyze the sRNAome in two different tissue types across two phenological stages from GRBV-infected grapevines (\u003cem\u003eVitis vinifera\u003c/em\u003e cv. Merlot) from a commercial vineyard. Both plant miRNAs and viral/viroidal siRNAs were analyzed to identify patterns in abundance and differential expression, as well as to validate the transcriptomic targets of key sRNAs to gain insight into the molecular interactions underlying symptom expression. These findings provide a foundation for future research to further study the role of sRNA-induced silencing mechanisms in GRBV-grapevine interactions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant Material\u003c/h2\u003e \u003cp\u003eLeaf and berry samples were collected from a commercial vineyard (45\u0026deg;52\u0026rsquo;07\u0026rdquo;N, 119\u0026deg;46\u0026rsquo;30\u0026rdquo;W) planted in 2008 with own-rooted \u003cem\u003eVitis vinifera\u003c/em\u003e cv. Merlot (clone 15) vines and maintained by the grower using standard viticultural practices. Based on grower feedback, the vineyard block was planted with virus-infected cuttings resulting in the introduction of virus into the block at the time of planting. Grapevines were selected for this study in pairs with each pair consisting of one vine showing GRBD symptoms and an adjacent, asymptomatic vine in the same row. They were selected such that each pair of grapevines is from a different row within the vineyard block. Candidate vines were tested initially for GRBV [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] to ensure that the symptomatic vines were positive for GRBV and that the non-symptomatic vines were negative.\u003c/p\u003e \u003cp\u003eThree pairs of vines, with each pair consisting of one symptomatic, GRBV-positive vine and one asymptomatic, GRBV-negative vine, were selected for this study. Both leaf and berry samples were collected separately from individual vines at pre-veraison (early July 2015) and post-veraison (September 2015). This resulted in eight distinct sampling categories based on the combination of timepoint, tissue type, and infection status. These sampling categories have been denoted according to the abbreviations, P (pre-veraison), PO (post-veraison), L (leaves), B (berries), H (GRBV-negative), and D (GRBV-positive), such that \u0026ldquo;PLH\u0026rdquo; would reference pre-veraison leaf samples from GRBV-negative vines.\u003c/p\u003e \u003cp\u003eThe samples were snap-frozen in liquid N\u003csub\u003e2\u003c/sub\u003e immediately after harvesting and transported in liquid N\u003csub\u003e2\u003c/sub\u003e to maintain the integrity of the samples until they could be stored at -80\u0026deg;C. Thus, individual leaf and berry samples collected from each vine are considered as one biological replicate for downstream applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePreparation, Sequencing, and Mapping of sRNA and RNA Libraries\u003c/h2\u003e \u003cp\u003eSmall RNAs were extracted from frozen leaf and berry tissues using a mirPremier\u0026reg; microRNA isolation kit (Sigma-Aldrich, MO) by following the protocol provided by the manufacturer. Separate RNA libraries were generated from these samples following the protocol discussed in Alabi et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and Li et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The quantity of sRNA and RNA preparations was assessed based on 260nm/280nm OD values using a NanoDrop 2000c spectrophotometer. The integrity of sRNA and RNA preparations was measured using the 2100 Bioanalyzer system (Agilent Technologies, SantaClara, CA). Preparations with 260/280 absorbance ratio from 1.8-2.0 and an RNA integrity number (RIN) higher than 7.0 were used for library preparations. High quality sRNA and RNA samples were shipped to BGI Genomics [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] for library construction and Illumina 50SE sequencing using the Hiseq 4000 system (RRID:SCR_016386). Subsequently, low quality reads and adapter sequences were removed using Trimmomatic version 0.39 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], and read quality was checked with FastQC. \u003cem\u003eV. vinifera\u003c/em\u003e miRNAs were identified and mapped to the 12X.v2 grapevine reference genome assembly [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], alongside several other databases including premiRBase21, Rfam, Silva, TIGR Plant Repeat Databases, and Repbase, as described in Suo et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Following mapping, all miRNAs without a minimum of 10 counts in at least one sample were removed. The remaining miRNAs were normalized to RPTM. Additionally, at this stage, one replicate (PLD2) was removed prior to further analysis due to quality concerns.\u003c/p\u003e \u003cp\u003esRNA reads were also mapped to a database containing all GenBank virus and viroid sequences under 23kb in length [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Two different methods for mapping the sRNA reads to the database were tested. In the first method, reads were first mapped to the Ensembl grapevine genome assembly (version 12X.v2), and then the unmapped sequences were mapped to the viral database. In the second method, the trimmed sequences were mapped directly to the viral database. There was minimal difference in the total number of mapped reads between these two methods. Additionally, there were none-to-minimal reads mapping to the GRBV genome in libraries from samples from GRBV-negative vines, even when reads were mapped directly to the viral database. The second method was ultimately used for all subsequent analyses. Mapping was performed requiring perfect matches and excluding reads which mapped non-specifically. The sRNA reads were also exclusively mapped to the grapevine genome with the same method for the determination of the length profile of sRNAs in the host.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003esRNA Length Distribution Analysis\u003c/h2\u003e \u003cp\u003eThe abundances of different lengths of sRNAs which mapped to the \u003cem\u003eVitis vinifera\u003c/em\u003e, GRBV, HSVd, and GYSVd genomes were determined using a custom python script. R Statistical Coding Language version 3.6.3 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and the \u0026ldquo;dplyr\u0026rdquo;, \u0026ldquo;car\u0026rdquo;, \u0026ldquo;xtable\u0026rdquo;, and \u0026ldquo;gridExtra\u0026rdquo; packages were used for the statistical analysis. Read counts were normalized by sample via the \u0026lsquo;scale\u0026rsquo; function. The \u0026lsquo;aov\u0026rsquo; function was used to perform two-way ANOVAs looking at the effects of both infection and veraison status on the prevalence of each read length. Levene tests were performed using the \u0026lsquo;leveneTest\u0026rsquo; function. Tukey post-hoc analyses were performed on all of the ANOVAs using the \u0026lsquo;TukeyHSD\u0026rsquo; function. Results were organized and exported using the \u0026lsquo;xtable\u0026rsquo;, \u0026lsquo;rbind\u0026rsquo;, and \u0026lsquo;grid.table\u0026rsquo; functions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Viral and Viroidal RNA Abundance\u003c/h2\u003e \u003cp\u003eThe RNA sequencing libraries, which were derived from the same samples as the sRNA sequencing libraries, were mapped to the Genbank viral/viroidal database [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] using CLC Genomics Workbench, to verify the presence of GRBV, HSVd, and GYSVd, as well as the absence of any other viruses or viroids. CLC Genomics Workbench was also used to assemble the RNA sequencing data \u003cem\u003ede novo\u003c/em\u003e. Generated \u003cem\u003ede novo\u003c/em\u003e assemblies were identified using NCBI BLAST [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Following \u003cem\u003ede novo\u003c/em\u003e assembly, the counts per million mapped reads (CPM) for the \u003cem\u003ede novo\u003c/em\u003e assemblies of GRBV, HSVd, and GYSVd-1 were calculated. Two-way ANOVAs, accompanied by Levene\u0026rsquo;s tests and Tukey\u0026rsquo;s post-hoc tests, were performed in R, using the same functions and packages described above, to compare viral and viroidal RNA abundance across sampling categories (leaf and berry, pre- and post-veraison, infected and healthy).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGrapevine Degradome Sequencing and Target Identification Analysis\u003c/h2\u003e \u003cp\u003eThe protocols described by Li et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] were used to prepare and sequence four degradome libraries, representing GRBV-positive and GRBV-negative leaf samples taken at both pre- and post-veraison. Trimmomatic version 0.39 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] was used to prepare the four libraries for analysis. The first six nucleotides of each read were cropped. Reads shorter than 23 nucleotides were excluded and reads over 28 nucleotides in length were trimmed down to 25 nucleotides. Additionally, an overrepresented sequence (\u0026gt;\u0026thinsp;25%) which mapped to \u003cem\u003eV. vinifera\u003c/em\u003e chloroplastic DNA was removed prior to analysis.\u003c/p\u003e \u003cp\u003eCleaveLand version 4.5 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] was used to identify targets in the degradome libraries. GSTAr version 1.0 and RNAplex version 2.4.17 [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], Perl version 5.26.1 [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], R version 3.2.2 [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], Samtools version 1.1.0 [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], and Bowtie version 1.0.0 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] were dependencies utilized within CleaveLand. The CleaveLand script was modified with regard to the section involving Bowtie, such that the default parameters (-k 1 -best) were altered (-a -m 12 -best -strata) to allow for reads to map to multiple loci, so long as they did not map to more than twelve separate loci.\u003c/p\u003e \u003cp\u003eThe cDNA annotation was obtained from Ensembl Plants version 54 [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The TAS3 sequences LOC100244732 and LOC104879803 were added manually from NCBI. Grape miRNA sequences were obtained from miRBase [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The vvi-tas3 sequences were derived from LOC100244732 and LOC104879803 based on their homology to known 21nt tas3 sequences. Additionally, a series of custom Python version 3.8.3 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] scripts was used to parse the sRNAs which mapped to the GRBV genome, record the fifty most abundant unique sequences from each GRBV-positive sample, and combine them into a non-redundant list, which was then used in the CleaveLand pipeline. This was chosen as an alternative to running all of the unique mapped sequences due to time and computational constraints. In total, 83 unique sRNA sequences were included. Custom Python version 3.8.3 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] scripts were used to determine statistics such as the 9nt, 10nt, and 11nt cleavage counts, the degradome peak ranks, valid reads, the total number of degradome reads per transcript, and percent reads valid.