Transcriptome analysis of transgenic rice responds to rice stripe virus p3 expression

Transcriptome analysis of transgenic rice responds to rice stripe virus p3 expression | 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 Transcriptome analysis of transgenic rice responds to rice stripe virus p3 expression Binhao Gao, Long Ma, Huiyuan Zhang, Sinan Chen, Qiao Wang, Ling Qing, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7359704/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Feb, 2026 Read the published version in Journal of Plant Diseases and Protection → Version 1 posted 5 You are reading this latest preprint version Abstract The p3 protein, is a viral suppressor of RNA silencing (VSR), encoded by the viral strand of RNA3 in the rice stripe virus (RSV). As a VSR, p3 facilitates viral infection, but its other function in the host plant is poorly understood. This study analyzed the differentially expressed genes (DEGs) of transgenic p3 gene rice using transcriptome sequencing and quantitative real-time PCR (RT-qPCR). The transcriptome result showed 533 DEGs between transgenic p3 rice and wild-type rice, which included 214 upregulated genes and 319 downregulated genes. Among them, NB-ARC (OS11G0492300, OS06G0268500, OS11G0227100) in this study were up-regulated. Expression of kinase genes (OS10G0539600, OS03G0331700, Os05g0498900, Os02g0786900, OS03G0758250, OS04G0339800) were altered in transgenic plants, with the majority of them being down-regulated and brassinosteroid biosynthesis gene were uniquely expressed (OS03G0602300) or down-regulated (Os07g0162100) in OS_p3 plants. Because the system involving plant-pathogen interaction and brassinosteroid production is intricate, the preliminary research identified two pathways in order to set the groundwork for future research via functional analysis of candidate genes and particular route locations. The study findings enhance understanding of the interactions between VSRs and host plants. Rice stripe virus p3 protein Transgenic rice Transcriptome analysis Figures Figure 1 Figure 2 Figure 3 Introduction Rice stripe virus (RSV) belongs to the genus Tenuivirus and is transmitted by the small brown plant hopper ( Laodelphax striatellus ) (Falk & Tsai. 1998). The RSV genome contains four single-stranded RNAs, which are named RNA1 to RNA4 (Cho et al. 2013). The RNA-dependent RNA polymerase (RdRp) is encoded by the viral-complementary RNA (vcRNA) of RNA1 (Barbier et al. 1992). Segments RNA2 to RNA4 have two non-overlapping ORFs on opposite strands separated by an intergenic region (IR) (Wu et al. 2013; Zhu et al. 1991). RNA3 encodes p3 (a viral suppressor of RNA silencing, VSR) and pc3 (coat protein, CP) from the viral RNA3 (vRNA3) and vcRNA3, respectively (Xiong et al. 2009). RNA silencing is a defense system of plants to combat viral infection. To counteract this mechanism, viruses encode RNA silencing suppressors (VSRs) to overcome antiviral RNA silencing. VSRs from different viruses block different steps in the RNA silencing pathway (Diaz-Pendon & Ding. 2008; Roth et al. 2004; Shamandi et al. 2015). P19 of tomato bushy stunt virus (TBSV), and p69 from turnip yellow mosaic virus (TYMV) bind small double-strand RNA (dsRNA) and/or long dsRNA (Hemmes et al. 2007; Lakatos et al. 2004; Roth et al. 2004). High plains wheat mosaic virus disrupts the RNA silencing defense pathway in plants by forming functional tetrameric structures, but its mechanism of action does not depend on binding to small interfering RNA (siRNA) (Hamo et al.2024). Cucumber mosaic virus (CMV) 2b and potato virus X (PVX) p25 interact with Argonaute (AGO) proteins which are core components of the RNA-induced silencing complex (RISC) (Chiu et al. 2010; Zhang et al. 2006). Furthermore, VSRs have multiple functions in plants. For example, plants expressing HC-Pro, the suppressor of tobacco etch virus (TEV), demonstrate enhanced resistance to tomato black ring virus (TBRV) and the oomycete Peronospora tabacina (Pruss et al. 2004). The RNA silencing suppressor P3 encoded by the virus (BdCV1) significantly reduces the damage caused by pear ring spot disease by inhibiting the pathogenicity of the host fungus ( Botryosphaeria dothidea ), thereby providing indirect protection to plants (Li et al.2023). The p3 protein, encoded by the RSV RNA3, is an RNA silencing suppressor. As a VSR, p3 not only binds 21-nt single-stranded siRNA, siRNA duplex and long single-stranded RNA to suppress RNA silencing (Xiong et al. 2009), but also induces the accumulation of several miRNAs by interaction with DRB1 and enhances viral infection and pathogenesis in rice (Zheng et al. 2017). However, UBL5 (ubiquitin-like protein 5) from host plants ( Oryza sativa and Nicotiana benthamiana ), interacted with p3 and mediated its degradation through 26S proteasome pathway. This mechanism affects not just the function of p3 as an RNA silencing suppressor, but also the ability of RSV to infect plants (Chen et al. 2020). Furthermore, p3 reduced expression of osa-miR171b upon RSV infection contributes to RSV symptom induction (Tong et al. 2017). Liu et al. (Liu et al. 2017) reported that RSV p3 interacts with host OsCIPK30, and OsCIPK30 regulates the expression of pathogenesis-related genes to enhance tolerance to RSV. Recently, it was reported that RSV p3 interacted with an uncharacterized protein P3IP in O. sativa and N. benthamiana , and induces p3 degration through autophagy pathways and limited RSV infection (Jiang et al. 2021a; Jiang et al. 2021b). RSV p3 can be recognized and interact with OsSNRK3.25 in rice, causing phosphorylation and thereby enhancing the host antiviral RNAi pathway (Zhuang et al.2025).These studies showed that RSV p3 plays dual and reverse roles in host plants during RSV infection. Our previous study revealed that expressing RSV p3 exhibits resistance to the fungus Magnaporthe oryzae in rice, and p3 transgenic N. benthamiana plants exhibited enhanced resistance to RSV (Wu et al. 2014; Wu et al. 2018). Further exploration is needed to determine the molecular mechanisms contributing to these roles of p3. To uncover the function of p3, which is vital to understand the interaction between host and virus, in this study, the transcriptome data was analyzed and quantitative real-time PCR (RT-qPCR) was conducted to identify the differentially expressed genes (DEGs) in the transgenic p3 rice. Materials and methods Plant growth Wild-type rice ( Oryza sativa L. spp. japonica. cv. Nipponbare) (Os_CK) and RSV p3 trangenic rice (Os_p3) were grown in plant illuminating incubator (RXZ-280B, NingBo JiangNan YiQiChang) under a 16-h light and 8-h dark cycle at 26ºC, 75% humidity. And the expression of p3 has been tested before (Wu et al., 2014). RNA sequencing Os_CK and Os_p3 plants at the 3th leaf stage were used for transcriptome sequencing. Total RNA was extracted from six different plants (Os_CK 1, Os_CK 2, Os_CK 3, Os_p3-1, Os_p3-2 and Os_p3-3) using TRIzol reagent according to the manufacturer’s instructions (Invitrogen, Carlsbad, CA, USA). RNA was qualified using agarose gel electrophoresis and NanoDrop assay. These six RNA samples were sent to the Novogene Bioinformatics Technology Company (Beijing, China) for transcriptome sequencing using Illumina HiSeq 2500 platform. Raw data processing and de novo assembly Raw data (raw reads) of fastq format were firstly processed through in-house perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N and low quality reads from raw data. Meanwhile, Q20, Q30, GC-content and sequence duplication level of clean data were calculated. All the downstream analyses were based on the clean data with high quality (Li et al. 2018). De novo assembly of clean reads was carried out using Trinity ( http://trinityrnaseq.sourceforge.net/ ). In brief, clean reads with a certain overlap length were initially combined to form long fragments without N. These fragments are called contigs. Related contigs were clustered using the TGICL software to yield unigenes (without N) that cannot be extended on either end, and redundancies were removed to acquire non-redundant unigenes (Wu et al. 2014). Reads mapping to the reference genome The draft sequence of the Oryza sativa L. reference genome have been downloaded from the SGN ftp site ( ftp://ftp.solgenomics.net/genomes/Oryza_sativa/assembly ) directly. Index of the reference genome was built using Bowtie v2.2.3 (Broad Institute, Cambridge, MA, USA) and single-end clean reads were aligned to the reference genome using TopHat v2.0.12 (Broad Institute, Cambridge, MA, USA) (mismatch = 2) (Li et al. 2018). Analyses of transcriptome data DEGs (differentially expressed genes) were identified by a P-value ≤ 0.05 and an expression change of 2-fold or more (|log2Foldchange| ≥ 1) between the two treatments using IDEG6 software. