Abstract

   Rice is a crucial staple food for over half the global population, and
   viral infections pose significant threats to rice yields. This study
   focuses on the Rice Stripe Virus (RSV), which is known to drastically
   reduce rice productivity. We employed RNA-seq and ribosome profiling to
   analyze the transcriptional and translational responses of RSV-infected
   rice seedlings. Our results reveal that translational reprogramming is
   a critical aspect of the plant’s defense mechanism, operating
   independently of transcriptional changes. Notably, less than half of
   the differentially expressed genes showed concordance between
   transcription and translation. Furthermore, RSV infection led to
   significant alterations in translational efficiency for numerous genes,
   suggesting that the virus selectively manipulates translation to
   enhance its pathogenicity. Our findings underscore the necessity of
   examining both transcriptional and translational landscapes to fully
   understand plant responses to viral infections.

   Keywords: ribosome profiling, RNA-seq, rice stripe virus, translational
   efficiency, uORFs

1. Introduction

   Rice (Oryza sativa L.) is a staple food crop in many Asian countries,
   including China, India, Indonesia, Japan, Korea, and the Philippines,
   with approximately 50% of the global population relying on it for
   sustenance. The demand for rice continues to rise significantly across
   various regions [[34]1,[35]2]. However, rice viruses pose a serious
   threat to crop yields, leading to symptoms such as dwarfism, altered
   tillering, wilting, and yellowing, all of which adversely affect
   production [[36]3]. One particularly destructive virus is the Rice
   Stripe Virus (RSV), a member of the Tenuivirus genus, known for its
   severe impact on rice yields [[37]4]. RSV can cause curling and
   discontinuous yellow stripes on leaves and even give rise to enormous
   losses in field production, especially in East Asia [[38]5,[39]6]. RSV
   is transmitted by the small brown planthopper (SBPH) in a persistent
   circulative–propagative manner [[40]7]. Infected rice plants typically
   exhibit chlorosis, weakness, and necrosis in new leaves, resulting in
   stunted growth and substantial yield losses [[41]8,[42]9,[43]10]. Thus,
   a thorough investigation into the mechanisms of RSV infection is
   crucial for developing effective antiviral strategies and enhancing
   disease resistance through targeted engineering [[44]11].

   Recent advancements in high-throughput methodologies, especially RNA
   sequencing (RNA-seq), have revolutionized gene expression studies
   [[45]12]. Several developments in RNA-seq methods have provided an even
   more complete characterization of RNA transcripts and solved many
   problems in plants [[46]9,[47]13,[48]14]. However, mRNA levels do not
   always accurately reflect protein production, as gene expression is
   regulated at both transcriptional and translational levels through
   mechanisms such as microRNAs and ribosome stalling
   [[49]15,[50]16,[51]17]. Consequently, there is a critical need for
   techniques that directly monitor protein synthesis
   [[52]18,[53]19,[54]20]. A novel method known as ribosome profiling has
   recently been developed, allowing for single-nucleotide-resolution
   measurement of protein synthesis by deep sequencing ribosome-protected
   mRNA fragments (RPFs) [[55]18,[56]21,[57]22]. As a significant
   methodological advancement, ribosome profiling has rapidly been applied
   to understand the translational landscape in various species
   [[58]18,[59]23,[60]24]. In plants, this technique has been employed to
   analyze responses to drought and salt stress in rice [[61]25,[62]26].
   Evidence increasingly suggests that transcriptional and translational
   responses to stress are relatively independent processes
   [[63]27,[64]28,[65]29,[66]30]. However, there is still limited research
   applying ribosome profiling to investigate translation regulation
   following viral infection.