\u003c/p\u003e \u003cp\u003eDue to the lack of gene descriptions on the \u003cem\u003eV. vinifera\u003c/em\u003e annotation used, BLASTp (from BLAST\u0026thinsp;+\u0026thinsp;version 2.10.1) was used to match proteins from \u003cem\u003eV. vinifera\u003c/em\u003e with those from \u003cem\u003eA. thaliana\u003c/em\u003e with an e-value cutoff of 1e-10. Protein sequences for both species were obtained from Ensembl Plants version 54. Each \u003cem\u003eV. vinifera\u003c/em\u003e gene was assigned a homologue from \u003cem\u003eA. thaliana\u003c/em\u003e based on the strongest BLAST hit (if any). Annotations for the \u003cem\u003eA. thaliana\u003c/em\u003e genes were derived from the Ensembl Plants version 54 annotations. For genes without \u003cem\u003eA. thaliana\u003c/em\u003e homologues, target gene functions were determined through use of NCBI\u0026rsquo;s conserved domain search function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003emiRNA Differential Expression Analysis\u003c/h2\u003e \u003cp\u003eThe differential expression analysis was performed in the R Statistical Coding Language version 3.6.3 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] using the \u0026ldquo;edgeR\u0026rdquo; (version 3.28.1) package by Bioconductor [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], with a generalized likelihood ratio model. Prior to the analysis, miRNAs that did not have more than one read in at least six samples were removed. Instead of the RPTM normalized read counts, the raw read counts were used in conjunction with the \u0026lsquo;calcnormfactors\u0026rsquo; function in edgeR. The \u0026lsquo;DGEList\u0026rsquo;, \u0026lsquo;glmFit\u0026rsquo;, and \u0026lsquo;glmLRT\u0026rsquo; functions were used to conduct the differential expression analysis, which was performed as a series of pairwise comparisons between groups utilizing the \u0026lsquo;makecontrasts\u0026rsquo; function. To determine significance, a p-value cutoff of \u0026lt;\u0026thinsp;0.05 and log(FC) cutoff of \u0026gt;|1.0| was used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMapping sRNAs to GRBV Genome\u003c/h2\u003e \u003cp\u003eCLC Genomics Workbench was used to map the sRNA reads which had been previously mapped to the GRBV genome in the viral database to a gene-annotated version of the GRBV genome. This was done, as opposed to utilizing the entirety of the trimmed reads, to prevent the inclusion of the previously excluded non-specific reads. The results of this mapping were analyzed using the \u0026ldquo;edgeR\u0026rdquo; package by Bioconductor [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] in R. Reads were normalized using the \u0026lsquo;calcnormfactors\u0026rsquo; function. Reads mapping to the GRBV ORFs were analyzed for differential expression using the \u0026lsquo;DGEList\u0026rsquo;, \u0026lsquo;glmFit\u0026rsquo;, amd \u0026lsquo;glmLRT\u0026rsquo; functions. This was done via a series of pairwise comparisons between experimental groups set up using the \u0026lsquo;makecontrasts\u0026rsquo; function. To determine significance, an FDR cutoff of \u0026lt;\u0026thinsp;0.05 and log(FC) cutoff of \u0026gt;|0.2| was used.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRT-PCR Assays of Grapevine Samples\u003c/h2\u003e \u003cp\u003eThe petioles of mature leaves were collected from Merlot vines exhibiting the symptoms of GRBD as well as adjacent, asymptomatic vines and tested for GRBV. Only symptomatic vines tested positive for the virus (GRBV). By contrast, both symptomatic and asymptomatic samples tested positive for two viroids, grapevine yellow-speckle viroid 1 (GYSVd-1) and hop stunt viroid (HSVd). High-throughput sequence analysis of small RNAs described below confirmed the presence of GRBV only in symptomatic vines and two viroid species (HSVd and GYSVd-1) in both symptomatic and non-symptomatic vines. None of the samples tested positive for other viruses or viroids. These results were used to select three symptomatic vines that tested positive for GRBV and three asymptomatic, GRBV-negative vines adjacent to GRBV-positive vines, for a total of six vines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003esRNA Sequencing and Mapping\u003c/h2\u003e \u003cp\u003eA total of 24 sRNA libraries were constructed and sequenced using the Illumina sequencing platform. These 24 libraries represented three biological replicates for leaf and berry samples harvested at pre- and post-veraison stages from symptomatic, GRBV-positive vines and non-symptomatic, GRBV-negative vines. The clean and mapped read numbers in each individual library are shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Per each of the eight sampling categories, there was an average of roughly 598,000 unique reads (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross all libraries, there were a total of 560,479,564 raw reads. Following the removal of low-quality reads and trimming for adapter sequences, a total of 23,129,077 clean reads remained. These reads were mapped to the 12X.v2 grapevine reference genome assembly [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], alongside several other databases (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), as described in Suo et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Following the removal of reads shorter than 19 and longer than 24 nucleotides, 2,210,559 reads remained that were successfully mapped to miRNA sequences. After filtering out reads with less than 10 counts present in a single sample, the remaining reads were mapped to 140 unique miRNAs belonging to 42 miRNA families using the methodology described in Suo et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Among the mapped reads, 21 and 23 nt-long reads were the most common (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA \u0026amp; B). Following normalization (RPTM), eight miRNAs had over 1,000 RPTM across all replicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC \u0026amp; D). One specific miRNA, miR3634a-3p, had a disproportionately high number of normalized counts relative to the other miRNAs identified. miR3634a-3p had 45,335 RPTM between all replicates, while the next most prevalent miRNA, miR3623a-3p, had 10,406 RPTM (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Three miRNA families (miR166, miR319, and miR396) were represented by more than ten distinct miRNA isoforms. Six other miRNA families (miR156, miR159, miR162, miR167, miR395, and miR398) were represented by five or more isoforms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Abundances of miRNAs by length, family, and number of isoforms\u003c/p\u003e \u003cp\u003eLength distribution of miRNAs identified from the (A) leaves and (B) berries from GRBV-positive and negative vines collected during pre- and post-veraison. Reads were normalized by RPTM. In leaf samples, 23nt reads were the most abundant during pre-veraison, but 21nt reads were the most abundant during post-veraison. In berries, 21nt reads were the most abundant in samples collected during pre- and post-veraison. Family-wise distribution of miRNAs in (C) leaves and (D) berries from GRBV-negative and GRBV-positive samples collected during pre- and post-veraison. Reads were normalized by RPTM. (E) Number of distinct miRNA isoforms detected from miRNA families. Reads from the miR3634 family were the most abundant in every sampling category except for berry samples at post-veraison from GRBV-positive vines, where miR3623 reads were more abundant. The overabundance of miR3634 family reads was due to the isoform miRNA3634a-3p. This overabundance also contributed to the high 23nt read abundance in leaves. Sampling categories are denoted according to the following abbreviations: pre-veraison [P], post-veraison [PO], leaves [L], berries [B], GRBV-negative [H], GRBV-positive [D].\u003c/p\u003e \u003cp\u003eThe trimmed sRNA reads from all 24 libraries were also mapped to a GenBank viral/viroidal database containing all viral and viroidal genomes less than 23kb in length using CLC Genomics Workbench 2021. Mapping parameters were set to exclude any nonspecific results and disallow for mismatches. The results are summarized in Table S3.\u003c/p\u003e \u003cp\u003eFor reads mapped to the GRBV genome, 25nt and longer reads made up minimal (\u0026lt;\u0026thinsp;2%) portions of the overall read distribution. 21nt reads were the most common in all sampling categories. Notably, 21nt sRNAs were significantly less abundant in post-veraison berries than in pre-veraison berries (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). 20nt and 22nt reads showed an inverse relationship, where they were more abundant in post-veraison berry samples. There was also a relatively low abundance of 24nt vsiRNAs compared to 21nt and 22nt vsiRNAs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Virus- and viroid-derived siRNA abundance by length\u003c/p\u003e \u003cp\u003e(A) Length distribution of sRNA reads mapping to GRBV genome with no mismatches and no nonspecific reads. Length distribution for (B) HSVd and (C) GYSVd from leaf (left) and berry (right) samples. Plotted as the percentage of total reads mapped. Significant comparisons were made in R between sampling categories for each sRNA. Asterisks show significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between sample categories based on veraison in A and infection status in B and C. Significant differences based on tissue type are not shown. sRNA reads ranging from 25\u0026ndash;28 nt long are not shown due to low abundance. Sampling categories are denoted according to the following abbreviations: pre-veraison [P], post-veraison [PO], leaves [L], berries [B], GRBV-negative [H], GRBV-positive [D].\u003c/p\u003e \u003cp\u003eAdditionally, all samples had substantial quantities of reads that mapped with high coverage to both HSVd and GYSVd-1. Substantial quantities of reads from sRNA libraries from GRBV-positive vines mapped to the GRBV genome with high coverage, while reads from GRBV-negative samples had negligible amounts of reads mapped to GRBV and poor coverage. No other viruses or viroids had substantial read abundance and high coverage in libraries from any samples.\u003c/p\u003e \u003cp\u003eAs in the GRBV-derived vsiRNAs, reads 25nt and longer constituted a negligible portion of the overall read distributions in both HSVd and GYSVd. In leaves, 21nt reads were the most abundant, and 24nt reads were the second-most (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB \u0026amp; C). In berries, 21nt and 24nt read abundances were roughly equal. There were no notable significant differences between sampling categories in either berries or leaves in HSVd. For GYSVd, 23nt reads were significantly more abundant in post-veraison berries than in pre-veraison berries in GRBV-negative samples. In GRBV-positive samples, there was no significant difference between pre- and post-veraison berries, nor in any of the leaf samples. There was a trend towards 23nt reads being higher in GRBV-positive leaves at post-veraison than at pre-veraison, but this trend was not observed in GRBV-negative samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEstimation and Analysis of Viral and Viroidal Titer from RNAseq Data\u003c/h2\u003e \u003cp\u003e24 RNA libraries were constructed and sequenced from the same samples as the sRNA libraries (unpublished data). The RNA sequencing libraries were mapped to the Genbank viral and subviral database to confirm the presence of GRBV in GRBV-positive samples, its absence in GRBV-negative samples, the presence of GYSVd-1 and HSVd in all samples, and the absence of any other viral or subviral entities in any of the samples. All three genomes were also successfully assembled \u003cem\u003ede novo\u003c/em\u003e in all relevant samples, except for one sample (PLH1), in which the HSVd genome was not successfully assembled, despite having a comparable number of reads which mapped in the previous step.