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) classification were used to determine the main biological functions and pathways related to DEGs. GO was implemented using the GOseq R package, in which gene length bias was corrected, and GO terms with corrected P-values ≤ 0.05 were considered significantly enriched in DEGs. GO annotation was performed using the REVIGO web server ( http://revigo.irb.hr/ ). The KEGG classification of the DEGs was performed using the KEGG Mapper Annotate Sequence tool with the BlastKOALA server available on the Kyoto Encyclopedia of Genes and Genomes website ( http:www.kegg.jp/keg/tool/annotate_sequence.html ). KOBAS software was used to test the statistical enrichment of DEGs in KEGG pathways. Real-time quantitative PCR (RT-qPCR) validation Total RNA was extracted with TRIzol reagent from Os_CK and Os_p3 plants at the 3th leaf stage according to the manufacturer’s instructions (Invitrogen, Carlsbad, CA, USA). These six plants were different from those plants for RNA sequencing, but all of samples were collected at the same treatment and time for both RNA extractions, 24 days after sowing. 1 µg of total RNA was used to synthesize cDNA by the PrimeScript RT Reagent Kit (TAKARA Bio, RR037A). RT-qPCR was performed using the SYBR Green Real-time PCR Master Mix (ToYoBo, JAPAN) with the iQ5 Real-Time PCR system (Bio-Rad, USA) with gene-specific primers in the Additional file Table S1 , each reaction containing 10.0 µL of SYBR Green Real-time PCR Master Mix, 1.0 µL of cDNA, 0.5 µL of (10 pM) primers, and 8.0 µL of water. The expression levels of transcripts are presented relative to the corresponding control samples for each condition. The OsActin gene was used as an internal control gene. All RT-qPCR experiments were performed in triplicate. The data were calculated by the 2 -ΔΔCT method using the Excel soft. Results RNA Sequencing Reveals Predominant Downregulation of Differentially Expressed Genes in RSV p3-Expressing Rice Seedlings To illustrate the transcriptional responses of the rice to RSV p3, RNA from six plants that included three samples of Os_CK and three samples of Os_p3 was used to construct six cDNA libraries for RNA sequencing. After trimming of adaptor sequences and removal of low-quality reads, clean reads were obtained from six libraries of Os_CK and Os_p3 plants. The results showed that the clean bases data of these six samples were among 8.29 GB and 10.79 GB. There were 62,350,654–71,917,236, 55,255,420–63,731,312 clean reads in the Os_CK and Os_p3 samples, respectively. And Q20 and Q30 was used to analysis the quality RNA sequencing. The results showed that Q20 (the percentage of base content with a base calling error less than 1%) and Q30 (the percentage of base content with a base calling error less than 0.1%) values are ranged from 95.31 to 95.51% and 88.61 to 89.03%, respectively. So, over 88% of the obtained base content had a base calling error probability less than 0.1% (Table 1 ). Altogether, these RNA sequencing results were sufficient for subsequent gene expression analysis. First, the expression level of genes was normalized as clean reads per kilobase of exon model per million mapped reads (RPKM). When we set the RPKM > 1, the expressed genes number were 19,132 and 19,620 in the Os_CK and Os_p3 samples, respectively (Fig. 1 a). Among these genes, there were 451 unique expressed genes in the Os_CK plants and 939 unique expressed genes in the Os_p3 plants. Table 1 List of quality of data output Sample name Raw reads Clean reads Clean bases Error rate (%) Q20 (%) Q30 (%) GC content (%) Os_CK1 73,797,144 71,917,236 10.79GB 0.02 95.48 89.03 56.12 Os_CK2 64,031,046 62,350,654 9.35GB 0.02 95.46 88.96 57.08 Os_CK3 64,938,042 63,450,002 9.52GB 0.02 95.51 88.99 58.04 Os_p3-1 65,210,035 63,731,312 9.56GB 0.03 95.41 88.81 56.10 Os_p3-2 64,278,212 62,716,270 9.41GB 0.02 95.45 88.92 56.05 Os_p3-3 56,673,022 55,255,420 8.29GB 0.03 95.31 88.61 57.19 Sample name: the name of the sample. Raw reads: statistics of raw sequence data. Clean read: the sum of reads, after removing low quality reads. Clean bases: the sum of bases from clean reads. Error rate (%): sequencing Phred score [Qphred = -10log10(e)]. Phred score from the Base Calling. Q20 (%), the percentage of base content with a base calling error less than 1%. Q30 (%), the percentage of base content with a base calling error less than 1%. GC content: the percentage of bases that are G and C. The differentially expressed genes (DEGs) were determined by comparing the expression level of genes in p3 transgenic rice plants with those from wild-type rice with the stringent criteria, since DESeq has eliminated biological variation, our screening criteria for differential genes are generally, padj < 0.05. DEGs were identified by a P-value ≤ 0.05, 533 DEGs were found in response to p3 at the seedling period in these six transcriptome profiles, including 214 upregulated genes and 319 downregulated genes (Fig. 1 b, Additional file Table S2 ). These results indicate that the majority genes differentially expressed in response to p3 were downregulated compared with wild-type rice. GO and KEGG Enrichment Reveals Photosynthesis and Plastid-Related Processes are Predominant among RSV p3-Responsive DEGs in Rice To further characterize the specific biological processes these DEGs may be involved with, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) classification were used. All 533 DEGs were annotated and the most of the top 10 GO terms of biological process, cellular component, molecular function three categories significantly enriched respectively were listed (Fig. 2 a). The three most highly represented GO terms under the biological process category were “metabolic process,” “cellular process”, and “single-organism process”, each of them account for more than 40 percent of DEGs, respectively. The most abundant GO terms in the cellular component category were “cell” “cell part”. The two most abundant terms in the molecular function category were “binding” and “catalytic activity”. They also account for more than 40 percent of DEGs. So we found that the enriched GO terms were mainly classified as biological processes, the most of the top 5 GO terms of biological processes by ranking p-value from Fisher’s Exact Test is plastid organization, single-organism biosynthetic process, photosynthesis, chloroplast organization and photosystem II assembly (Fig. 2 b). Integrated KEGG Enrichment and Gene Expression Validation Reveal RSV p3-Mediated Modulation of Metabolic and Defense Pathways in Rice The KEGG classification of the DEGs was performed using the KEGG Mapper Annotate Sequence tool. The top 20 of KEGG pathways of the more or main significantly enriched are Ubiquinone and other terpenoid-quinone biosynthesis, Carbon fixation in photosynthetic organisms, Metabolic pathways, Biosynthesis of secondary metabolites, Vitamin B6 metabolism, Fatty acid metabolism, Terpenoid backbone biosynthesis, Nitrogen metabolism, Carbon metabolism, Pyruvate metabolism, Biosynthesis of unsaturated fatty acids, Phenylalanine metabolism, Glyoxylate and dicarboxylate metabolism, Fatty acid biosynthesis, Biotin metabolism, Alanine, aspartate and glutamate metabolism, Butanoate metabolism, Photosynthesis and Glycerolipid metabolism, Starch and sucrose metabolism in the KEGG pathway analysis about 533 DEGs (Fig. 3 a). In order to explore the differentially expressed genes of the transgenic p3 rice, 939 unique expressed genes in the transgenic rice was analyzed by KEGG pathway analysis (Fig. 3 b), the result showed that RSV p3 affects plant-pathogen interaction, brassinosteroid biosynthesis and phosphatidylinositol signaling system and so on. To validate expression pattern of genes from RNA sequence analysis, eight genes were selected from those with significant differential expression for RT-qPCR analysis, and OsActin was used as the internal reference gene. The results showed that all these eight genes have different expression in the p3 transgenic rice, compared to that in the Os_CKs, including seven up-regulates genes and one down-regulated gene (Table 2 ). Compared with the results of RNA sequencing, RT-qPCR showed that seven genes have the same expression trend, except Proteinase inhibitor I12 (OS01G0124200). The results revealed that the RT-qPCR data were consistent with the transcriptome results. Table 2 Verification of RNA sequence analysis results using RT-qPCR Gene ID Description(s) RNA-seq Fold RT-qPCR Fold VAR Pval OS11G0227100 Leucine-rich repeat||NB-ARC 1.22 2.81 0.74 0.063 OS11G0492300 Leucine-rich repeat||NB-ARC 2.40 9.25 0.42 0.013 OS11G0461000 serine carboxypeptidase -3.69 -11 0.25 0.006 OS04G0249600 thiosulfate sulfurtransferase 3.68 5.12 1.24 0.057 OS03G0210200 Proteinase inhibitor I25 0.97 2.45 0.6 0.033 OS01G0124200 Proteinase inhibitor I12 -0.65 2.05 0.32 0.028 OS01G0165800 F-box protein 2.23 4.40 0.29 0.016 OS08G0193600 F-box protein 2.10 3.08 1.08 0.054 A selection of upregulated or downregulated genes from rice samples were tested. The OsActin gene was used as an internal control. Fold change is indicated as a ratio of Os_p3 vs Os_CK calculated from normalized median intensity values (n = 3). VAR value represents the standard deviation of the qRT-PCR signals (n = 3). Pval, the p-value of t-test, indicate statistically significant differences compared with Os_CK. Discussion VSRs affect the expression of host genes in response to biotic or abiotic stress (Jada et al. 2013; Soitamo et al. 2011; Westwood et al. 2013). For example, the expression of PVX-p25 in transgenic tobacco plants caused 1,350 up-regulated transcripts and 5 down-regulated transcripts in the leaves (Jada et al. 2013). Based on comparative transcriptomic analysis of SA treatment (promoting) and ABT treatment (inhibiting SA synthesis), 1,759 differentially expressed genes with opposite regulation were identified in potatoes (Chen et al. 2025). The expression of 748 and 332 genes was significantly changed in the leaves and flowers, respectively, in HC-Pro transgenic plants (Soitamo et al. 2011). CMV-infection 2b-dependent induced genes were enriched in plant immunity pathways, including salicylic acid (SA) signaling pathway (Zhao et al. 2018). When Rhizoctonia solani infects resistant and susceptible rice cultivars, the plant-pathogen interaction pathway was significantly affected by R. solani infection on resistant cultivars (Shi et al. 2020). In our previous study, we found that RSV p3 induced N.benthamiana resistance by affecting host gene expression (Wu et al. 2018), the DEGs associated with ribosomes, photosynthesis, and carbon metabolism in N. benthamiana , and most highly represented GO terms were “metabolic process,” “cellular process”, “single-organism process”, “cell part”, “cell”, “catalytic activity” and “binding”. We also investigated the influence of p3 on gene expression using RNA sequencing analysis and RT-qPCR verification. The results showed that RSV p3 expression was associated with 533 DEGs in rice. GO annotation and KEGG pathway enrichment analysis revealed that p3 affects plant-pathogen interaction, brassinosteroid biosynthesis and phosphatidylinositol signaling system. And the most abundant GO terms were also classified into “metabolic process,” “cellular process”, “single-organism process”, “cell part”, “cell”, “catalytic activity” and “binding”. Furthermore, our results of KEGG of DEGs from transgenic p3 rice were consistent with the resistance of transgenic p3 rice independent of the SA and JA pathway (Wu et al. 2014). Compare with its function in N. benthamiana , RSV p3 also affects plant-pathogen interaction, and GO. Nucleotide-binding site-leucine-rich repeat (NBS-LRR) proteins play crucial roles in plant development and stress responses, and some of them are induced to against bacterial and viral infection(Santos et al. 2010; Zipfel et al. 2006). Protease inhibitors (PIs) are derived from R genes, belong to the PR-6 family of pathogenesis-related proteins, affect plant growth and metabolism by combining and regulating proteases in insects and the host. By binding proteinases and modulating proteinase activity in numerous biochemical processes, PIs serve diverse functions in development and metabolism. (Grudkowska & Zagdańska. 2004; Sawano et al. 2008). F-box proteins components of SKP1/CUL1/F-box (SCF) complexes responsible for recognizing target substrates, and SCF complexes regulate ethylene (ET) signal transduction at multiple points, such as defence against pathogens infection(Jia et al. 2016). Bowman-Birk-type protease inhibitors (BBTIs) induced by JA and corn borer in corn exhibit significant anti-feeding activity against corn borers (Chen et al.2024). Potato type I protease inhibitor (PI) in tobacco interacts with P25 encoded by potato virus X (PVX) and targets P25 for degradation through autophagy and ubiquitination, thereby reducing PVX infection (Shen et al.2025). In this study, we found that the expression of NBS-LRR, F-box and PIs genes was induced in p3 transgenic rice using RNA sequence analysis and RT-qPCR detection. Brassinosteroids (BRs) are plant growth–promoting natural products required for plant growth and development, BR signaling is essential for the development of intercalary meristems(Sakamoto et al. 2006; Yamamuro et al. 2000). Brassinosteroid (BR)-regulated GhBEE3-like genes can increase the expression of stress-related genes, thereby enhancing the drought tolerance of plants (Chen et al.2025). However, When N. benthamiana infected by tobacco curly shoot virus (TbCSV), brassinosteroid synthesis-related gene, 3-epi-6-deoxocathasterone 23-monooxygenase (CYP90C1/D1) was significantly decreased upon virus infection (Li et al. 2018). In this study, Gene involve in brassinosteroid biosynthesis were uniquely expressed (OS03G0602300) or down-regulated (Os07g0162100) in OS_p3 plants, it was speculated that brassinosteroid may help the RSV p3 protein to maintain rice growth and development. The DGEs related plant-pathogen interaction and brassinosteroid biosynthesis were showed in Table 3 . The resistance (R) genes in plants confer specificity to the innate immune system. Most R genes have a centrally located NB-ARC (nucleotide-binding adaptor shared by APAF-1, R proteins, and CED-4) domain (Tameling et al. 2006). NB-ARCs (OS11G0492300, OS06G0268500) in this study were up-regulated DEGs, p3 protein in rice may regulate the expression NB-ARC to enhance host resistance. Plant-pathogen interaction includes complex process that PAMP-triggered immunity (PTI) and effector-triggered immunity (ETI), genes with kinase (OS10G0539600, OS03G0331700, Os05g0498900, Os02g0786900) were changed in Os_p3. Table 3 DEGs of plant-pathogen interaction and brassinosteroid biosynthesis Pathway way Unigene Gene name log 2 FC Pval plant-pathogen interaction OS11G0492300 NB-ARC 2.4064 0.00 OS06G0268500 NB-ARC 2.4510 0.00 OS11G0227100 NB-ARC 1.2155 0.00 OS03G0210200 PIs 0.9749 0.04 OS01G0124200 PIs -0.6562 0.57 OS01G0165800 F-box proteins 2.2345 0.00 OS08G0193600 F-box proteins 2.0867 0.00 OS10G0539600 SPK -0.7462 0.00 OS03G0331700 CML27 -0.6462 0.00 Os05g0498900 OJ1789_D08.20 -1.0593 0.00 Os02g0786900 B1096D03.48 -1.8835 0.00 OS03G0758250 Os03g0758300 0.1212 0.78 OS04G0339800 OSJNBa0004L11.12 1.3525 0.11 brassinosteroid biosynthesis OS03G0602300 CYP85A1 0.0613 0.86 Os07g0162100 P0529H11.33 -1.0161 0.00 DEGs were Pvalue<0.05 FC Fold change Our findings suggest that p3 regulates the expression of genes involved in plant-pathogen interactions and brassinosteroid production in rice. We believe that p3 acts as a host defense inducer, inhibiting pathogenic development by promoting the expression of genes like NBS-LRR and protease inhibitors genes. The plant-pathogen interaction and brassinosteroid production route is complex; nevertheless, early research has identified the two pathways, layed the groundwork for future functional studies of these candidate genes and their exact placement in the process. Declarations Acknowledgments We are grateful to Fei Yan of the Ningbo University, Ningbo, China, for kindly providing the rice samples and p3 transgenic rice plants. Funding This research was supported financially by the National Natural Science Foundation of China (31601607), and the Fundamental Research Funds for the Central Universities (Grant No. SWU-XDJH202318). Author Contributions Gentu Wu and Binhao Gao performed the major experiments. Binhao Gao and Long Ma developed the research program and wrote manuscripts. Huiyuan Zhang, Sinan Chen and Qiao Wang participated in carrier construction, data processing and analysis. Ling Qing and Gentu Wu conceived the study and revised the manuscript. Human and animal rights No human and/or animal participants were present in this research. Informed consent All authors of this study consent to this submission. Conflict of interest The authors declare no conflict of interest. References Barbier P, Takahashi M, Nakamura I, Toriyama S, Ishihama A (1992) Solubilization and promoter analysis of RNA polymerase from rice stripe virus. Journal of Virology 66(10): 6171-6174. Chen BH, Lin L, Lu YW, Peng JJ, Zheng HY, Yang QK, Rao SF, Wu GW, Li JM, Chen Z, Song BA, Chen JP, Yan F (2020) Ubiquitin-Like protein 5 interacts with the silencing suppressor p3 of rice stripe virus and mediates its degradation through the 26S proteasome pathway. PLoS Pathogens 16(8):e1008780. 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Sawano Y, Hatano K, Miyakawa T, Komagata T, Miyauchi Y, Yamazaki H, Tanokura M (2008) Proteinase inhibitor from ginkgo seeds is a member of the plant nonspecific lipid transfer protein gene family. Plant Physiology 146(4): 1909-1919. Shamandi N, Zytnicki M, Charbonnel C, Elvira-Matelot E, Bochnakian A, Comella P, Mallory AC, Lepere G, Saez-Vasquez J, Vaucheret H (2015) Plants encode a general siRNA suppressor that is induced and suppressed by viruses. PLoS Biology 13(12): e1002326. Shi W, Zhao SL, Liu K, Sun YB, Ni ZB, Zhang GY, Tang HS, Zhu JW, Wan BJ, Sun HQ, Dai JY, Sun MF, Yan GH, Wang AM, Zhu GY (2020) Comparison of leaf transcriptome in response to Rhizoctonia solani infection between resistant and susceptible rice cultivars. BMC Genomics 21: 245. Soitamo, Aj, Jada, Lehto (2011) HC-Pro silencing suppressor significantly alters the gene expression profile in tobacco leaves and flowers. BMC Plant Biology 11: 68. Tameling WIL, Vossen JH, Albrecht M, Lengauer T, Berden JA, Haring MA, Cornelissen BJC, Takken FLW (2006) Mutations in the NB-ARC Domain of I-2 that impair ATP hydrolysis cause autoactivation. Plant Physiology 140(4): 1233-1245. Tong A, Yuan Q, Wang S, Peng J, Lu Y, Zheng H, Lin L, Chen H, Gong Y, Chen J (2017) Altered accumulation of osa-miR171b contributes to rice stripe virus infection by regulating disease symptoms. Journal of Experimental Botany 68(15): 4357-4367. Westwood JH, Mccann L, Naish M, Dixon H, Murphy AM, Stancombe MA, Bennett MH, Powell G, Webb A, Carr JP (2013) A viral RNA silencing suppressor interferes with abscisic acid‐mediated signalling and induces drought tolerance in Arabidopsis thaliana . Molecular Plant Pathology 14(2): 158-170. Wu GT, Lu YW, Zheng HY, Lin L, Yan F, Chen JP (2013) Transcription of ORFs on RNA2 and RNA4 of rice stripe virus terminate at an AUCCGGAU sequence that is conserved in the genus Tenuivirus. Virus Research 175(1): 71-77. Wu GT, Wang JY, Yang Y, Dong B, Wang YL, Sun GC, Yan CQ, Yan F, Chen JP (2014) Transgenic rice expressing rice stripe virus NS3 protein, a suppressor of RNA silencing, shows resistance to rice blast disease. Virus Genes 48(3): 566-569. Wu GT, Zheng GX, Hu Q, Ma MG, Li MJ, Sun XC, Yan F, Qing L (2018) NS3 protein from rice stripe virus affects the expression of endogenous genes in Nicotiana benthamiana . Virology Journal 15: 105. Xiong RY, Wu JX, Zhou YJ, Zhou XP (2009) Characterization and subcellular localization of an RNA silencing suppressor encoded by rice stripe tenuivirus. Virology 387(1): 29-40. Yamamuro C, Ihara Y, Wu X, Noguchi T, Fujioka S, Takatsuto S, Ashikari M, Kitano H, Matsuoka M (2000) Loss of runction of a rice brassinosteroid insensitive1 homolog prevents internode elongation and bending of the lamina joint. The Plant Cell 12(9): 1591-1606. Chen EY, Yang XB, Liu R, Zhang MK, Zhang M, Zhou F, Li DX, Hu HY, Li CW (2025) GhBEE3-Like gene regulated by brassinosteroids is involved in cotton drought tolerance. Frontiers in Plant Science 13:1019146. Zhang XR, Yuan YR, Pei Y, Lin SS, Tuschl T, Patel DJ, Chua NH (2006) Cucumber mosaic virus-encoded 2b suppressor inhibits Arabidopsis argonaute1 cleavage activity to counter plant defense. Genes Development 20(23): 3255-3268. Zheng LJ, Zhang C, Shi CN, Yang ZR, Wang Y, Zhou T, Sun F, Wang H, Zhao SS, Qin QQ, Qiao R, Ding ZM, Wei CH, Xie LH, Wu JG, Li Y (2017) Rice stripe virus NS3 protein regulates primary miRNA processing through association with the miRNA biogenesis factor OsDRB1 and facilitates virus infection in rice. PLoS Pathogens 13(10): e1006662. Zhao JH, Liu XL, Fang YY, Fang RX, Guo HS (2018) CMV2b-dependent regulation of host defense pathways in the context of viral infection. Viruses 10: 618. Zhu YF, Hayakawa T, Toriyama S, Takahashi M (1991) Complete nucleotide sequence of RNA 3 of rice stripe virus: an ambisense coding strategy. Journal of General Virology 72: 763-767. Zipfel C, Kunze G, Chinchilla D, Caniard A, Jones JDG, Boller T, Felix G (2006) Perception of the bacterial PAMP EF-Tu by the receptor EFR restricts Agrobacterium-mediated transformation. Cell 125(4) : 749-760. Supplementary Files AdditionalfileTableS1.doc AdditionalfileTableS2AllDEGsinOsp3vsOsCK.xls Cite Share Download PDF Status: Published Journal Publication published 27 Feb, 2026 Read the published version in Journal of Plant Diseases and Protection → Version 1 posted Reviewers agreed at journal 09 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Editor invited by journal 14 Aug, 2025 Editor assigned by journal 13 Aug, 2025 First submitted to journal 12 Aug, 2025 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. We do this by developing innovative software and high quality services for the global research community. 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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-7359704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512158631,"identity":"f56602c2-d26e-4a76-9a1a-38bdb9f840c6","order_by":0,"name":"Binhao Gao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Binhao","middleName":"","lastName":"Gao","suffix":""},{"id":512158632,"identity":"d2bedde4-1975-4b8a-9c1c-292ab6845237","order_by":1,"name":"Long Ma","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Ma","suffix":""},{"id":512158633,"identity":"9e0339c2-f849-4505-9762-f7c4a444859a","order_by":2,"name":"Huiyuan Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Huiyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":512158634,"identity":"4703b93e-28d6-4189-8339-39bfdc771482","order_by":3,"name":"Sinan Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sinan","middleName":"","lastName":"Chen","suffix":""},{"id":512158635,"identity":"a75d6109-07e9-4e8c-b912-7fcf1d378653","order_by":4,"name":"Qiao Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Qiao","middleName":"","lastName":"Wang","suffix":""},{"id":512158636,"identity":"fe238f60-e520-4085-85a9-5ce79c8bcf55","order_by":5,"name":"Ling Qing","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Qing","suffix":""},{"id":512158637,"identity":"b13970b9-cad1-4ca5-a59c-a2c821fa1b60","order_by":6,"name":"Gentu Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYNCCCgk5fvYGMJOxgQj1QEVnbIwlew6QooWxJS3RYEYCkVoMbqRff8zbcDjBQPL5M2keBhvZDQeYnz3AryWnsJl3x+E8c+kcM6CWNOMNB9jMDfBpMbuRk9jMe+ZwseXsHLbbPAyHEzcc4GGTIKylDajy5vFnQC3/idGSfhCoJS1xww0GM6CWA4S12J95wzhzDjiQc8x/zjFINp55mM0MrxbJ9vQHH96Ao/L4Y4M3FXayfcebn+HVwsDAgxw8IDYzfvVAwP6AoJJRMApGwSgY4QAAX+NPc9eV4RYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4256-2808","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Gentu","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-08-13 01:23:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7359704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7359704/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41348-026-01232-3","type":"published","date":"2026-02-27T15:58:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91413019,"identity":"69bfc84b-35e2-41f1-b02d-b3e33a94571e","added_by":"auto","created_at":"2025-09-16 08:56:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1595506,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagrams of the expressed genes number in the Os_CK and Os_p3 and Volcano plots of DEGs about expressing \u003ca href=\"https://fanyi.so.com/?src=onebox#jointly\" target=\"_blank\"\u003ejointly\u003c/a\u003ein Os_CK and Os_p3(a-b). a, Venn diagrams showing the overlap in the expressed genes between Os_CK and Os_p3. b, DEGs of Os_CK and Os_p3 displayed by volcano plots. The abscissa shows the fold change difference in the expression of genes in different groups, and the vertical coordinates indicate the adjusted p-adj for the differences in expression. Genes without significant differences are indicated by blue dots. The up-regulated genes are represented by red dots, and the down-regulated genes are represented by green dots.\u003c/p\u003e","description":"","filename":"Fig1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7359704/v1/53f7f445c1c50f63ace0ed46.jpg"},{"id":91413018,"identity":"aa569a2c-7a42-4ab3-9958-54ab22104945","added_by":"auto","created_at":"2025-09-16 08:56:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2822165,"visible":true,"origin":"","legend":"\u003cp\u003eThe gene ontology profile of the DEGs between Os_CK and Os_p3 and bar plot of biological process. a, the bar charts represent the functional annotations of the DEGs with the second level of GO (biological process, cellular component, molecular function), the abscissa shows functional annotations, the left vertical coordinates display the percent of gene in different term and the right vertical coordinates shows the number of genes. b, the bar plot of biological process shows the top 10 terms by confidence about p-value.\u003c/p\u003e","description":"","filename":"Fig2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7359704/v1/564b8bcbedbdd5e9329c88ed.jpg"},{"id":91414899,"identity":"e44f1c90-5ed8-4642-a724-f2a7bf3c3207","added_by":"auto","created_at":"2025-09-16 09:12:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2608085,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathways of the significantly enriched 20 pathways in DGEs and 939 unique expressed genes in Os_p3. a, the rich factor reflects the degree of enriched DGEs in a given pathway. The number of enriched DGEs in the pathway is indicated by the circle area, and the circle color represents the ranges of the corrected p-value. KEGG pathways of the significantly enriched 20 pathways in DGEs between Os_CK and Os_p3. b, KEGG pathways of the significantly enriched 20 pathways with 939 unique expressed genes in Os_p3.\u003c/p\u003e","description":"","filename":"Fig3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7359704/v1/f771fdebd97fcdec1c7c1f4b.jpg"},{"id":103765820,"identity":"5895acb2-8eed-465d-832c-0d498ce00eed","added_by":"auto","created_at":"2026-03-02 16:09:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7849780,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7359704/v1/aabdd5cd-f2dd-4887-9cee-7cb5586f7f92.pdf"},{"id":91414900,"identity":"9a53d429-7d63-461a-b6c4-56c91e33d252","added_by":"auto","created_at":"2025-09-16 09:12:54","extension":"doc","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":31744,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalfileTableS1.doc","url":"https://assets-eu.researchsquare.com/files/rs-7359704/v1/065b8cc4350afe102a9e2027.doc"},{"id":91415380,"identity":"9b6591cc-8e52-4793-856c-782303e454b5","added_by":"auto","created_at":"2025-09-16 09:20:54","extension":"xls","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":163328,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalfileTableS2AllDEGsinOsp3vsOsCK.xls","url":"https://assets-eu.researchsquare.com/files/rs-7359704/v1/30b729b2487e0bb6aecea679.xls"}],"financialInterests":"","formattedTitle":"Transcriptome analysis of transgenic rice responds to rice stripe virus p3 expression","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRice stripe virus (RSV) belongs to the genus \u003cem\u003eTenuivirus\u003c/em\u003e and is transmitted by the small brown plant hopper (\u003cem\u003eLaodelphax striatellus\u003c/em\u003e) (Falk \u0026amp; Tsai. 1998). The RSV genome contains four single-stranded RNAs, which are named RNA1 to RNA4 (Cho et al. 2013). The RNA-dependent RNA polymerase (RdRp) is encoded by the viral-complementary RNA (vcRNA) of RNA1 (Barbier et al. 1992). Segments RNA2 to RNA4 have two non-overlapping ORFs on opposite strands separated by an intergenic region (IR) (Wu et al. 2013; Zhu et al. 1991). RNA3 encodes p3 (a viral suppressor of RNA silencing, VSR) and pc3 (coat protein, CP) from the viral RNA3 (vRNA3) and vcRNA3, respectively (Xiong et al. 2009).