   In this study, we conducted RNA-seq and ribosome profiling on healthy
   and RSV-infected seedlings to comprehensively understand molecular
   responses to viral infection. Our analysis revealed significant
   differences in transcriptomic and translatomic profiles between the two
   groups, suggesting that translational reprogramming constitutes a
   distinct layer of the plant’s response to RSV infection that is
   independent of transcriptional changes. Fewer than half of the
   differentially expressed genes (DEGs) were common to both transcription
   and translation, with many genes exhibiting altered translational
   efficiencies post-infection. This indicates that the virus may
   selectively modulate the translation of specific mRNAs to enhance its
   pathogenicity. Overall, our study advances understanding of the
   molecular basis of RSV infection and emphasizes the importance of
   examining both transcriptional and translational responses to fully
   appreciate the plant’s adaptive mechanisms against viral threats.
   Future investigations could focus on identifying regulatory elements
   governing these translational changes and exploring their potential
   applications in improving disease resistance in rice and other crops.

2. Result

2.1. Overview of RNA-seq and Ribo-Profiling

   To systematically characterize the transcriptional and translational
   responses during virus infection at a genome-wide scale, we conducted
   RNA-seq and ribosome profiling on both healthy and RSV-infected rice
   seedlings, each with three biological replicates. Compared to healthy
   plants, samples infected with RSV exhibit obvious symptoms and higher
   levels of viral RNA ([67]Figure S1A). On average, we generated
   approximately 11 million ribosome profiling reads for healthy seedlings
   and 25 million for those infected with RSV ([68]Figure 1A). During the
   translation process, ribosomes move relative to RNA in units of codon
   length (3 nt) [[69]31,[70]32,[71]33,[72]34]. And we observed a strong
   three-nucleotide periodicity when scanning after the start codon and
   before the stop codon in both healthy and RSV-infected seedlings
   ([73]Figures S2A and S3A). After initial processing to filter out rRNA,
   low-quality, and tRNA reads, about 30% to 60% of the total reads mapped
   to the Oryza sativa Japonica Group reference genome (RefSeq:
   GCF_034140825.1) ([74]Figure 1A). To investigate the impact of virus
   infection on ribosome profiling characteristics, we compared data
   between healthy and RSV-infected seedlings. We observed that the length
   of ribosome footprints (RPFs) in healthy samples is around 28 nt, with
   a distribution of 25–28 nt and a peak at 28 nt in RSV-infected samples
   ([75]Figure 1B,C). In healthy seedlings, 0.79% and 2.3% of RPFs were
   found in 5′ UTRs and 3′ UTRs, respectively, while 96.3% were located in
   coding sequences (CDSs) and 0.52% in introns ([76]Figure 2D). In
   RSV-infected seedlings, 0.4% and 23.7% of RPFs were found in 5′ UTRs
   and 3′ UTRs, respectively, with 67.3% in CDSs and 4.8% in introns
   ([77]Figure 2E). These changes imply that translation in UTRs may
   mediate the plant response to RSV infection.

Figure 1.

   [78]Figure 1
   [79]Open in a new tab

   The characteristics of ribosome profiling data for healthy and
   RSV-infected rice seedlings. (A) Alignment statistics for ribosome
   profiling reads. Mapping efficiency is defined as the total number of
   mapped reads divided by the total number of reads. Unique mapping
   efficiency represents the proportion of unique reads among the total
   mapped reads. (B) Length distribution of ribosome protected fragments
   (RPFs) in healthy seedlings. The grey, black, and white bars correspond
   to three biological replicates. (C) Length distribution of ribosome
   protected fragments (RPFs) in RSV-infected rice seedlings. The grey,
   black, and white bars correspond to three biological replicates. (D)
   The percentages of RPF reads located in CDSs, 5′ UTRs, and 3′ UTRs in
   healthy seedlings. (E) The percentages of RPF reads located in CDSs, 5′
   UTRs, and 3′ UTRs in RSV-infected rice seedlings.

Figure 2.