\u003c/p\u003e \u003cp\u003eGRBV RNAs were significantly more abundant in leaf tissue than in berry tissue, but there was no significant difference between pre-veraison and post-veraison timepoints in either tissue type (Table S4). GYSVd-1 RNA abundance was not significantly affected by phenological stage or by GRBV infection in either tissue type. HSVd RNA was significantly more abundant in post-veraison leaves than pre-veraison leaves, but significantly less abundant in post-veraison berries than pre-veraison berries. Additionally, in both pre- and post-veraison berries, HSVd RNAs were significantly more abundant in GRBV-positive samples than in GRBV-negative samples (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Table S4). A similar trend was observed in post-veraison leaves, but it was not statistically significant. This indicates a potential synergistic interaction between GRBV and HSVd, but not between GRBV and GYSVd-1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSmall RNA Target identification using Degradome Analysis\u003c/h2\u003e \u003cp\u003eFour degradome libraries were prepared and sequenced from GRBV-positive and GRBV-negative leaf samples taken at both pre- and post-veraison. Across all four libraries, there were 66,492,906 raw reads. The first six nucleotides were trimmed from all of the raw reads with Trimmomatic 0.39 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Additionally, reads with fewer than 23 nucleotides were removed, and reads longer than 28nt were trimmed to 25 nt. An overrepresented sequence mapping to chloroplastic DNA was also removed, resulting in a total of 38,204,856 clean reads. The cleaned degradome reads were used to identify miRNA targets within the \u003cem\u003eVitis vinifera\u003c/em\u003e transcriptome using CleaveLand version 4.5 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This was performed using the Ensembl Plants version 54 [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] annotation of the \u003cem\u003eV. vinifera\u003c/em\u003e transcriptome. In the pooled degradome from leaves of GRBV-negative vines, a total of 9,241,719 reads were reported to have at least one alignment to the transcriptome. In the degradome from leaves of GRBV-positive vines, this total was 14,059,040 reads. Between both pools, only 75,081 reads were omitted due to the use of the (-m) argument.\u003c/p\u003e \u003cp\u003eTarget gene annotations were determined by assigning homologues from the Ensembl Plants version 54 \u003cem\u003eA. thaliana\u003c/em\u003e annotation. This allowed for the identification of a total of 58 conserved targets for 18 miRNA families. Additionally, three targets for tasiRNA3 were identified (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). T-plots for select targets are shown in Figure S3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDegradome validation of miRNA family targets and target functions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTranscript\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAllen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eGRBV- Leaves\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eGRBV\u0026thinsp;+\u0026thinsp;leaves\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTarget function\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCat.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCat\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003emiR156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi10g04328_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003esquamosa_promoter_binding_protein-like_3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi12g00280_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003esquamosa_promoter_binding_protein-like_4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi11g00909_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003esquamosa_promoter_binding_protein-like_2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi17g00473_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSquamosa_promoter-binding_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi01g01837_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSquamosa_promoter-binding_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi01g01660_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003esquamosa_promoter_binding_protein-like_2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi01g01678_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSquamosa_promoter-binding_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi17g00100_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSquamosa_promoter-binding_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi13g01266_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein_33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003emiR160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi18g00337_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin_response_factor_17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi13g02058_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin_response_factor_16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi06g00272_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin_response_factor_10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi08g01033_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin_response_factor_16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi15g00864_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003edicer-like_1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emiR164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi17g00622_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNAC_domain_containing_protein_100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi19g01484_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNAC_domain_containing_protein_1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003emiR166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi09g00310_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHomeobox-leucine_zipper_family_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi06g00276_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHomeobox-leucine_zipper_family_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi13g00609_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHomeobox-leucine_zipper_family_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi10g00913_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHomeobox-leucine_zipper_family_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi04g00287_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHomeobox-leucine_zipper_family_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emiR167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi04g00824_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin_response_factor_8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi10g00854_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin_response_factor_8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi12g00102_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin_response_factor_6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi17g01218_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eargonaute 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emiR169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi08g01883_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003enuclear_factor_Y,_subunit_A1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi09g00133_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003enuclear_factor_Y,_subunit_A3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi08g00292_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003enuclear_factor_Y,_subunit_A10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emiR171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi04g01247_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eGRAS_family_transcription_factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi15g00680_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eGRAS_family_transcription_factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi02g00536_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eGRAS_family_transcription_factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emiR172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi13g00529_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003erelated_to_AP2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi06g00360_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003erelated_to_AP2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi07g01706_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAPETALA2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emiR319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi06g01139_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein_33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi12g00219_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTCP_family_transcription_factor_4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003emiR393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi00g04585_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF-box/RNI-like_superfamily_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi07g00248_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF-box/RNI-like_superfamily_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi14g04156_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF-box/RNI-like_superfamily_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi14g01482_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin_signaling_F-box_3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi18g00363_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003esulfate_transporter_2;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emiR396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi02g00796_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eleucine_zipper_transcription_factor_16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi08g01498_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003egrowth-regulating_factor_4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi02g00239_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003egrowth-regulating_factor_8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003emiR398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi14g02607_t005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ecopper/zinc_superoxide_dismutase_1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi06g01349_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ecopper/zinc_superoxide_dismutase_2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi02g00444_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ecopper_chaperone_for_SOD1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi11g01445_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eblue-copper-binding_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emiR828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi17g00822_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein_66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi02g01732_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein_66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi14g03020_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein_23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003emiR858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi14g00974_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein_4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi11g00097_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi06g00414_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein_59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi07g00393_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein_12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi09g00112_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein_7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi04g00160_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein_66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi08g01797_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003emyb_domain_protein_5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003etasiRNA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi01g01759_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin_response_factor_2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVitvi10g00510_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin-responsive_factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin-responsive_factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi17g00036_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eauxin_response_factor_2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eCleavage position (site) and Allen score (Allen) for miRNA targets. Number of valid reads, percentage of total reads that are valid, and category value for degradome validation in both GRBV-negative (GRBV-) and GRBV-positive (GRBV+) leaves. Target functions based on \u003cem\u003eA. thaliana\u003c/em\u003e annotation and further supported by NCBI BLAST results.