\u003c/p\u003e\u003cp\u003eRNA silencing is a defense system of plants to combat viral infection. To counteract this mechanism, viruses encode RNA silencing suppressors (VSRs) to overcome antiviral RNA silencing. VSRs from different viruses block different steps in the RNA silencing pathway (Diaz-Pendon \u0026amp; Ding. 2008; Roth et al. 2004; Shamandi et al. 2015). P19 of tomato bushy stunt virus (TBSV), and p69 from turnip yellow mosaic virus (TYMV) bind small double-strand RNA (dsRNA) and/or long dsRNA (Hemmes et al. 2007; Lakatos et al. 2004; Roth et al. 2004). High plains wheat mosaic virus disrupts the RNA silencing defense pathway in plants by forming functional tetrameric structures, but its mechanism of action does not depend on binding to small interfering RNA (siRNA) (Hamo et al.2024). Cucumber mosaic virus (CMV) 2b and potato virus X (PVX) p25 interact with Argonaute (AGO) proteins which are core components of the RNA-induced silencing complex (RISC) (Chiu et al. 2010; Zhang et al. 2006). Furthermore, VSRs have multiple functions in plants. For example, plants expressing HC-Pro, the suppressor of tobacco etch virus (TEV), demonstrate enhanced resistance to tomato black ring virus (TBRV) and the oomycete \u003cem\u003ePeronospora tabacina\u003c/em\u003e(Pruss et al. 2004). The RNA silencing suppressor P3 encoded by the virus (BdCV1) significantly reduces the damage caused by pear ring spot disease by inhibiting the pathogenicity of the host fungus (\u003cem\u003eBotryosphaeria dothidea\u003c/em\u003e), thereby providing indirect protection to plants (Li et al.2023).\u003c/p\u003e\u003cp\u003eThe p3 protein, encoded by the RSV RNA3, is an RNA silencing suppressor. As a VSR, p3 not only binds 21-nt single-stranded siRNA, siRNA duplex and long single-stranded RNA to suppress RNA silencing (Xiong et al. 2009), but also induces the accumulation of several miRNAs by interaction with DRB1 and enhances viral infection and pathogenesis in rice (Zheng et al. 2017). However, UBL5 (ubiquitin-like protein 5) from host plants (\u003cem\u003eOryza sativa\u003c/em\u003e and \u003cem\u003eNicotiana benthamiana\u003c/em\u003e), interacted with p3 and mediated its degradation through 26S proteasome pathway. This mechanism affects not just the function of p3 as an RNA silencing suppressor, but also the ability of RSV to infect plants (Chen et al. 2020). Furthermore, p3 reduced expression of osa-miR171b upon RSV infection contributes to RSV symptom induction (Tong et al. 2017). Liu et al. (Liu et al. 2017) reported that RSV p3 interacts with host OsCIPK30, and OsCIPK30 regulates the expression of pathogenesis-related genes to enhance tolerance to RSV. Recently, it was reported that RSV p3 interacted with an uncharacterized protein P3IP in \u003cem\u003eO. sativa\u003c/em\u003e and \u003cem\u003eN. benthamiana\u003c/em\u003e, and induces p3 degration through autophagy pathways and limited RSV infection (Jiang et al. 2021a; Jiang et al. 2021b). RSV p3 can be recognized and interact with OsSNRK3.25 in rice, causing phosphorylation and thereby enhancing the host antiviral RNAi pathway (Zhuang et al.2025).These studies showed that RSV p3 plays dual and reverse roles in host plants during RSV infection.\u003c/p\u003e\u003cp\u003eOur previous study revealed that expressing RSV p3 exhibits resistance to the fungus \u003cem\u003eMagnaporthe oryzae\u003c/em\u003e in rice, and p3 transgenic \u003cem\u003eN. benthamiana\u003c/em\u003e plants exhibited enhanced resistance to RSV (Wu et al. 2014; Wu et al. 2018). Further exploration is needed to determine the molecular mechanisms contributing to these roles of p3. To uncover the function of p3, which is vital to understand the interaction between host and virus, in this study, the transcriptome data was analyzed and quantitative real-time PCR (RT-qPCR) was conducted to identify the differentially expressed genes (DEGs) in the transgenic p3 rice.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant growth\u003c/h2\u003e\u003cp\u003eWild-type rice (\u003cem\u003eOryza sativa\u003c/em\u003e L. spp. japonica. cv. Nipponbare) (Os_CK) and RSV p3 trangenic rice (Os_p3) were grown in plant illuminating incubator (RXZ-280B, NingBo JiangNan YiQiChang) under a 16-h light and 8-h dark cycle at 26\u0026ordm;C, 75% humidity. And the expression of p3 has been tested before (Wu et al., 2014).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRNA sequencing\u003c/h3\u003e\n\u003cp\u003eOs_CK and Os_p3 plants at the 3th leaf stage were used for transcriptome sequencing. Total RNA was extracted from six different plants (Os_CK 1, Os_CK 2, Os_CK 3, Os_p3-1, Os_p3-2 and Os_p3-3) using TRIzol reagent according to the manufacturer\u0026rsquo;s instructions (Invitrogen, Carlsbad, CA, USA). RNA was qualified using agarose gel electrophoresis and NanoDrop assay. These six RNA samples were sent to the Novogene Bioinformatics Technology Company (Beijing, China) for transcriptome sequencing using Illumina HiSeq 2500 platform.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRaw data processing and\u003c/b\u003e \u003cb\u003ede novo\u003c/b\u003e \u003cb\u003eassembly\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRaw data (raw reads) of fastq format were firstly processed through in-house perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N and low quality reads from raw data. Meanwhile, Q20, Q30, GC-content and sequence duplication level of clean data were calculated. All the downstream analyses were based on the clean data with high quality (Li et al. 2018). \u003cem\u003eDe novo\u003c/em\u003e assembly of clean reads was carried out using Trinity (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://trinityrnaseq.sourceforge.net/\u003c/span\u003e\u003cspan address=\"http://trinityrnaseq.sourceforge.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In brief, clean reads with a certain overlap length were initially combined to form long fragments without N. These fragments are called contigs. Related contigs were clustered using the TGICL software to yield unigenes (without N) that cannot be extended on either end, and redundancies were removed to acquire non-redundant unigenes (Wu et al. 2014).\u003c/p\u003e\n\u003ch3\u003eReads mapping to the reference genome\u003c/h3\u003e\n\u003cp\u003eThe draft sequence of the \u003cem\u003eOryza sativa\u003c/em\u003e L. reference genome have been downloaded from the SGN ftp site (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.solgenomics.net/genomes/Oryza_sativa/assembly\u003c/span\u003e\u003cspan address=\"http://ftp://ftp.solgenomics.net/genomes/Oryza_sativa/assembly\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) directly. Index of the reference genome was built using Bowtie v2.2.3 (Broad Institute, Cambridge, MA, USA) and single-end clean reads were aligned to the reference genome using TopHat v2.0.12 (Broad Institute, Cambridge, MA, USA) (mismatch\u0026thinsp;=\u0026thinsp;2) (Li et al. 2018).\u003c/p\u003e\n\u003ch3\u003eAnalyses of transcriptome data\u003c/h3\u003e\n\u003cp\u003eDEGs (differentially expressed genes) were identified by a P-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and an expression change of 2-fold or more (|log2Foldchange| \u0026ge; 1) between the two treatments using IDEG6 software. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) classification were used to determine the main biological functions and pathways related to DEGs. GO was implemented using the GOseq R package, in which gene length bias was corrected, and GO terms with corrected P-values\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered significantly enriched in DEGs. GO annotation was performed using the REVIGO web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://revigo.irb.hr/\u003c/span\u003e\u003cspan address=\"http://revigo.irb.hr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The KEGG classification of the DEGs was performed using the KEGG Mapper Annotate Sequence tool with the BlastKOALA server available on the Kyoto Encyclopedia of Genes and Genomes website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp:www.kegg.jp/keg/tool/annotate_sequence.html\u003c/span\u003e\u003cspan address=\"http:www.kegg.jp/keg/tool/annotate_sequence.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). KOBAS software was used to test the statistical enrichment of DEGs in KEGG pathways.