   [80]Figure 2
   [81]Open in a new tab

   RSV infection-induced transcriptional and translational responses. (A)
   The number of differentially expressed genes (DEGs) at transcriptional
   levels. Genes with a fold change ≥ 2 and a q-value ≤ 0.05 are
   displayed. The horizontal axis shows the log2 fold changes, and the
   vertical axis represents the −log10 p-values. The blue dashed line
   indicates the threshold for differential gene screening. (B) The number
   of differentially expressed genes (DEGs) (fold change ≥ 2 and q-value ≤
   0.05) at translational levels. The representation is similar to that of
   panel A, with the same axis representations. (C) The relationship
   between upregulated genes at transcriptional and translational levels.
   (D) The relationship between downregulated genes at transcriptional and
   translational levels. (E) KEGG (Kyoto Encyclopedia of Genes and
   Genomes) pathway enrichment analysis at transcriptional levels, using a
   p-value < 0.05 as the threshold for significant enrichment. (F) KEGG
   (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis
   at translational levels, using a p-value < 0.05 as the threshold for
   significant enrichment. Gene ratios represents the ratios of DEGs
   annotated with KEGG pathways to the total number of DEGs, with a
   p-value < 0.05 as the threshold for significance.

2.2. Virus Infection Altered Expression at Both Transcriptional and
Translational Levels

   Using both RNA-seq and ribosome profiling data of healthy and
   RSV-infected plants, we were able to examine the effects of virus
   infection on gene expression at both transcriptional and translational
   levels simultaneously. Based on the criteria of an absolute fold change
   ≥ 2 and a p-value < 0.05, we obtained 2833 upregulated and 2258
   downregulated DEGs ([82]Figure 2A). Meanwhile, 6434 upregulated genes
   and 3011 downregulated genes were detected at the translational levels
   ([83]Figure 2B). The numbers of upregulated genes and downregulated
   genes at transcriptional levels were lower than those at translational
   levels, indicating that viral infection can cause a significant amount
   of gene regulation at translational levels. We found that only 1306
   DEGs overlapped in genes with upregulated transcription and translation
   levels ([84]Figure 2C) and that only 996 DEGs overlapped in genes with
   downregulated transcription and translation levels ([85]Figure 2D),
   accounting for 20% and 33% of all translational DEGs, respectively.
   Notably, DEGs at both levels were enriched in amino acid biosynthesis
   pathways, highlighting that viral infection triggers extensive protein
   synthesis and metabolic reactions ([86]Figure 2E,F). The differences in
   the number and pathways of DEGs at both levels emphasize that gene
   regulation occurs at both transcriptional and translational stages
   post-infection, rather than solely at the transcriptional level.

2.3. RSV Infection Introduced Significant Changes in the Translational
Efficiencies of a Large Number of Genes

   Translational efficiency (TE; calculated by RPKM in Ribo-Profiling/RPKM
   in RNA-seq) serves as a key indicator of RNA utilization efficiency
   [[87]18]. We assessed how rice seedlings respond to RSV infection
   through alterations in TE. A total of 3821 genes exhibited increased
   TE, while 2522 genes showed decreased TE under RSV infection compared
   to the healthy seedling controls ([88]Figure 3A). Overall, the
   downregulated TE genes were predominantly enriched in pathways related
   to amino acid biosynthesis and carbon metabolism ([89]Figure 3B). The
   upregulated TE genes were primarily associated with pathways involving
   spliceosome and ribosome biosynthesis ([90]Figure 3C). In order to
   analyze the relationship between transcription and translation levels,
   we calculated the Pearson correlation coefficients for the
   transcriptome and translation groups. Under RSV-infected conditions,
   the Pearson correlation coefficient between gene transcription and
   translation levels was 0.905 ([91]Figure 3D). And the Pearson
   correlation coefficient between gene transcription and translation
   levels in healthy seedlings was 0.865 ([92]Figure 3E), indicating a
   strong synergistic effect between transcription and translation under
   stress conditions. These findings suggest that rice plants may employ
   independent translational regulation in response to RSV infection.

Figure 3.