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/strong\u003e \u003cp\u003eCurrently located at the end of the document.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTo find potential novel targets, the remaining identifications were screened according to strict criteria of having greater than 10 total valid reads and greater than 5% valid reads. In this manner, 41 novel targets of 19 miRNA families were identified, over half of which exhibited greater than 25% valid reads in one or both pooled degradomes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S5).\u003c/p\u003e \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTarget function annotations for novel targets.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003eQuery\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eTranscript\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eArabidopsis homologue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eGene description\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi12g00209_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT2G42570.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eTRICHOME_BIREFRINGENCE-LIKE_39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi03g00206_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT3G12500.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003ebasic_chitinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi09g02081_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eATMG00510.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003e*Complex 1 49kDa superfamily;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003erespiratory-chain NADH dehydrogenase, 49 Kd subunit (cl21493)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi09g02090_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eATMG00510.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eInterval 16-1155; E 2.01e-62; Bit 205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR3476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi03g00706_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT1G04945.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eHIT-type_Zinc_finger_family_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi13g01803_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT1G04945.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi15g04391_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT1G04945.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi15g04396_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT1G04945.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi15g04403_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT1G04945.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi16g00437_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT3G06530.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eARM_repeat_superfamily_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR3623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi18g00591_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT5G49650.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003exylulose_kinase-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi16g00880_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT2G45590.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eProtein_kinase_superfamily_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR3624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi10g00241_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT4G16380.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eHeavy_metal_transport/detoxification_superfamily_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi10g00245_t002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT4G16380.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi08g01651_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT3G57000.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003enucleolar_essential_protein-like_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi13g01167_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT3G57000.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR3629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi13g01758_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT5G56710.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eRibosomal_protein_L31e_family_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR3633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi03g00203_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT4G35250.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eNAD(P)-binding_Rossmann-fold_superfamily_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi03g04485_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003e*Protein of Unknown Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi10g04333_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003e*Protein of Unknown Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi07g01217_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT4G02600.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eSeven_transmembrane_MLO_family_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR3634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi19g04623_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003e*Ank 2 superfamily; Ankyrin repeats (3 copies); cl39094;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eInterval 212\u0026ndash;370; E 3.27e-04; Bit 38.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR3635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi03g01290_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT3G12750.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003ezinc_transporter_1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi06g01295_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT2G28000.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003echaperonin-60alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi01g00876_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT1G59640.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003etranscription_factor_BIG_PETAL_P_(BPE)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi06g04411_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT2G27970.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eCDK-subunit_2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi06g01133_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT1G50420.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003escarecrow-like_3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi02g01038_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT5G26742.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eDEAD_box_RNA_helicase_(RH3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi09g00019_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT1G72330.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003ealanine_aminotransferase_2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi07g02021_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT1G04290.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eThioesterase_superfamily_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi17g00218_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT1G18210.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eCalcium-binding_EF-hand_family_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi11g00481_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT5G20200.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003enucleoporin-like_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi18g02996_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003e*Protein of Unknown Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi09g01354_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT1G51580.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eRNA-binding_KH_domain-containing_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi13g04689_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT3G14470.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eNB-ARC_domain-containing_disease_resistance_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi14g01187_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT5G16120.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003ealpha/beta-Hydrolases_superfamily_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR5139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi18g01220_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT3G14430.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eGRIP/coiled-coil_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi14g04439_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003e*Protein of Unknown Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi14g04415_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT3G18280.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eBifunctional_inhibitor/lipid-transfer_protein/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eseed_storage_2S_albumin_superfamily_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 8.6626%;\"\u003e\n \u003cp\u003emiR894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 18.5628%;\"\u003e\n \u003cp\u003eVitvi14g04418_t001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003eAT3G18280.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eBifunctional_inhibitor/lipid-transfer_protein/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 52.8009%;\"\u003e\n \u003cp\u003eseed_storage_2S_albumin_superfamily_protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 95.014%;\"\u003eTarget functions are based on \u003cem\u003eA. thaliana\u003c/em\u003e annotation and further supported by NCBI BLAST results. Target functions marked with an asterisk (*) either lacked an annotated function in the \u003cem\u003eA. thaliana\u003c/em\u003e annotation of the \u003cem\u003eA. thaliana\u003c/em\u003e homologue or lacked an \u003cem\u003eA. thaliana\u003c/em\u003e homologue altogether. In this case, functions were annotated using the NCBI Conserved Domain Search function. In some cases, no matching domains were found, in which case they were marked as proteins of unknown function.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/strong\u003e \u003cp\u003eCurrently located at the end of the document.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eUsing the methods described above, putative targets were also identified for a list of sRNAs which had specifically mapped to the GRBV genome. Valid targets were manually curated based on relaxed cutoffs (\u0026gt;\u0026thinsp;2% Valid Reads and \u0026gt;\u0026thinsp;4 10nt reads). The t-plots and transcript alignments of the manually selected targets were then considered before the final list of valid targets was chosen (Figure S4). These analyses were performed twice, once using the degradome data from healthy samples, and once using the degradome data from infected samples (Table S6). This was done to determine if cleavage events from GRBV-specific sRNAs also had support in GRBV-negative samples, which could indicate that the observed degradome support may not be tied to the identified cleavage event. Degradome support for the identified targets did vary based on which degradome data was used. The overwhelming majority of potential targets either lacked substantial alignment at the 5\u0026rsquo; end or had poor degradome support. In the end, only fourteen targets of eight different virus-derived sRNAs were selected.