\u003c/p\u003e\n\u003ch3\u003eReal-time quantitative PCR (RT-qPCR) validation\u003c/h3\u003e\n\u003cp\u003e Total RNA was extracted with TRIzol reagent from Os_CK and Os_p3 plants at the 3th leaf stage according to the manufacturer\u0026rsquo;s instructions (Invitrogen, Carlsbad, CA, USA). These six plants were different from those plants for RNA sequencing, but all of samples were collected at the same treatment and time for both RNA extractions, 24 days after sowing. 1 \u0026micro;g of total RNA was used to synthesize cDNA by the PrimeScript RT Reagent Kit (TAKARA Bio, RR037A). RT-qPCR was performed using the SYBR Green Real-time PCR Master Mix (ToYoBo, JAPAN) with the iQ5 Real-Time PCR system (Bio-Rad, USA) with gene-specific primers in the Additional file Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, each reaction containing 10.0 \u0026micro;L of SYBR Green Real-time PCR Master Mix, 1.0 \u0026micro;L of cDNA, 0.5 \u0026micro;L of (10 pM) primers, and 8.0 \u0026micro;L of water. The expression levels of transcripts are presented relative to the corresponding control samples for each condition. The \u003cem\u003eOsActin\u003c/em\u003e gene was used as an internal control gene. All RT-qPCR experiments were performed in triplicate. The data were calculated by the 2\u003csup\u003e-ΔΔCT\u003c/sup\u003e method using the Excel soft.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eRNA Sequencing Reveals Predominant Downregulation of Differentially Expressed Genes in RSV p3-Expressing Rice Seedlings\u003c/h2\u003e\u003cp\u003eTo illustrate the transcriptional responses of the rice to RSV p3, RNA from six plants that included three samples of Os_CK and three samples of Os_p3 was used to construct six cDNA libraries for RNA sequencing. After trimming of adaptor sequences and removal of low-quality reads, clean reads were obtained from six libraries of Os_CK and Os_p3 plants. The results showed that the clean bases data of these six samples were among 8.29 GB and 10.79 GB. There were 62,350,654\u0026ndash;71,917,236, 55,255,420\u0026ndash;63,731,312 clean reads in the Os_CK and Os_p3 samples, respectively. And Q20 and Q30 was used to analysis the quality RNA sequencing. The results showed that Q20 (the percentage of base content with a base calling error less than 1%) and Q30 (the percentage of base content with a base calling error less than 0.1%) values are ranged from 95.31 to 95.51% and 88.61 to 89.03%, respectively. So, over 88% of the obtained base content had a base calling error probability less than 0.1% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Altogether, these RNA sequencing results were sufficient for subsequent gene expression analysis. First, the expression level of genes was normalized as clean reads per kilobase of exon model per million mapped reads (RPKM). When we set the RPKM\u0026thinsp;\u0026gt;\u0026thinsp;1, the expressed genes number were 19,132 and 19,620 in the Os_CK and Os_p3 samples, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Among these genes, there were 451 unique expressed genes in the Os_CK plants and 939 unique expressed genes in the Os_p3 plants.\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\u003eList of quality of data output\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRaw reads\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClean reads\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClean bases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eError rate (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ20 (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eQ30 (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGC content (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOs_CK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e73,797,144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71,917,236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.79GB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e95.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e89.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e56.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOs_CK2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64,031,046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62,350,654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.35GB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e95.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e88.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e57.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOs_CK3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64,938,042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63,450,002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.52GB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e95.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e88.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e58.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOs_p3-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65,210,035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63,731,312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.56GB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e95.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e88.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e56.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOs_p3-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64,278,212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62,716,270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.41GB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e95.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e88.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e56.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOs_p3-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56,673,022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55,255,420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.29GB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e95.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e88.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e57.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eSample name: the name of the sample. Raw reads: statistics of raw sequence data. Clean read: the sum of reads, after removing low quality reads. Clean bases: the sum of bases from clean reads. Error rate (%): sequencing Phred score [Qphred = -10log10(e)]. Phred score from the Base Calling. Q20 (%), the percentage of base content with a base calling error less than 1%. Q30 (%), the percentage of base content with a base calling error less than 1%. GC content: the percentage of bases that are G and C.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe differentially expressed genes (DEGs) were determined by comparing the expression level of genes in p3 transgenic rice plants with those from wild-type rice with the stringent criteria, since DESeq has eliminated biological variation, our screening criteria for differential genes are generally, padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05. DEGs were identified by a P-value\u0026thinsp;\u0026le;\u0026thinsp;0.05, 533 DEGs were found in response to p3 at the seedling period in these six transcriptome profiles, including 214 upregulated genes and 319 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Additional file Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). These results indicate that the majority genes differentially expressed in response to p3 were downregulated compared with wild-type rice.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGO and KEGG Enrichment Reveals Photosynthesis and Plastid-Related Processes are Predominant among RSV p3-Responsive DEGs in Rice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further characterize the specific biological processes these DEGs may be involved with, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) classification were used. All 533 DEGs were annotated and the most of the top 10 GO terms of biological process, cellular component, molecular function three categories significantly enriched respectively were listed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The three most highly represented GO terms under the biological process category were \u0026ldquo;metabolic process,\u0026rdquo; \u0026ldquo;cellular process\u0026rdquo;, and \u0026ldquo;single-organism process\u0026rdquo;, each of them account for more than 40 percent of DEGs, respectively. The most abundant GO terms in the cellular component category were \u0026ldquo;cell\u0026rdquo; \u0026ldquo;cell part\u0026rdquo;. The two most abundant terms in the molecular function category were \u0026ldquo;binding\u0026rdquo; and \u0026ldquo;catalytic activity\u0026rdquo;. They also account for more than 40 percent of DEGs. So we found that the enriched GO terms were mainly classified as biological processes, the most of the top 5 GO terms of biological processes by ranking p-value from Fisher\u0026rsquo;s Exact Test is plastid organization, single-organism biosynthetic process, photosynthesis, chloroplast organization and photosystem II assembly (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntegrated KEGG Enrichment and Gene Expression Validation Reveal RSV p3-Mediated Modulation of Metabolic and Defense Pathways in Rice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe KEGG classification of the DEGs was performed using the KEGG Mapper Annotate Sequence tool. The top 20 of KEGG pathways of the more or main significantly enriched are Ubiquinone and other terpenoid-quinone biosynthesis, Carbon fixation in photosynthetic organisms, Metabolic pathways, Biosynthesis of secondary metabolites, Vitamin B6 metabolism, Fatty acid metabolism, Terpenoid backbone biosynthesis, Nitrogen metabolism, Carbon metabolism, Pyruvate metabolism, Biosynthesis of unsaturated fatty acids, Phenylalanine metabolism, Glyoxylate and dicarboxylate metabolism, Fatty acid biosynthesis, Biotin metabolism, Alanine, aspartate and glutamate metabolism, Butanoate metabolism, Photosynthesis and Glycerolipid metabolism, Starch and sucrose metabolism in the KEGG pathway analysis about 533 DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In order to explore the differentially expressed genes of the transgenic p3 rice, 939 unique expressed genes in the transgenic rice was analyzed by KEGG pathway analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), the result showed that RSV p3 affects plant-pathogen interaction, brassinosteroid biosynthesis and phosphatidylinositol signaling system and so on.