Exploring the Potential of Oryza sativa derived Candidate miRNAs to Target Rice Tungro Bacilliform Virus (RTBV) Genome

Exploring the Potential of Oryza sativa derived Candidate miRNAs to Target Rice Tungro Bacilliform Virus (RTBV) Genome | 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 Exploring the Potential of Oryza sativa derived Candidate miRNAs to Target Rice Tungro Bacilliform Virus (RTBV) Genome Mudassar Fareed Awan, Tauheed Suddal, Rozina Bibi, Muhammad Shahzad Iqbal, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4422179/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Rice tungro disease (RTD) poses a serious threat to global rice cultivation, primarily afflicted by Rice tungro bacilliform virus (RTBV) and Rice tungro spherical virus (RTSV) strains. This challenge demands innovative approaches presenting a computational procedure to identify potential candidate miRNAs from Oryza sativa based mirBase repository that can predict target sites in RTBV and RTSV genome. Results: Through our insilico based analysis utilizing different target prediction algorithms, five potential rice derived miRNAs were screened with maximum potential to target the RTBV genome. The candidate miRNAs include osa-miR166a-5p, osa-miR156g-3p, osa-miR413, osa-miR426, and osa-miR160a-5p. Additionally, seven miRNAs were predicted to explore their potential to target the RTSV genome i.e., osa-miR530-3p, osa-miR414, osa-miR390-5p, osa-miR156h-3p, osa-miR164b, osa-miR166c-3p, and osa-miR160a-5p. These miRNAs underwent effective evaluation, including free energy estimation and secondary structures were determined to ensure their efficacy in genome silencing. Moreover, site conservation analysis revealed conserved domains inside target sites. The screened miRNAs underscoring their immense potential to trigger robust mRNA intervention in viral genome. Furthermore, phylogenetic trees were also constructed to interpret the evolutionary relationships among closely related species providing valuable insights into the evolutionary standing of RTBV and RTSV. This study presents a holistic computational framework exploring potential candidate miRNAs derived from Oryza sativa with good capability to target RTBV and RTSV genomes. Conclusion: These findings hold considerable promise for the development of RNA-based strategies aimed at mitigating the impact of rice tungro disease, thereby contributing to sustainable rice production and global food security. miRNA mRNA RTBV RTSV Computational tools Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1. INTRODUCTION Rice ( Oryza sativa ) belongs to family Poaceae and is a highly nutritious cereal grain crop that sustains a significant numbers of world’s population. This grain crop is particularly prevalent in regions where food security is highly dependent upon economic stability 1 . Rice is one of the largest crop accounting for 1.3% of the country’s total GDP 2 . Pakistan is considered not only the 11th major producer but also the 4th largest rice exporter globally. The rice production accounts for 3.2% of value-added in agriculture 3 . However, the productivity and yield potential of rice are constantly threatened by a myriad of multiple biotic stresses especially viruses that can induce havoc on agricultural production 4 . The Rice Tungro Bacilliform Virus (RTBV in association with Rice Tungro Spherical Virus (RTSV) causes a deadly rice disease which is designated as Rice Tungro Disease (RTD). This disease remains a gigantic threat to rice productivity all over the world, particularly in Asia. The spreading of RTD results from the complex interaction happened between Rice Tungro Bacilliform Virus (RTBV) and Rice Tungro Spherical Virus (RTSV) 6 . The RTBV belongs to para-retrovirus with double-stranded DNA genome causing this viral infection. On the other hand, the RTSV is an RNA virus which only exhibited disease symptoms after interacting with RTBV 7 . The transmission of RTBV occurs through green leafhopper ( Nephotettix virescens) which acts as a vector, gets accumulated in the nucleus of phloem cells in infected rice 8 ; 9 . The two viruses work in an intricate mutual relationship having RTBV encoding suppression activity for handling localized silencing activity of the host, while RTSV components aid in suppression of viral propagation through cell-to-cell facilitating the spread of viral infection 10 . The interaction between these two viruses within single host plant is very complex leading to further complicating RTD progression and obstruct its management 11 . The virus is equally devastating as it infects both the indica and japonica subspecies of rice widely cultivated across Asia. Additionally, the complicated interaction between RTBV & RTSV and their vector makes the control difficult through conventional methods 12 . Among diverse rice afflicting pathogens, Rice Tungro Bacilliform Virus (RTBV) emerged as a formidable adversary, causing discoloration, stunted growth, less number of