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGRBV-derived siRNAs with putative targets in \u003cem\u003eV. vinifera\u003c/em\u003e transcriptome\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003esRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStart-Stop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eORF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAACGACGTGTCTGGTGGAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1246\u0026ndash;1266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACGACTGGGAGGAGTTCTGCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e761\u0026ndash;781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGGTGTTGTGCTTCCGTCGGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e946\u0026ndash;966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGATGGGTTAGGGGATGAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e389\u0026ndash;409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA5-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGGGCTATATCATTGGGAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2222\u0026thinsp;\u0026minus;\u0026thinsp;2202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA6-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGTGGCAATGACTCCTGCGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1192\u0026thinsp;\u0026minus;\u0026thinsp;1172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTGTGGTGATGATGATGGGTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e378\u0026ndash;398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA7b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTTGTGGTGATGATGATGGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e377\u0026ndash;397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSequences of identified grbvasRNAs with potential targets in the \u003cem\u003eV. vinifera\u003c/em\u003e genome and locations within the GRBV genome. The \u0026ldquo;-3p\u0026rdquo; suffix indicates that the detected vsiRNA sequence was the reverse complement of the associated sequence in the GRBV reference genome. The other identified vsiRNAs aligned normally in the 5\u0026rsquo;-3\u0026rsquo; direction.\u003c/p\u003e \u003cp\u003eFor ease of reference, the eight identified vsiRNAs were assigned identifiers grbvasRNA1-7. Instead of using grbvasRNA8, a grbvasRNA7a and grbvasRNA7b were used, since the two sequences differed only by a single nucleotide (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Interestingly, all but one of these eight vsiRNAs are mapped to ORFs V1 and V2, while only grbvasRNA5 is located within the complementary-sense C1 ORF. It is worth noting that, among the eight identified vsiRNAs, it is the 3p forms of grbvas5 and grbvas6 with identified targets. This is expected for grbvas5, as it is within the C2 ORF, which is normally transcribed in the 3\u0026rsquo;-5\u0026rsquo; direction. However, grbvas6 is derived from the V1 ORF, which is transcribed in the 5\u0026rsquo;-3\u0026rsquo; direction. This indicates that for this particular vsiRNA to accumulate, it would need to have a source other than the normal transcription of the V1 ORF, such as read-through transcription in the 3\u0026rsquo;-5\u0026rsquo; direction.\u003c/p\u003e \u003cp\u003eFor the selected vsiRNA targets, gene annotations were identified using NCBI\u0026rsquo;s Conserved Domain Search function [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. These targets included proteins involved in intracellular transport, photosynthesis, apoptosis, transcription, translation, DNA/RNA recognition, binding, and maintenance (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table S7).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdentified targets of GRBV-derived siRNAs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTarget Function (Conserved Domain Search)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003egrbvasRNA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi01g00128_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMu homology domain (MHD) of adaptor protein (AP) coat protein I (COPI) delta subunit, cd09254; delta subunit of the F-COPI complex, N-terminal domain, cd14830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi01g00128_t002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC-terminal domain of adaptor protein (AP) complexes medium mu subunits and its homologs, cl10970, member cd09254: AP_delta-COPI_MHD; delta subunit of the F-COPI complex, N-terminal domain, cd14830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003egrbvasRNA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi06g00398_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRibosomal L29 protein, pfam00831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi07g01275_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ephotosystem II oxygen-evolving enhancer protein 1, PLN00037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi19g00148_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApoptosis inhibitory protein 5 (API5), pfam05918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi09g00707_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRNA recognition motif (RRM) superfamily, cl17169, member cd12288: RNA recognition motif (RRM) found in plant proteins related to the La autoantigen; inosine-5'-monophosphate dehydrogenase, cl33447, member PLN02274, inosine-5'-monophosphate dehydrogenase; inosine-5'-monophosphate dehydrogenase cl36546, member PTZ00314, inosine-5'-monophosphate dehydrogenase, Provisional\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003egrbvasRNA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi15g01427_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZn-finger in Ran binding protein and others, pfam00641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi15g01427_t002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZn-finger in Ran binding protein and others, pfam00641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi19g01704_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuperfamily II DNA and RNA helicase [Replication, recombination and repair], COG0513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003egrbvasRNA7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi17g00483_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePPR repeat family, pfam13041; maturation of RBCL 1, cl33664 member PLN03218; Uncharacterized protein cl23818 member PRK00976: methanogenesis marker 12 protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi18g01006_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChloroplast import apparatus Tic20-like, cl15935 member TIGR00994: 3a0901s05TIC20 chloroplast protein import component, Tic20 family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi18g01006_t002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChloroplast import apparatus Tic20-like, cl15935 member TIGR00994: 3a0901s05TIC20 chloroplast protein import component, Tic20 family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi18g01006_t003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChloroplast import apparatus Tic20-like, cl15935 member TIGR00994: 3a0901s05TIC20 chloroplast protein import component, Tic20 family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrbvasRNA7b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitvi11g00285_t001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA-binding domain in plant proteins such as APETALA2 and EREBPs, smart00380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Expression of miRNA Between Samples\u003c/h2\u003e \u003cp\u003eTo compare miRNA expression levels between GRBV-infected and noninfected samples and between the two phenological stages, read counts were analyzed in R Statistical Language [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] using the package \u0026lsquo;edgeR\u0026rsquo; by Bioconductor. Due to a large number of miRNAs that were detected in very low counts and only in certain categories or replicates, miRNAs lacking at least one count in a minimum of six different samples were excluded from the analysis, leaving 99 unique miRNAs. Significant differential expression was determined through use of the Likelihood Ratio Model. After applying cutoffs (P-Value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; |log2(FC)| \u0026gt;1.0), a total of 63 unique miRNAs belonging to 28 different families were significantly differentially expressed between the comparisons made. P-value was chosen as the cutoff metric for this analysis, instead of the more conservative FDR value, due to the extremely high variance in the data (common dispersion\u0026thinsp;\u0026gt;\u0026thinsp;0.8). In the following sections, the term \u0026ldquo;differentially expressed\u0026rdquo; is used to indicate miRNAs that passed these cutoffs regarding a specific comparison (i.e., GRBV\u0026thinsp;+\u0026thinsp;vs. GRBV- or pre- vs. post-veraison).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in miRNA Expression Between GRBV-negative and GRBV-positive Samples\u003c/h2\u003e \u003cp\u003eWhen comparing the miRNA expression patterns in GRBV-positive samples relative to GRBV-negative samples, a total of 41 miRNAs across 18 different miRNA families showed differential expression (Table S8). Three miRNAs, miR166ax, miR3624a-3p, and miR482aw, were differentially expressed in both pre- and post-veraison berries. Similarly, miR396j was differentially expressed in both pre- and post-veraison leaves. Including miR396j, twelve miRNAs were only differentially expressed in the leaves, while 24 were only differentially expressed in berries. Pre-veraison berries and post-veraison leaves each had differentially expressed miRNAs belonging to a single family that was uniquely represented within the corresponding category. They are miR156 and miR2950, respectively, in berry and leaf samples. Post-veraison berries exhibited three such families, miR159, miR162, and miR166. The miRNA3624 family was uniquely differentially expressed within berry samples.\u003c/p\u003e \u003cp\u003eOf the 30 different miRNAs that were significantly differentially regulated, seven miRNA families were upregulated and 16 miRNAs from three different families were downregulated in response to viral infection (Table S8). There was no overlap between leaf and berry sample categories in down regulated miRNAs. No downregulated miRNAs were observed in leaf samples from the pre-veraison stage. Pre-veraison berries had two down-regulated miRNAs, miR166be and miR6478a, while post-veraison berries and post-veraison leaves had seven each. Isoforms of miR156, miR395, and miR3624 were exclusively down-regulated in post-veraison berries. The miR396 family had down-regulated members in all post-veraison samples. miR166 had members that were down-regulated in all categories except in pre-veraison leaves. Additionally, isoforms of miR159 and miR319 were only downregulated in post-veraison leaves.\u003c/p\u003e \u003cp\u003eUpregulation of some miRNAs occurred in samples from both pre- and post-veraison (Fig.\u0026nbsp;4). Eleven miRNAs were upregulated in infected plants relative to healthy plants exclusively in the pre-veraison berry samples. This includes miR3624a-3p, which was down-regulated in post-veraison berries. Eight miRNAs were differentially expressed in post-veraison berries only. One isoform, miR482aw, was upregulated in both pre-and post-veraison berries. The previously mentioned miR396j was upregulated in both pre- and post-veraison leaves. Aside from miR482aw and miR396j, pre-veraison leaves showed six upregulated miRNAs, belonging to miR166, miR395, and miR3630, while post-veraison leaves showed upregulation of three miRNAs, namely miR166l, miR167an, and miR2950-3c. Members of miR166 and miR396 were upregulated in all four categories of samples in GRBV-positive vines relative to GRBV-negative vines. Members of miR395 were upregulated in all pre-veraison samples. miRNAs belonging to miR3630 and miR167 families were exclusively upregulated in leaf samples taken at pre- and post-veraison, respectively. Additionally, members of miR156, miRR165, miR3624, and miR3633 were exclusively upregulated in pre-veraison berry samples, while members of miR159, miR162, miR3476, miR3637, and miR7505 were exclusively upregulated in post-veraison berries (Fig.\u0026nbsp;4). The fold change, p-values, and FDR values for differential expression of miRNAs are shown in Table S8.