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo validate expression pattern of genes from RNA sequence analysis, eight genes were selected from those with significant differential expression for RT-qPCR analysis, and \u003cem\u003eOsActin\u003c/em\u003e was used as the internal reference gene. The results showed that all these eight genes have different expression in the p3 transgenic rice, compared to that in the Os_CKs, including seven up-regulates genes and one down-regulated gene (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with the results of RNA sequencing, RT-qPCR showed that seven genes have the same expression trend, except Proteinase inhibitor I12 (OS01G0124200). The results revealed that the RT-qPCR data were consistent with the transcriptome results.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVerification of RNA sequence analysis results using RT-qPCR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription(s)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRNA-seq Fold\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRT-qPCR\u003c/p\u003e\u003cp\u003eFold\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVAR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOS11G0227100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeucine-rich repeat||NB-ARC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOS11G0492300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeucine-rich repeat||NB-ARC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOS11G0461000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eserine carboxypeptidase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOS04G0249600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ethiosulfate sulfurtransferase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOS03G0210200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProteinase inhibitor I25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOS01G0124200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProteinase inhibitor I12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOS01G0165800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF-box protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOS08G0193600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF-box protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eA selection of upregulated or downregulated genes from rice samples were tested. The \u003cem\u003eOsActin\u003c/em\u003e gene was used as an internal control. Fold change is indicated as a ratio of Os_p3 vs Os_CK calculated from normalized median intensity values (n\u0026thinsp;=\u0026thinsp;3). VAR value represents the standard deviation of the qRT-PCR signals (n\u0026thinsp;=\u0026thinsp;3). Pval, the p-value of t-test, indicate statistically significant differences compared with Os_CK.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eVSRs affect the expression of host genes in response to biotic or abiotic stress (Jada et al. 2013; Soitamo et al. 2011; Westwood et al. 2013). For example, the expression of PVX-p25 in transgenic tobacco plants caused 1,350 up-regulated transcripts and 5 down-regulated transcripts in the leaves (Jada et al. 2013). Based on comparative transcriptomic analysis of SA treatment (promoting) and ABT treatment (inhibiting SA synthesis), 1,759 differentially expressed genes with opposite regulation were identified in potatoes (Chen et al. 2025). The expression of 748 and 332 genes was significantly changed in the leaves and flowers, respectively, in HC-Pro transgenic plants (Soitamo et al. 2011). CMV-infection 2b-dependent induced genes were enriched in plant immunity pathways, including salicylic acid (SA) signaling pathway (Zhao et al. 2018). When \u003cem\u003eRhizoctonia solani\u003c/em\u003e infects resistant and susceptible rice cultivars, the plant-pathogen interaction pathway was significantly affected by \u003cem\u003eR. solani\u003c/em\u003e infection on resistant cultivars (Shi et al. 2020). In our previous study, we found that RSV p3 induced \u003cem\u003eN.benthamiana\u003c/em\u003e resistance by affecting host gene expression (Wu et al. 2018), the DEGs associated with ribosomes, photosynthesis, and carbon metabolism in \u003cem\u003eN. benthamiana\u003c/em\u003e, and most highly represented GO terms were \u0026ldquo;metabolic process,\u0026rdquo; \u0026ldquo;cellular process\u0026rdquo;, \u0026ldquo;single-organism process\u0026rdquo;, \u0026ldquo;cell part\u0026rdquo;, \u0026ldquo;cell\u0026rdquo;, \u0026ldquo;catalytic activity\u0026rdquo; and \u0026ldquo;binding\u0026rdquo;. We also investigated the influence of p3 on gene expression using RNA sequencing analysis and RT-qPCR verification. The results showed that RSV p3 expression was associated with 533 DEGs in rice. GO annotation and KEGG pathway enrichment analysis revealed that p3 affects plant-pathogen interaction, brassinosteroid biosynthesis and phosphatidylinositol signaling system. And the most abundant GO terms were also classified into \u0026ldquo;metabolic process,\u0026rdquo; \u0026ldquo;cellular process\u0026rdquo;, \u0026ldquo;single-organism process\u0026rdquo;, \u0026ldquo;cell part\u0026rdquo;, \u0026ldquo;cell\u0026rdquo;, \u0026ldquo;catalytic activity\u0026rdquo; and \u0026ldquo;binding\u0026rdquo;. Furthermore, our results of KEGG of DEGs from transgenic p3 rice were consistent with the resistance of transgenic p3 rice independent of the SA and JA pathway (Wu et al. 2014). Compare with its function in \u003cem\u003eN. benthamiana\u003c/em\u003e, RSV p3 also affects plant-pathogen interaction, and GO.\u003c/p\u003e\u003cp\u003eNucleotide-binding site-leucine-rich repeat (NBS-LRR) proteins play crucial roles in plant development and stress responses, and some of them are induced to against bacterial and viral infection(Santos et al. 2010; Zipfel et al. 2006). Protease inhibitors (PIs) are derived from R genes, belong to the PR-6 family of pathogenesis-related proteins, affect plant growth and metabolism by combining and regulating proteases in insects and the host. By binding proteinases and modulating proteinase activity in numerous biochemical processes, PIs serve diverse functions in development and metabolism. (Grudkowska \u0026amp; Zagdańska. 2004; Sawano et al. 2008). F-box proteins components of SKP1/CUL1/F-box (SCF) complexes responsible for recognizing target substrates, and SCF complexes regulate ethylene (ET) signal transduction at multiple points, such as defence against pathogens infection(Jia et al. 2016). Bowman-Birk-type protease inhibitors (BBTIs) induced by JA and corn borer in corn exhibit significant anti-feeding activity against corn borers (Chen et al.2024). Potato type I protease inhibitor (PI) in tobacco interacts with P25 encoded by potato virus X (PVX) and targets P25 for degradation through autophagy and ubiquitination, thereby reducing PVX infection (Shen et al.2025). In this study, we found that the expression of NBS-LRR, F-box and PIs genes was induced in p3 transgenic rice using RNA sequence analysis and RT-qPCR detection.\u003c/p\u003e\u003cp\u003eBrassinosteroids (BRs) are plant growth\u0026ndash;promoting natural products required for plant growth and development, BR signaling is essential for the development of intercalary meristems(Sakamoto et al. 2006; Yamamuro et al. 2000). Brassinosteroid (BR)-regulated GhBEE3-like genes can increase the expression of stress-related genes, thereby enhancing the drought tolerance of plants (Chen et al.2025). However, When \u003cem\u003eN. benthamiana\u003c/em\u003e infected by tobacco curly shoot virus (TbCSV), brassinosteroid synthesis-related gene, 3-epi-6-deoxocathasterone 23-monooxygenase (CYP90C1/D1) was significantly decreased upon virus infection (Li et al. 2018). In this study, Gene involve in brassinosteroid biosynthesis were uniquely expressed (OS03G0602300) or down-regulated (Os07g0162100) in OS_p3 plants, it was speculated that brassinosteroid may help the RSV p3 protein to maintain rice growth and development. The DGEs related plant-pathogen interaction and brassinosteroid biosynthesis were showed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The resistance (R) genes in plants confer specificity to the innate immune system. Most R genes have a centrally located NB-ARC (nucleotide-binding adaptor shared by APAF-1, R proteins, and CED-4) domain (Tameling et al. 2006). NB-ARCs (OS11G0492300, OS06G0268500) in this study were up-regulated DEGs, p3 protein in rice may regulate the expression NB-ARC to enhance host resistance. Plant-pathogen interaction includes complex process that PAMP-triggered immunity (PTI) and effector-triggered immunity (ETI), genes with kinase (OS10G0539600, OS03G0331700, Os05g0498900, Os02g0786900) were changed in Os_p3.