tillers, sterile and partly filling grains debilitating tungro disease that poses a serious challenge to rice production worldwide, causing substantial economic losses 5 . The genetic manipulation is so far considered a promising approach against RTD which help in creating virus resistant rice lines. But the acceptability of GMOs food crop is still the biggest challenge in most of the world. In order to avoid any controversy and to enhance rice resistance against RTBV, gene silencing may be a procedure which involve no genetic manipulation or introduction of any foreign protein expression in rice. Recent studies have screened multiple potential miRNAs derived from Oryza sativa possessing immense potential for silencing RTBV. The RTD can be controlled by the most promising approach gene silencing which identified particular target positions in viral genomic regions for miRNA 13 . The miRNAs are small non-coding RNAs regulating gene expression through binding to complementary sequences in target mRNAs causing degradation or translational repression. Recent years in scientific eras, the miRNAs for RNAi-mediated gene silencing has gained considerable attention as a potent tool for combating viral infections in plants 14 . The present research work was aimed to predict potential candidate miRNAs derived from Oryza sativa and helped in discovering locations in mRNA employing computational algorithms and bioinformatics tools that can effectively interfere with viral replication and mitigate the possibility of Tungro disease infection in rice crop 15 . Previous studies have worked in identifying specific rice-derived miRNAs that could target RTBV at certain positions. Computational analyses have been employed to predict candidate miRNAs in rice like osa-miR5510 and osa-miR3980a-3p showed efficacy against RTBV and potentially prevented the expression of crucial viral proteins 16 . The RTBV has also evolved procedure to suppress miRNA functions leading to create complications in RTBV resistance mechanism. The RTBV protein P4 has been shown to alter the siRNA profiles within infected cells, modulating the plant's innate defense responses and enhancing viral survival and replication 17 . Advancements in computational biology and employments of latest bioinformatics tools, have allowed for the in silico prediction of miRNA targets within the RTBV genome, offering insights into potential resistance mechanisms. These research methodologies provide a blueprint for genetic engineers make strategies for enhancing rice resistance against tungro disease 18 . The investigation of miRNA-based techniques for combating RTD is a significant development in the fields of plant virology and agricultural biotechnology. By utilizing the regulatory power of Oryza sativa derived miRNAs specifically target and silence viral genetic sequences, the present research work focused on employing multiple bioinformatics tools to determine potential target sites in RTBV genome through identified miRNAs. These tools work on the basis of multi-dimensional parameters and explored potential target sites in RTBV genome which interact with rice miRNAs by releasing free energies and folding energy. The study also identified those miRNAs appeared and suggested by all four computational algorithms. Such candidate miRNAs are probably the most effective promising miRNAs against RTBV. This research aims to predict candidate rice derived miRNAs exploring targets in RTBV genome to make solid interaction with viral mRNA for degradation of RTBV. Such novel developments will cause employing breakthroughs in rice pathology and agricultural practices. 2. MATERIALS & METHODS 2.1 Acquisition of Oryza sativa miRNAs from miRBase Repository The 50 mature Oryza sativa miRNAs were extracted from miRBase, a comprehensive database of miRNA sequence annotations. The latest version of miRBase was employed to retrieve a curated collection of known miRNAs specific to Oryza sativa (https://www.mirbase.org/browse/). 2.2 Retrieval of RTBV & RTSV Genomes from NCBI The complete genome sequences, RTBV and RTSV were retrieved from the National Center for Biotechnology Information (NCBI) database. Accession numbers for RTBV (>HM149532.1) and RTSV (>U70989.1) genomes were obtained for subsequent analysis.(https://www.ncbi.nlm.nih.gov/) 2.3 Analysis of Open Reading Frames of RTBV & RTSV by CLC Genomics The CLC Genomics Workbench software (version X) was employed to analyze open reading frames (ORFs) present within RTBV and RTSV genomic sequences. The ORFs were identified and annotated on the basis of their sequence homology and codon usage. It provides wonderful insights into potential protein-coding regions exist within the viral genomes. 