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 4\u003c/strong\u003e \u003cp\u003eDifferential expression of miRNAs in GRBV-negative vs. GRBV-positive vines\u003c/p\u003e \u003c/p\u003e \u003cp\u003eNumber of miRNAs differentially expressed in response to infection by GRBV in leaves and berries, either in pre-veraison, post-veraison, or both (venn diagram). The number, direction, and identity of differentially expressed miRNAs in response to infection by GRBV, either in pre-veraison, post-veraison, or both (bar chart). miRNAs in red were downregulated in GRBV-positive relative to GRBV-negative samples, and miRNAs in green were up-regulated GRBV-positive relative to GRBV-negative samples. miRNAs in grey were differentially expressed in different directions in pre- compared to post-veraison samples. Both miR166ax and miR3624a-3p were up-regulated during pre-veraison and down-regulated during post-veraison.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in miRNA Expression Between Pre- and Post-Veraison Leaves and Berries\u003c/h2\u003e \u003cp\u003eWhen comparing the miRNA expression patterns in leaf and berry samples collected from pre-veraison relative to samples collected post-veraison, there were 50 differentially expressed miRNAs across twenty different miRNA families (Table S9). Of the 50 differentially expressed miRNAs, nine miRNAs belonging to six different families were only differentially expressed in the leaves. Of these six families, miR398 and miR408 only had differentially expressed members in leaf samples. Six miRNAs were differentially expressed in both leaves and berries, while the remaining 35 miRNAs were exclusively differentially expressed in the berries. Additionally, six miRNAs belonging to the families miR156, miR162, miR165, miR396, and miR6478, were differentially expressed only in GRBV-positive samples. Eight miRNAs were exclusively differentially expressed in GRBV-negative samples (Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eDespite the focus of this study being the interactions between GRBV infection and the \u003cem\u003eV. vinifera\u003c/em\u003e cv. \u0026lsquo;Merlot\u0026rsquo; miRNA profile, this post- minus pre-veraison DE analysis was performed to provide a more comprehensive picture of the normal shift in the grapevine miRNA profile across the growing season. This additional analysis allowed for the identification miRNAs which were differentially expressed in response to veraison in either GRBV-negative or GRBV-positive samples (not both) which did not exhibit significant differential expression with regard to infection status (miR156ao, miR159ak, miR166ag,ai,aj,h, miR168b, miR319ag, miR396h, miR398b, miR408ab, miR3627e-3p, and miR3633a). Despite the lack of differential expression in the viral status comparison, the presence of differential expression in healthy samples, paired with its lack in diseased samples, or vice versa, is still a meaningful finding.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 5\u003c/strong\u003e \u003cp\u003eDifferential Expression of miRNAs during pre-veraison vs. post-veraison\u003c/p\u003e \u003c/p\u003e \u003cp\u003eNumber of miRNAs differentially expressed in post-veraison relative to pre-veraison in leaves and berries, either in GRBV-negaitve plants, GRBV-positive plants, or both (venn diagram). The number, direction, and identity of miRNAs differentially expressed in post-veraison relative to pre-veraison either in GRBV-negative plants, GRBV-positive plants, or both (bar chart). miRNAs in red were downregulated post- relative to pre-veraison, and miRNAs in green were up-regulated post- relative to pre-veraison. miRNAs in grey were differentially expressed in different directions in GRBV-negative compared to GRBV-positive samples. Both miR395o and miR3624a-3p were upregulated in GRBV-negative berries and downregulated in GRBV-positive berries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in abundance of vsiRNAs specific to GRBV ORFs\u003c/h2\u003e \u003cp\u003eTo determine if the GRBV-derived sRNAs were particularly associated with specific regions of the GRBV genome, sRNA reads were mapped to the NCBI gene-annotated version of the GRBV genome (accession NC_022002.1). To gauge whether tissue type and phenological stage had an impact on the relative abundance of sRNAs mapping to each ORF, they were analyzed using the differential expression pipeline described above, utilizing the \u0026ldquo;EdgeR\u0026rdquo; package by Bioconductor in the R Statistical Coding Language [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Out of 1,360,633 reads that mapped to the GRBV genome, 1,302,547 sRNA reads (\u0026gt;\u0026thinsp;95%) mapped to known ORFs. The number of reads that mapped to individual ORFs in each sample are shown in Table S6. The highest number of vsiRNAs mapped to the V3 ORF, which had almost double the normalized reads (TPM) when compared to the other ORFs. The high abundance of V3-specific vsiRNAs was consistent in leaves and berries from both pre- and post-veraison.\u003c/p\u003e \u003cp\u003eFour different ORFs exhibited differential sRNA abundance between pre- and post-veraison in berry samples, while only a single ORF exhibited differential expression between pre-and post-veraison in leaf samples. In berries, sRNAs that mapped to C1, C2, and C3 ORFs were more abundant in post-veraison relative to pre-veraison, while the sRNAs specific to the V1 ORF were less abundant in post-veraison relative to pre-veraison. In leaves, sRNAs specific to the C2 ORF were significantly more abundant in post-veraison relative to pre-veraison (Fig.\u0026nbsp;6).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 6\u003c/strong\u003e \u003cp\u003eAbundance of vsiRNAs specific to GRBV ORFs\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAbundance (TPM) of sRNAs which mapped to specific GRBV ORFs. Asterisks are indicative of a significant difference between pre- and post-veraison within a tissue type. In berries, sRNAs mapping to V1 were significantly less abundant and sRNAs mapping to C1-3 were significantly more abundant. In leaves, sRNAs mapping to C2 were significantly more abundant in post- relative to pre- veraison. In V2 and C3, the general trend in leaves lined up with what was observed in berries but was not significant. A linearized depiction of the circular GRBV genome (5\u0026rsquo; to 3\u0026rsquo;) is displayed above to illustrate ORF locations within the GRBV genome. This depiction was created using SnapGene software [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and the RefSeq GRBV annotation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Sampling categories are denoted according to the following abbreviations: pre-veraison [P], post-veraison [PO], leaves [L], berries [B], GRBV-positive [D].\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGRBV is a monopartite geminivirus and is one of only four DNA viruses known to infect grapevines [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The biogenesis and action mechanisms of RNA silencing in DNA viruses are more complex and less well understood compared with RNA viruses. The size of vsiRNAs can provide context related to their biogenesis and putative functional roles. Regarding their biogenesis, the length of a given vsiRNA is dependent upon the DCL protein that processed its precursor. The DCLs responsible for vsiRNAs of different lengths are known: DCL2 for 22nt, DCL3 for 24nt, and DCL4 for 21nt [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The DCLs involved in the biogenesis of 20nt and 23nt vsiRNAs are not confirmed. Available evidence suggests that 20nt vsiRNAs are primarily generated by DCL2 but can also be generated by DCL 4, and that 23nt vsiRNAs are generated by both DCL2 and DCL3 [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. With regard to mode of action, there is a general trend throughout sRNA-mediated silencing towards long sRNAs (~\u0026thinsp;24nt) being likely to act via chromatin deposition (DNA methylation), and smaller sRNAs (20-22nt) tending to act via RNA degradation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Indeed, the production of 24nt siRNAs by DCL3 has been directly tied to the RNA-directed DNA methylation (RdDM) pathway [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the GRBV-derived siRNAs, 21nt reads were the most common in all sampling categories, which is consistent with previous observations from other viral pathosystems, including Grapevine leafroll-associated virus 3 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This also suggests that DCL4 could be the primary dicer involved in the generation of GRBV-derived vsiRNAs. Additionally, the observation that 20nt and 22nt vsiRNAs were significantly more abundant during post-veraison compared to pre-veraison, alongside a decrease in the abundance of 21nt vsiRNAs, could be indicative of a shift in DCL-2 activity across the growing season.\u003c/p\u003e \u003cp\u003eThe greater abundance of 21nt and 22nt vsiRNAs, relative to the low abundant 24nt vsiRNAs, may suggest that the majority of antiviral silencing targets GRBV transcripts and that the action of the RdDM pathway does not play a major role in silencing GRBV genomic DNA. This contrasts with observations made in geminivirus-infected \u003cem\u003eArabidopsis\u003c/em\u003e, which have demonstrated the importance of RdDM in antiviral defense [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Our findings may suggest that the RdDM pathway is not always the primary defense strategy against DNA viruses in plants, as has been previously suggested [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding the length distributions of siRNAs derived from GYSVd-1 and HSVd, the observed predomination of 21nt and 24nt vdsiRNAs supports what has previously been observed from these two viroids infecting \u003cem\u003eV. vinifera\u003c/em\u003e cv. Merlot [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In contrast to observations with GRBV vsiRNAs, relatively high levels of 24nt vdsiRNAs from both viroids suggest a higher level of interaction with the RdDM pathway. While these viroids do not have genomic DNA that could be methylated by this pathway, these 24nt vdsiRNAs may be involved in the methylation of host DNA. RdDM of host DNA has been observed in viroid-infected plants, including those infected with HSVd [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], but the exact mechanisms involved are unknown. It has been suggested that 24nt vdsiRNAs are involved in the formation of longer dsRNAs which trigger RdDM, as opposed to triggering RdDM directly. This would explain observations that complementarity between vdsiRNAs and target host DNA is not necessary for viroid-induced RdDM to occur [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe abundances of GRBV transcripts in leaf and berry samples at pre- and post-veraison were analyzed to examine the correlation between viral transcript levels and observed changes in the vsiRNA profile of two phenological stages. However, we did not find any significant differences in GRBV transcript levels between pre- and post-veraison samples, indicating that significant differences in the vsiRNA profile between the two phenological stages cannot be attributed to a change in the transcription of GRBV RNAs.\u003c/p\u003e \u003cp\u003eThe abundances of viroidal RNA, on the other hand, were analyzed primarily to determine if there was any evidence of synergism between GRBV and the two-viroids. Viruses and viroids are known to participate in such synergistic interactions, but the majority of research has focused on virus-virus and viroid-viroid interactions [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Other studies have investigated the possibility of virus-viroid synergisms [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. To our knowledge, only two such interactions have been described to date, one of which was between GYSVd-1 and Grapevine fanleaf virus (GFLV), a nepovirus responsible for fanleaf degeneration in grapevines [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Similar to what was observed between GYSVd-1 and GFLV, we observed an increase in the titer of HSVd RNAs in plants co-infected with GRBV, making this study yet another example of a virus-viroid synergistic interaction.