\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\u003eDEGs of plant-pathogen interaction and brassinosteroid biosynthesis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathway way\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnigene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGene name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003elog\u003csub\u003e2\u003c/sub\u003eFC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"12\" rowspan=\"13\"\u003e\u003cp\u003eplant-pathogen interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS11G0492300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNB-ARC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.4064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS06G0268500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNB-ARC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.4510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS11G0227100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNB-ARC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.2155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS03G0210200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePIs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS01G0124200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePIs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.6562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS01G0165800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF-box proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.2345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS08G0193600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF-box proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS10G0539600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSPK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.7462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS03G0331700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCML27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.6462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOs05g0498900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c4\"\u003e\u003cp\u003e0.1212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS04G0339800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOSJNBa0004L11.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.3525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ebrassinosteroid biosynthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS03G0602300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCYP85A1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOs07g0162100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP0529H11.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.0161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eDEGs were Pvalue\u0026lt;0.05\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eFC Fold change\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOur findings suggest that p3 regulates the expression of genes involved in plant-pathogen interactions and brassinosteroid production in rice. We believe that p3 acts as a host defense inducer, inhibiting pathogenic development by promoting the expression of genes like NBS-LRR and protease inhibitors genes. The plant-pathogen interaction and brassinosteroid production route is complex; nevertheless, early research has identified the two pathways, layed the groundwork for future functional studies of these candidate genes and their exact placement in the process.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eWe are grateful to Fei Yan of the Ningbo University, Ningbo, China, for kindly providing the rice samples and p3 transgenic rice plants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This research was supported financially by the National Natural Science Foundation of China (31601607), and the Fundamental Research Funds for the Central Universities (Grant No. SWU-XDJH202318).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003eGentu Wu and Binhao Gao performed the major experiments. Binhao Gao and Long Ma developed the research program and wrote manuscripts. Huiyuan Zhang, Sinan Chen and Qiao Wang participated in carrier construction, data processing and analysis. Ling Qing and Gentu Wu conceived the study and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman and animal rights\u003c/strong\u003e No human and/or animal participants were present in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e All authors of this study consent to this submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarbier P, Takahashi M, Nakamura I, Toriyama S, Ishihama A (1992) Solubilization and promoter analysis of RNA polymerase from rice stripe virus. 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Cell 125(4) : 749-760.\u003c/li\u003e\n\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-plant-diseases-and-protection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jpdp","sideBox":"Learn more about [Journal of Plant Diseases and Protection](https://www.springer.com/journal/41348)","snPcode":"41348","submissionUrl":"https://www.editorialmanager.com/jpdp","title":"Journal of Plant Diseases and Protection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Rice stripe virus, p3 protein, Transgenic rice, Transcriptome analysis","lastPublishedDoi":"10.21203/rs.3.rs-7359704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7359704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe p3 protein, is a viral suppressor of RNA silencing (VSR), encoded by the viral strand of RNA3 in the rice stripe virus (RSV). As a VSR, p3 facilitates viral infection, but its other function in the host plant is poorly understood. This study analyzed the differentially expressed genes (DEGs) of transgenic \u003cem\u003ep3\u003c/em\u003e gene rice using transcriptome sequencing and quantitative real-time PCR (RT-qPCR). The transcriptome result showed 533 DEGs between transgenic p3 rice and wild-type rice, which included 214 upregulated genes and 319 downregulated genes. Among them, NB-ARC (OS11G0492300, OS06G0268500, OS11G0227100) in this study were up-regulated. Expression of kinase genes (OS10G0539600, OS03G0331700, Os05g0498900, Os02g0786900, OS03G0758250, OS04G0339800) were altered in transgenic plants, with the majority of them being down-regulated and brassinosteroid biosynthesis gene were uniquely expressed (OS03G0602300) or down-regulated (Os07g0162100) in OS_p3 plants. Because the system involving plant-pathogen interaction and brassinosteroid production is intricate, the preliminary research identified two pathways in order to set the groundwork for future research via functional analysis of candidate genes and particular route locations. The study findings enhance understanding of the interactions between VSRs and host plants.\u003c/p\u003e","manuscriptTitle":"Transcriptome analysis of transgenic rice responds to rice stripe virus p3 expression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 08:56:49","doi":"10.21203/rs.3.rs-7359704/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-09-10T02:57:43+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-08T23:18:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Journal of Plant Diseases and Protection","date":"2025-08-14T08:22:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T12:18:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Plant Diseases and Protection","date":"2025-08-12T21:21:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-plant-diseases-and-protection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jpdp","sideBox":"Learn more about [Journal of Plant Diseases and Protection](https://www.springer.com/journal/41348)","snPcode":"41348","submissionUrl":"https://www.editorialmanager.com/jpdp","title":"Journal of Plant Diseases and Protection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d08e4f80-8e38-4197-8fb7-9eebb0772680","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:06:27+00:00","versionOfRecord":{"articleIdentity":"rs-7359704","link":"https://doi.org/10.1007/s41348-026-01232-3","journal":{"identity":"journal-of-plant-diseases-and-protection","isVorOnly":false,"title":"Journal of Plant Diseases and Protection"},"publishedOn":"2026-02-27 15:58:38","publishedOnDateReadable":"February 27th, 2026"},"versionCreatedAt":"2025-09-16 08:56:49","video":"","vorDoi":"10.1007/s41348-026-01232-3","vorDoiUrl":"https://doi.org/10.1007/s41348-026-01232-3","workflowStages":[]},"version":"v1","identity":"rs-7359704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7359704","identity":"rs-7359704","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}