   [93]Figure 3
   [94]Open in a new tab

   RSV infection-induced transcriptional and translational responses. (A)
   The number of TEs (fold change ≥ 2 and q-value ≤ 0.05) at the
   transcriptional level. TEs with a fold change ≥ 2 and a q-value ≤ 0.05
   are displayed. The horizontal axis shows the log2 fold changes, and the
   vertical axis represents the −log10 p-values. (B) KEGG (Kyoto
   Encyclopedia of Genes and Genomes) pathway enrichment analysis at
   transcriptional levels, using a p-value < 0.05 as the threshold for
   significant enrichment. (C) KEGG (Kyoto Encyclopedia of Genes and
   Genomes) pathway enrichment analysis at transcriptional levels, using a
   p-value < 0.05 as the threshold for significant enrichment. (D) Pearson
   correlation coefficients between the ribo-profiling level and RNA-seq
   in RSV-infected seedlings. (E) Pearson correlation coefficients between
   the ribo-profiling level and RNA-seq in healthy seedlings.

2.4. RSV Infection Altered Gene Translation Efficiency

   To understand the effect of RSV infection on transcription and
   translation levels in more detail, the genes that changed markedly at
   the transcription or translation level were defined by the criteria of
   fold-change values ≥ 2 and false-discovery rates (FDRs) below 0.05. We
   subdivided all genes into five groups ([95]Figure 4). A homodirectional
   group (8.17%, 2301 genes) changed markedly at both the transcriptional
   and translational levels and showed consistent trends. An opposite
   group (0.83%, 233 genes) changed significantly at both the
   transcriptional and translational levels and showed an inconsistent
   trend. A transcription group (8.73%, 2457 genes) changed markedly only
   at the transcriptional level. A translation group (22.21%, 6254 genes)
   changed markedly only at the translational level. An unchanged group
   (60.05%, 16908 genes) did not change markedly at either the
   transcriptional or the transactional level. Except for the unchanged
   group, only 8.17% of genes exhibited the same trend at the
   transcription and translation levels, indicating that for some genes
   there is independence between transcriptional and translational
   regulation. And only a few genes showed the opposite trend; so, for the
   regulation of most genes, the translation level cannot override the
   transcription level.

Figure 4.

   [96]Figure 4
   [97]Open in a new tab

   Fold changes at the transcription and translation levels with RSV
   infection. Scatter plot of fold changes at the transcription and
   translation levels of RSV-infected vs. healthy seedlings. Genes were
   classified into five distinct groups based on their expression changes,
   defined by fold-change values ≥ 2 and false-discovery rates (FDRs)
   below 0.05.

2.5. Functional Comparison at the Transcription and Translation Levels

   We further performed a pathway enrichment analysis on the genes in the
   five groups. Genes in the homodirectional group were significantly
   enriched in carbon metabolism, biosynthesis of cofactors, and several
   pathways related to photosynthesis, such as photosynthesis—antenna
   proteins, photosynthesis, and circadian rhythm—plant ([98]Figure 5A).
   Few genes showed the opposite trend at transcription and translation
   levels. Genes in the opposite group were enriched in oxidative
   phosphorylation, purine metabolism, and nucleotide metabolism
   ([99]Figure 5B). Genes in the transcription group were significantly
   enriched in plant hormone signal transduction, biosynthesis of amino
   acids, and amino sugar and nucleotide sugar metabolism pathways
   ([100]Figure 5C). Amino acids are precursors for the synthesis of many
   metabolites and have multiple functions in the growth and other
   biological processes of organisms [[101]35]. Plant hormones play
   pivotal roles in the regulation of immune responses to microbial
   pathogens, insect herbivores, and beneficial microbes [[102]36], which
   are significantly regulated at the transcriptional level under viral
   infection. Genes in the unchanged group were enriched in protein
   processes, endocytosis, nucleocytoplasmic transport, and spliceosomes
   ([103]Figure 5D). Genes in the translation group were enriched in
   ribosomes, carbon metabolism, biosynthesis of amino acid pathways, and
   glycolysis/gluconeogenesis ([104]Figure 5E).

Figure 5.