\u003c/p\u003e \u003cp\u003eIn recent research, there have been multiple cases in which vsiRNAs have been shown to target host mRNA transcripts, leading to symptom expression [81, and cited references]. In this study, we identified eight distinct vsiRNAs with a total of 14 targets in the grapevine transcriptome. Of the fourteen putative target transcripts in the grapevine transcriptome, five possess functional domains directly tied to chloroplast function. An additional sixth target, containing a pentatricopeptide repeat (PPR), may also be connected to chloroplast function, as PPR proteins are known to be involved in the posttranscriptional regulation (RNA maturation, editing, intron splicing) of chloroplastic and mitochondrial genes [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. In white grape cultivars, chlorosis is the major visual symptom, rather than the characteristic red blotches. Additionally, GRBV has been demonstrated to lower photosynthetic rates in infected vines [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In other viral systems, including Rice stripe virus and Southern rice black-streaked dwarf virus, vsiRNAs targeting host genes encoding chloroplastic proteins have been implicated as a mechanism leading to the development of chlorotic symptoms [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. While more work needs to be done to make any definitive claims, it is possible that grbvasRNA2 and grbvasRNA7a are at least in part responsible for the lowered rate of photosynthesis in GRBV-infected grapevines, and the development of chlorosis in virus-infected, white-fruited cultivars.\u003c/p\u003e \u003cp\u003eRegarding the identification of the most abundant miRNAs and miRNA families, one that attracted our attention was the highly abundant miR3634a-3p, which was expressed at high levels in all samples (Figure S5) but were notably more abundant in pre-veraison leaf samples, regardless of infection by GRBV (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). miR3634-3p miRNAs have previously been observed to be among the most abundant miRNAs in grapevine [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], though not to the extent observed here. This study also found that miR3634a-3p targets the transcript Vitvi19g04623_t001 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which contains 3 copies of an ankyrin repeat domain from the Ank 2 superfamily. Ankyrin repeat domains are involved in a wide array of different protein functions, including transport, cell-cell signaling, and various regulatory processes, although none have been observed to have any enzymatic function [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Previously, an miR3634 isoform in grapevine was found to target a transcript for an E3 ubiquitin ligase [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. The discovery of an additional target of a completely unrelated nature suggests that the miR3634 family may regulate a variety of processes, which could be a contributing factor to its high abundance.\u003c/p\u003e \u003cp\u003eThe miR159, miR166, miR395, miR396, and miR3623 families were also highly expressed across all samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which is supported by previous studies of grapevine miRNAs [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. The miR166 family target identification (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) supports its previously established role in regulating the expression of a class III Homeodomain leucine-zipper protein tied to wood formation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Previous studies have shown miR166 displaying upregulation in response to viral infection [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Here, miR166 family members displayed a mixture of responses to infection with GRBV. One in particular (miR166ax) even displayed a directional shift in its response to GRBV infection between pre- and post-veraison (Table S8).\u003c/p\u003e \u003cp\u003eOne miRNA which responded the same way to GRBV infection during both pre- and post-veraison was miR396j, which was upregulated in leaves (Table\u0026nbsp;2.3a). Overall, differential expression of miR396 family members in response to GRBV infection was highly varied, but predominantly followed the pattern of up-regulation in the berries and down-regulation in the leaves, making miR396j somewhat of an outlier relative to related miRNAs. This varied response of miR396 expression is supported by other studies, which have observed both up- and down-regulation of miR396 in response to various biotic and abiotic stresses [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is widely known that miRNAs from the miR396 family target growth regulating factors (GRFs), which are involved in many developmental processes, ranging from general cell proliferation to flower, fruit, and seed formation [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. The degradome analysis in this study validated two cleavage events for GRF transcripts, specifically those for GRF4 and GRF8 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These two GRF proteins are primarily involved in cell proliferation of leaf tissue [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. miR396j\u0026rsquo;s consistent response to GRBV infection in leaf tissue could indicate that miR396j is specifically involved with these two GRF factors, leading to reduced leaf growth in GRBV-infected plants, which could then contribute to the observed overall reduction in plant vigor [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also identified novel targets for the miR396 family, including the transcription factor Big Petal P (BPEp), Cyclin-Dependent Kinase (CDK) subunit 2, and Scarecrow-like protein 3 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), all of which are involved in one or more aspects of plant development/growth [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Scarecrow-like protein 3, specifically, promotes gibberellin signaling, which in turn induces an array of different plant growth and development processes [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. The up-regulation of different miR396-family miRNAs in both pre- and post-veraison berries in response to GRBV infection warrants further investigation, as it could be acting to lower the activity of the gibberellin signaling pathway, which may be a contributing factor to the reduction of fruit yield and slower fruit maturation.\u003c/p\u003e \u003cp\u003eThe miR156 family is also known to regulate proteins involved in plant development, specifically the SQUAMOSA promoter-binding protein-like (SPL) transcription factors, which have been shown to play roles in both fruit ripening and stress response [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. In this study, miRNAs belonging to the miR156 family exhibited up-regulation in response to GRBV infection in berries during pre-veraison, and in one case, down-regulation in post-veraison berries (Table S8). Prior studies investigating the grapevine sRNA profile have detected up-regulation [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e] and down-regulation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e] in response to a variety of other pathogens and stresses. Our study detected an inverse response of the miR156 family as berries mature, and this regulation may contribute to the impeded berry development observed in GRBV-infected vines.\u003c/p\u003e \u003cp\u003eAlso tied to plant development, miR160, miR167, miR393, and tasiRNA3 were all found to target auxin response genes. Several isoforms of the miR167 family were significantly differentially expressed in response to GRBV infection, with miR167an being upregulated during post-veraison (Table S8). miR167 is known to target auxin response factors 6 and 8 (ARF6 and ARF8) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. ARF6 is a positive regulator of photosynthetic processes, sugar accumulation, and fruit ripening [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e], and both ARFs are positive regulators of jasmonic acid biosynthesis [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. Increased expression of miR167 has been shown to lead to defective development of flowers in tomato [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. However, in our analysis, we only detected significantly increased expression of miR167an at post-veraison, long after flower formation. Based on the processes that ARF6 and ARF8 are involved in, this up-regulation could lead to an overall reduction in photosynthetic activity, fruit ripening, and jasmonic acid synthesis. Decreases in photosynthetic levels and fruit ripening are known symptoms of GRBV [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and down-regulation of jasmonic acid has been observed in grapevines infected with Grapevine fabavirus [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e], so the increase in miR167 in GRBV-infected plants could play a role in symptom development. Contrastingly, silencing of ARF6 by miR167 would lead to an overall reduction in sugar accumulation, and yet sugar accumulation in leaves is known to increase due to GRBV infection. It is currently believed that GRBV interferes with the transport of sugars from leaf to berry [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which may explain the accumulation of sugar despite increased miR167 expression.\u003c/p\u003e \u003cp\u003eThe miR3623 family was found to target a protein homologous to the \u003cem\u003eA. thaliana\u003c/em\u003e Xylulose kinase-2 protein (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which is integral to the isoprenoid biosynthesis pathway [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]. The miR3623 family has also been linked to the regulation of disease resistance genes involved in the regulation of phasiRNA production and has been suggested to be related to the miR482 family, which is also known to target disease resistance proteins [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. It has also been found to exhibit up-regulation in grapes infected with the phytoplasma disease, Flavescence dor\u0026eacute;e [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Our findings show that miR3623-family miRNAs were not differentially expressed in response to GRBV-infection, while miR482aw was upregulated in GRBV-infected berries, which could suggest that, in this plant-pathogen system, the expression of miR3623 is tied more closely to the regulation of isoprenoid biosynthesis and that the regulation of disease resistance proteins may be a secondary function.\u003c/p\u003e \u003cp\u003eInterestingly, miR482aw was only one of two miRNAs, the other being miR396j, which responded the same way to GRBV infection regardless of veraison stage, being upregulated in the berries at both timepoints (Table S8). The fact that miRNAs in the miR482 family negatively regulate disease resistance proteins [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e] makes the increased expression of these miRNAs in response to disease appear counterintuitive, though this same pattern has also been observed in other plant-pathogen systems [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. It has also been suggested that members of the miR482 family serve as an evolutionary strategy, reducing the fitness costs of inefficient or non-functioning R genes, protecting against their misexpression, and allowing more freedom for genetic variation [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]. This would explain the upregulation of miR482aw in diseased berries, as it may be suppressing resistance genes that are being expressed because of GRBV infection but do not offer any functional resistance to it.\u003c/p\u003e \u003cp\u003eThe miR395 family is known to inhibit both ATP sulfurylases and sulfate transporter 2;1. The inhibition of these proteins leads to the accumulation of sulfate in plant tissue [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. Sulfate and sulfate-derived compounds are important contributors to stress tolerance in plants and play a role in plant defense strategies against pathogens [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. In grapevine, miR395 has previously been observed to exhibit increased expression in vines infected with Grapevine leafroll-associated virus 3, suggesting it may play a role in a defense strategy against viral disease [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. We observed that miR395-family miRNAs were only up-regulated in response to GRBV infection during pre-veraison, and that some were even down-regulated during post-veraison.