2.4 Utilization of miRNA Target Prediction Tools A variety of bioinformatics tools were employed for miRNA target prediction, details are presented in figure 1 & table 1 below. Various bioinformatics tools working on different parameters were employed to predict target sites in RTBV and RTSV genomes through multiple miRNAs. The various parameters and their details are mentioned in the table 1 and figure 1. Each computational tool worked in differential parameter to predict potential target sites in viruses. Table 1. The list of various computational miRNA target prediction algorithms with respective parameters employed in the present study. Tools Parameters Source psRNATarget (Expectation = 8, penalty for opening gap = 2, penalty for extending gap = 0.5, penalty for G.U pair = 1, seed region = 2–7 nt and HPS size = 19 https://www.zhaolab.org/psRNATarget/ RNA22 Maximum folding energy = −12 kcal/mol, Minimum number of paired-up bases = 12 https://cm.jefferson.edu/rna22/Interactive/ miRanda Alignment Score Threshold = 140, Energy Threshold = -20kcal/mol http://www.microrna.org/microrna/getDownloads.do RNA hybrid Energy Threshold = -20kcal/mol https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid 2.5 The Target Prediction of RTBV Genome through psRNATarget Algorithm The psRNATarget is a computational tool designed for the prediction of small mRNA targets against pathogens. It was utilized to identify potential miRNA binding sites within the RTBV and RTSV genomes. The algorithm employs a schema-based approach to identify complementary patterns between small RNAs and target transcripts, providing a list of putative miRNA target matches https://www.zhaolab.org/psRNATarget/ . 2.6 RNA22 Algorithm The RNA22 algorithm, known for its pattern-based approach to miRNA target prediction, was employed to predict miRNA binding sites within the RTBV and RTSV genomes. RNA22 identifies potential targets based on folding energy and pattern recognition, allowing for the prediction of non-canonical targets and targets beyond the 3 untranslated region (3-UTR) https://cm.jefferson.edu/rna22/Interactive/ . 2.7 The Target Prediction sites in Viral Genomes through miRanda The miRanda is considered a widely employed miRNA target prediction tool, utilized to identify putative miRNA binding sites within the RTBV and RTSV genomes. The algorithm employs sequence complementarity and thermodynamic stability criteria to predict miRNA-target interactions, providing valuable insights into potential regulatory mechanisms (http://www.microrna.org/microrna/getDownloads.do). 2.8 Target Prediction through RNAHybrid in RTBV and RTSV Genomes The RNAHybrid, a tool is employed to predict the hybridization of a short miRNA sequence with targeted mRNA sequence. It was also employed for miRNA target prediction in the context of RTBV and RTSV genomes. These algorithms calculate minimum hybridization capability on the basis of free energy, facilitating the identification of potential miRNA binding sites (https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid). 2.9 Structural Annotation of Potential miRNA-mRNA Interactions The RNAfold and RNAcofold are bioinformatics tools manifested for to represent miRNAs interactions with viral mRNAs. These tools help not only predicting secondary structure, and folding energy estimations. The stability of miRNAs-mRNA interactions was also ascertained through these tools help making RNA hetero-duplexes. These heteroduplexes formed between potential consensus miRNAs and their target regions in RTBV and RTSV through http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAcofold.cgi. 2.10 The Visualization of miRNAs Data in Circos Plots through R Studio The statistical analysis of miRNA target prediction data was evaluated and represented by graphs from RStudio which is an integrated development environment for the R programming language. The tabulated data obtained from results using four different computational tools were graphically represented as scatter plots and intersection plots by through R. 2.11 Target-site Conservation Analysis in RTBV and RTSV Genomic Sequences The conservation analysis of miRNA target sites within RTBV and RTSV genomes was conducted to assess their evolutionary lineages and conservation of miRNA-target interactions across different virants. Multipl Sequence Alignment (MSA) was executed through MUSCLE with default settings in Jalview (http://www.jalview.org/), to study the target-site conservation analysis. 2.12 Construction of Phylogenetic Trees The phylogenetic analysis of RTBV and RTSV isolates was manifested to determine their evolutionary relationships and genetic diversity was also explored. Multiple Sequence Alignments (MSA) of viral genomes were executed by employing CLC Genomics Workbench, followed by the construction of phylogenetic trees through distance-based methods such as neighbor-joining with bootstrap value 100. 