   [105]Figure 5
   [106]Open in a new tab

   Top 20 KEGG pathways of DEGs at transcriptional and translational
   levels. (A) KEGG pathway analysis of genes in the homodirectional
   group. (B) KEGG pathway analysis of genes in the opposite group. (C)
   KEGG pathway analysis of genes in the transcription group. (D) KEGG
   pathway analysis of genes in the unchanged group. (E) KEGG pathway
   analysis of genes in the translation group.

2.6. Identification and Characteristics of Upstream Open Reading Frames
(uORFs)

   Upstream open reading frames (uORFs)—short coding sequences located 5′
   of the main coding sequence—are found in a minority of eukaryotic mRNAs
   and often play regulatory roles [[107]37]. These uORFs precede the
   translation start site for downstream main open reading frames (mORFs)
   [[108]38,[109]39]. Utilizing Ribo-seq data, we conducted a genome-wide
   identification of uORFs based on the ATG start codon located in the 5′
   untranslated region (UTR). We first performed a motif analysis to
   examine the frequency of nucleotides surrounding the uORF start codon
   in both translated and untranslated regions, as this codon is crucial
   for translation initiation. Our analysis revealed that the probability
   of cytosine at the −1 and −2 positions was higher in untranslated uORFs
   compared to translated uORFs in RSV-infected seedlings ([110]Figure 6A,
   [111]Supplementary Table S1). Additionally, in healthy seedlings, the
   probability of guanine at the −1 position was also higher in
   untranslated uORFs than in translated uORFs, with a greater overall
   frequency of guanine in the untranslated uORFs ([112]Figure 6C,
   [113]Supplementary Table S2). To compare the characteristics of
   translated and untranslated uORFs, we examined their lengths. We found
   that the lengths of translated uORFs were shorter than those of
   untranslated uORFs in healthy seedlings ([114]Figure 6D). However, this
   difference diminished under RSV infection ([115]Figure 6B). These
   findings indicate that the translation of uORFs in rice plants is
   regulated in response to RSV infection, suggesting a complex interplay
   between translation and viral response mechanisms.

Figure 6.

   [116]Figure 6
   [117]Open in a new tab

   Characteristics of uORFs in healthy seedlings and RSV-infected
   seedlings. (A) Sequence composition of translated (top) and
   untranslated uORFs (lower) around the ATG start codon in the
   RSV-infected seedlings. (B) Comparison of translated and untranslated
   uORFs by uORF length in the RSV-infected seedlings. (C) Sequence
   composition of translated (top) and untranslated uORFs (lower) around
   the ATG start codon in the healthy seedlings. (D) Comparison of
   translated and untranslated uORFs by uORF length in the healthy
   seedlings.

3. Discussion

   Rice Stripe Virus (RSV) has become a major pathogen of rice [[118]40].
   Over the past few decades, numerous studies have utilized RNA
   sequencing technology to generate high-throughput sequence data
   [[119]28,[120]41]. These findings indicate that the downregulation of
   genes associated with photosynthesis and flowering is closely linked to
   the disease symptoms induced by RSV [[121]42]. Early in the infection
   process, RSV triggers the expression of many host defense genes;
   however, in the later stages, the majority of these upregulated defense
   genes are suppressed [[122]41]. It is important to note that mRNA
   levels do not always accurately reflect the regulation of protein
   translation [[123]43,[124]44]. Ribosome profiling, which maps the
   positions of millions of translating ribosomes within the cell, allows
   for a genome-wide investigation of translation
   [[125]18,[126]19,[127]45]. We employed this technique to examine
   translation during RSV infection. By successfully obtaining both
   ribosome profiling and RNA-seq data, we were able to assess genome-wide
   gene expression changes at the translational level post-RSV infection
   and explore the interactions between transcriptional and translational
   responses.