\u003c/p\u003e \u003cp\u003eThe miRNA family miR159 is known to target the MYB33 and MYB65 genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e], and is closely related to the miR319 family. miR319 is generally recognized as silencing TCP genes. While both miRNA families have been found to target both MYB and TCP genes, their sequence differences have been shown to make them more effective at silencing their primary targets [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]. Interestingly, the expression levels of miRNAs from both miR159 and miR319 families showed down-regulation in response to GRBV infection in post-veraison leaf samples. This appears to be a somewhat unique interaction, as previous studies have observed both groups exhibiting increased expression in response to viral infection in other plant systems [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMYB33 and MYB65 are associated with several aspects of plant growth and development [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]. TCP-4 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is tied to the regulation of leaf morphogenesis and phytohormone levels [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. Interestingly, MYB33 and MYB65 have also been associated with drought tolerance responses and their repression has been shown to increase the impact of drought on \u003cem\u003eArabidopsis\u003c/em\u003e [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]. GRBV infected vines have been shown to have increased symptom severity when receiving limited water, which is problematic due to the frequent use of deficit irrigation to increase fruit quality in vineyards [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e]. In their study, Levin and KC only witnessed adverse effects of water limitation in GRBV-infected vines during post-veraison. This supports the possibility that the downregulation of miR159 post-veraison witnessed in this study could be an effort by the plant to mitigate drought stress by increasing MYB33 and MYB65 levels.\u003c/p\u003e \u003cp\u003eAdditionally, some MYBs, such as VvMYB114, have been shown to regulate anthocyanin accumulation in grapevines [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e]. VvMYB114 is known to be regulated by miR828 and miR858. These miRNAs also target MYB4, MYB5, MYB7, MYB12, MYB23, MYB59, and MYB66 family proteins (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), but were not found to be significantly differentially expressed in this study. The lack of differential expression, particularly between pre- and post-veraison stages, was unexpected due to the important shifts that occur in anthocyanin production during veraison. However, these miRNAs were not detected in all samples and possessed very small read counts, which could have contributed to the lack of statistical significance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, the results of this study lay a foundation for future research into the mechanisms of the interactions between \u003cem\u003eVitis vinifera\u003c/em\u003e and GRBV. The length distribution of GRBV-derived vsiRNAs suggests that the primary action of vsiRNAs is through mRNA silencing and not RNA-dependent DNA methylation, contrary to observations in other plant-DNA virus pathosystems. Future research into the actual levels of RdDM in GRBV-infected vines could provide insight into a potential method of increasing grapevine resistance to the virus.\u003c/p\u003e \u003cp\u003eThe observed synergism between GRBV and HSVd warrants further investigation, as it is unclear if the synergistic relationship is mutual, or if coinfection impacts host gene expression differently relative to what would be observed in single infections with HSVd or GRBV.\u003c/p\u003e \u003cp\u003eMore work is needed with regard to the biogenesis of vsiRNAs derived from DNA viruses, as well. The observed overabundance of vsiRNAs originating from the V3 ORF indicates that this particular ORF in GRBV may play an important role in this process, which requires further study.\u003c/p\u003e \u003cp\u003eAdditionally, the results of this study align with the ever-increasing body of evidence supporting the idea that viral infection can modulate plant miRNAs [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e, \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]. Transcripts involved in various areas of grapevine development, metabolism, and defense are targeted by many of the miRNAs we identified to be differentially expressed in response to infection by GRBV. This study identified 41 miRNAs which were differentially expressed in response to GRBV infection (Table S8), in addition to 50 miRNAs that were differentially expressed in response to veraison (Table S9). We also found 58 targets of conserved miRNAs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.4), as well as 40 novel targets of grapevine miRNAs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), all supported by cleavage events in the grapevine degradome. It is possible that some of the identified novel targets are a result of the use of grapevine specific sequences and may not be valid targets in other plant systems. Nevertheless, the differentially expressed miRNAs and their targets identified from this study in own-rooted \u003cem\u003eV. vinifera\u003c/em\u003e cv. Merlot vines infected with GRBV can be helpful towards an improved understanding of virus-host interactions.\u003c/p\u003e \u003cp\u003eThis research also provides further support for the recent discovery that vsiRNAs can also target host transcripts. While the cleavage events were supported by evidence in the degradome, additional validation needs to be done to verify the occurrence of these interactions. Despite this, however, the identified targets do align with what has been observed in other viral systems and represent an interesting potential mechanism for symptom development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.A., R.S., and R.N. conceived and designed the research project. N.A., D.P., Y.L, and A.S. performed experiments, analyzed, interpreted, and visualized data. S.R. and Y.Z. processed the initial data. N.A. and R.N. wrote the manuscript. S.R., Y.Z., and R.S. edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Drs. Prashant Swamy and Sridhar Jarugula for helping with sample collection, extraction of RNA, and shipping for library preparation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw sRNA and degradome sequencing data are available under the BioProject accession PRJNA1129059, or at the following link: https://www.ncbi.nlm.nih.gov/sra/PRJNA1129059. The associated SRA accession numbers for the sequencing files are SRR29616259-86.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAxtell MJ. Classification and Comparison of Small RNAs from Plants. Annu Rev Plant Biol. 2013;64:137\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong X, Li Y, Cao X, Qi Y. 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Dev Cell. 2003;4(2):205\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Grapevine, Vitis vinifera, Grapevine red blotch virus, Geminiviridae, Viroid, small RNA, microRNA, microRNA Target, High Throughput Sequencing","lastPublishedDoi":"10.21203/rs.3.rs-4803716/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4803716/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRed blotch disease, caused by Grapevine red blotch virus (GRBV, genus \u003cem\u003eGrablovirus\u003c/em\u003e, family \u003cem\u003eGeminiviridae\u003c/em\u003e), negatively impacts vine health, fruit yield, and quality, leading to substantial economic losses to growers. While recent studies have enhanced our understanding of the epidemiology of GRBV and its effects, little is known about the molecular basis of the host-virus interactions. Since small RNAs (sRNAs) are known to play a central role in host-virus interactions, this study was undertaken to investigate sRNA dynamics in leaves and berries at two phenological stages (asymptomatic pre- and symptomatic post-veraison) of GRBV-infected grapevines (\u003cem\u003eVitis vinifera\u003c/em\u003e cv. Merlot).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 140 microRNAs (miRNAs) detected, 41 isoforms belonging to 18 miRNA families exhibited significant differential expression in response to GRBV infection. Furthermore, 50 miRNAs showed differential expression in samples from pre- and post-veraison stages. A total of 58 conserved and 41 novel targets for known \u003cem\u003eV. vinifera\u003c/em\u003e miRNAs were validated using degradome sequencing data from leaf samples of pre- and post-veraison stages. Viroid-derived small-interfering RNAs (vdsiRNAs) specific to Grapevine yellow-speckle viroid-1 and Hop stunt viroid were also identified in all samples, while virus-derived siRNAs (vsiRNAs) specific to GRBV were present only in GRBV-positive samples. The vsiRNAs predominantly ranged from 19 to 24 nucleotides (nt), with the 21nt size being the most abundant. Mapping vsiRNAs across the GRBV genome revealed an uneven distribution, with vsiRNA-generating hotspots predominantly located in the V3 ORF. Of the 83 most abundant vsiRNAs, targets within the grapevine transcriptome were identified for eight of them. Significantly higher levels of HSVd RNAs were observed in GRBV-positive samples compared to GRBV-negative samples, suggesting a potential synergistic interaction between the two pathogens.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe predominance of 21-nt long vsiRNAs, as well as the predominance of those mapping to the V3 ORF compared to other ORFs, provide insight into both the biogenesis and methods of action of GRBV vsiRNAs. Target validations of vsiRNAs and differentially expressed miRNAs are indicative of pathways and mechanisms which may lead to the expression of Grapevine red blotch disease symptoms. This research serves as a foundation for future studies on the molecular interactions in this plant-geminivirus pathosystem.\u003c/p\u003e","manuscriptTitle":"Dynamics of small RNAs in a red-fruited wine grape cultivar infected with Grapevine red blotch virus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-23 07:03:21","doi":"10.21203/rs.3.rs-4803716/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-08T19:32:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-23T19:50:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249436778406157899387688381375209927594","date":"2024-10-11T12:49:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-12T10:04:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240746141260074498136981395259564696395","date":"2024-08-30T09:54:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-01T12:58:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-30T12:45:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-29T02:32:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-29T02:32:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-07-25T18:18:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"238e49a9-3cec-4d5a-817d-76b30d502ecb","owner":[],"postedDate":"August 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T16:03:07+00:00","versionOfRecord":{"articleIdentity":"rs-4803716","link":"https://doi.org/10.1186/s12864-025-11539-4","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-04-29 15:57:25","publishedOnDateReadable":"April 29th, 2025"},"versionCreatedAt":"2024-08-23 07:03:21","video":"","vorDoi":"10.1186/s12864-025-11539-4","vorDoiUrl":"https://doi.org/10.1186/s12864-025-11539-4","workflowStages":[]},"version":"v1","identity":"rs-4803716","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4803716","identity":"rs-4803716","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}