   Overall, we observed a strong correlation between the fold changes in
   gene expression at both the transcriptional and translational levels,
   regardless of RSV infection ([128]Figure 3D,E). Notably, there were
   more differentially expressed genes at the translational level compared
   to the transcriptional level following RSV infection. There were 6434
   upregulated genes and 3011 downregulated genes at the translation
   level, while there were only 2833 upregulated genes and 2258
   downregulated genes at the transcription level, indicating that a large
   number of genes were intentionally regulated at the translation level
   ([129]Figure 2A,B). Ribosome profiling quantifies the genome-wide
   ribosome occupancy of transcripts. By integrating this data with
   matched RNA sequencing, we can calculate the TEs of genes to reveal
   translational regulation [[130]46,[131]47]. After RSV infection, we
   observed that 3821 genes were upregulated and 2522 were downregulated
   in terms of TE, highlighting the specific regulation of translation in
   plants. Additionally, the changes in translated and untranslated uORFs
   following RSV infection differed, suggesting that uORFs also play a
   role in plant antiviral regulation.

   In this article, we enhanced the analysis of differentially expressed
   genes at both the transcriptional and translational levels post-RSV
   infection. Our findings reveal that certain genes are specifically
   regulated at the translational level after viral infection, which will
   aid in our understanding of the mechanisms of virus infection and
   inform strategies to protect rice crops from viral diseases.

4. Methods

4.1. Plant Materials and Growth Conditions

   The rice cultivar ZH11 was used as the wildtype variety in this study.
   Seeds were germinated by soaking them in water in a 36 °C incubator for
   three days. After germination, the seedlings were transferred to a
   greenhouse maintained at 28–30 °C with a 14/10 h light/dark cycle.

4.2. Insect Vectors and Virus Infection

   The transmission of RSV was facilitated by the small brown planthopper
   (Laodelphax striatellus). For the virus transmission experiments on
   transgenic rice, virus-free nymphs were fed on RSV-infected rice plants
   for 3–5 days. The insects were then moved to healthy rice seedlings for
   approximately 10 days to allow for viral circulation within the
   insects. Subsequently, 2–3 SBPHs carrying RSV were placed on each
   transgenic rice plant at the 3- to 4-leaf stages for a duration of 3
   days. Concurrently, the same number of virus-free insects was
   introduced to another set of rice seedlings as a negative control.
   After the feeding period, all insects were removed, and the inoculated
   plants were grown in a fertile field to monitor for symptoms
   [[132]9,[133]10]. The primers used in the qPCR analysis are listed in
   [134]Supplementary Table S3.

4.3. Ribosome Profiling

   To digest RNA other than RPFs, cell or tissue lysates were treated with
   the non-specific endoribonuclease RNase I. Size-exclusion
   chromatography was performed using a MicroSpin S-400 HR column to
   separate monosomes. RPF fragments (20–38 nt) were purified via PAGE
   gel. Subsequently, the ends of these fragments were phosphorylated and
   ligated to 5′- and 3′-end adapters. The RNA samples were then processed
   with an rRNA depletion kit (Qiagen, 334387) to minimize rRNA
   contamination. Following this, the fragments underwent reverse
   transcription to generate cDNAs, which were then amplified by PCR
   [[135]48,[136]49]. After constructing the library using the Multiplex
   Small RNA Library Prep Set for Illumina (Set1, NEB, E7300L), the
   concentration of the library was measured with the Qubit^® 2.0
   Fluorometer and adjusted to 1 ng/μL. The insert size of the library was
   assessed using the Agilent 2100 Bioanalyzer. Finally, the precise
   concentration of the cDNA library was verified using qPCR. Once the
   insert size and concentration were confirmed to be correct, the samples
   were ready for sequencing.

4.4. mRNA Sequencing Library Construction

   Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA,
   USA), following the manufacturer’s instructions. The HISAT2 v2.0.5 was
   used to build the index for the reference genome, and paired-end clean
   reads were aligned to the reference genome using HISAT2 v2.0.5. We
   chose HISAT2 as the alignment tool.

4.5. Quality Control for Raw Data

   The raw FASTQ data were processed using internal Perl and Python
   scripts. In this step, clean reads were obtained by removing the
   following reads: reads with 5′ adapter sequences; reads lacking 3′
   adapter sequences or insertion sequences; reads with more than 10%
   ambiguous bases (N); reads for which over 30% of bases had a Qphred
   score ≤ 20. The 3′ adapter sequences of the reads were trimmed, and the
   Q20, Q30, and GC contents of the clean data were calculated. Next,
   Bowtie (version 1.1.2) was used to align the reads with rRNA sequences
   from the SILVA database, identifying and discarding reads that aligned
   to rRNA without allowing mismatches. Bowtie was also used to align the
   reads with tRNA sequences from the Rfam database, identifying and
   discarding the remaining reads derived from tRNA, again without
   allowing mismatches [[137]50]. Subsequent analyses were conducted based
   on high-quality clean data.

4.6. Quantification of Gene Expression Levels

   Quantification of mapped results at the gene level was carried out
   using HTSeq (0.9.1). HTSeq is a Python package that calculates the
   number of mapped reads for each gene. RPKM values were generated to
   represent the gene expression level of each specific gene.

4.7. Differential Expression Analysis

   Differential expression analysis was performed using the DESeq2 R
   package (version 1.14.1). DESeq2 is a statistical analysis tool based
   on a negative binomial distribution model that identifies differential
   expression in digitized gene expression data. The Benjamini–Hochberg
   procedure was used to control the false-discovery rate and adjust the
   resulting p-values. The significance criteria for differentially
   expressed genes were set as follows: |log2(Fold Change)| > 0 and
   p-value < 0.05.

4.8. uORF Analysis

   uORF expression was assessed using an RPKM threshold ≥ 1 [[138]26].
   Subsequently, the R library (SeqLogo) was utilized to visualize the
   enrichment of motifs surrounding the start codons of expressed versus
   non-expressed uORFs.

4.9. TE Analysis

   TE was calculated as the ratio of RPKM values from Ribo-seq to RPKM
   values from RNA-seq. For samples with biological replicates,
   differential TE analysis was performed using RiboDiff (version 0.2.1)
   [[139]51,[140]52].

Supplementary Materials

   The following supporting information can be downloaded at:
   [141]https://www.mdpi.com/article/10.3390/v16121866/s1, Figure S1:
   Healthy rice seedlings and RSV infected rice seedlings (A) Symptoms in
   rice plants and WT after RSV-infection. (B) RT-qPCR analysis of the
   relative mRNA levels of RSV CP in RSV-infected rice plants and WT ZH11
   at 30 dpi. Figure S2: (A) Three-nucleotide periodicity in heathy
   seedlings. Figure S3: (A) Three-nucleotide periodicity in RSV-infected
   seedlings. Table S1: uORFs analysis in RSV-infected seedings. Table S2:
   uORFs analysis in healthy seedings. Table S3: The primers using to
   viral RNA analysis.
   [142]viruses-16-01866-s001.zip^ (462.4KB, zip)

Author Contributions

   Conceptualization, C.W.; methodology, C.W. and S.L.; software, C.W.;
   validation, Y.T. and C.Z.; formal analysis, C.W.; investigation, C.W.
   and S.L.; resources, J.C.; data curation, C.W.; writing—original draft
   preparation, C.W.; writing—review and editing, C.W.; visualization,
   Z.S.; supervision, Z.S.; project administration, Z.S.; funding
   acquisition, Z.S. All authors have read and agreed to the published
   version of the manuscript.

Data Availability Statement

   Any information required to reanalyze the data reported in this paper
   will be shared by the lead contact upon reasonable request.

Conflicts of Interest

   The authors declare no competing interests.

Funding Statement

   This research was funded by National Natural Science Foundation of
   China (U23A6006, 32272555, 32401818), Zhejiang Provincial Natural
   Science Foundation (LZ22C140001) and The Ningbo Major Research and
   Development Plan Project (2023Z124). And the APC was funded by Young
   Elite Scientist Sponsorship Program by CAST (YESS20210121).

Footnotes

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References