3. RESULTS 3.1 Sequence Annotation & ORF Prediction of RTBV & RTSV In this section, we analyzed the complete genome sequences of RTBV and RTSV using CLC Genomics Workbench. The identification and annotation of open reading frames (ORFs) within the viral genomes were conducted to gain insights into potential protein-coding regions. Additionally, sequence characteristics and structural features of RTBV and RTSV genomes were examined to provide a comprehensive understanding of viral genome organization and composition in Fig. 2 . The Table 2 and Table 3 indicates the details of ORF and their location of both genomes in more details. The circular view of both viral genomes was also shown in Fig. 3 . Table 2 Representing RTBV Genome contains a specific number of ORFs, each with a defined location, starting codon, length, and the corresponding number of nucleotides. No. of ORFs Start End Length (nt| aa) Strand Start Codon ORF1 1 1164 1164 | 388 Positive ATG ORF2 2 124 123 | 41 Positive ATG ORF3 155 244 90 | 30 Positive ATG Table 3 Representation of the the number of ORFs, their location, starting codons, lengths and no. of their corresponding nucleotides in RTSV Genome. No. of ORFs Start End Length (nt | aa) Strand Start Codon ORF1 1 2109 2109 | 703 Positive ATG ORF2 2 208 207 | 69 Positive ATG ORF3 515 570 55 | 18 Positive ATG ORF4 1853 2107 254 | 84 Positive ATG 3.2. The miRNA Target-site Prediction by Four Algorithms In this study, we employed four different bioinformatics tools for miRNA target prediction using miRanda, psRNATarget, RNA22, and RNAhybrid. These tools are based on different parameters to identify potential miRNA binding sites within the genomes of RTBV and RTSV. The miRanda employed sequence complementarity and thermodynamic stability criteria, while psRNATarget used a schema-based approach. The highly precise RNA22 tool identified targets based on folding energy and pattern recognition while RNAhybrid predicts hybridization between short and long RNA sequences. By integrating results from these tools, we aimed to analyze miRNA-target interactions in viral genomes in highly comprehensively manner. 3.3 Identification of miRNA target by psRNATarget Algorithm The psRNATarget algorithm is used to identify possible targets of miRNA by searching for complementary sequences between miRNA and target transcripts. The colored dots in the Fig. 4 below represents the target sites predicted by psRNATarget on the genome of RTBV and RTSV. The figure indicates that the criteria in psRNATarget is very low and lenient indicating highest possibilities of miRNA location in various viral genomic regions. The expectation rate in RTSV is much lower as compared to RTBV genome sequence as Fig. 4 showed. 3.4 Identification of miRNA target by RNA22 The RNA22 algorithm predicts potential miRNA targets in the RTBV & RTSV genomes based on sequence complementarity and structural accessibility. This computational tool worked by estimating folding energy (kcal/mol) according to various genomic positions. Seven purple colored dots representing miRNAs with highest folding energies are targeted before 300bp. Five miRNAs target 600bp-800bp while only four miRNAs comes after 1000bp location. The Fig. 5 below shows the potential target sites predicted by RNA22. Similarly, the lowest numbers of miRNAs are placed after 2000bps in RTSV genome. 3.5 Identification of miRNA target by miRanda Algorithm The miRanda algorithm reliably identified candidate miRNA targets by assessing sequence complementarity and thermodynamic stability based on folding energy (kcal/mol). The miRanda explored that only single miRNA targeted between 100-200bps while three miRNAs targeted between 800-1000bp in RTBV. Likewise, miRanda tool indicates one miRNA target at nearly 250-260bp position while two miRNAs make interaction with 1800-2000bps location in RTSV. As shown in Fig. 6 below, the miRanda algorithm has accurately predicted the potential target on the genome of RTBV& RTSV. 3.6 Identification of miRNA targets through RNAHybrid Algorithm The RNAHybrid algorithm effectively identifies potential miRNA targets by assessing the hybridization between miRNAs and target mRNAs. The Fig. 7 below conclusively displays the miRNAs that RNAHybrid has predicted and their respective target sites on the genome of RTBV and RTSV. 3.7 Graphical representation of Consensus of predicted miRNAs The comprehensive graphical representation by R studio is constructed to showcase the consensus of predicted miRNAs. This visualization interpreted data from diverse prediction tools which offers a holistic view of potential miRNA target sites. Through graphical denotation, the consensus sequence patterns of miRNAs binding across the genome are elaborated helps in the identification of robust target candidates for further analysis and validation. The consensus selected miRNAs in rice against RTBV as Venn diagram is shown in Fig. 8 A and 8 B. Similarly, RTSV target points by screened miRNAs from rice are shown in Fig. 9 A and 9 B. Likewise, the circos plots are drawn as shown in Fig. 10 A and 10 B from input data with the help from R studio packages. Additionally, the circos plots also depicted seed regions through psRNATarget and interaction of miRNA-mRNA from RTSV was shown in Fig. 11 A and 11 B. 3.8 Estimation of Free Energies of RTBV The evaluation of the thermodynamic stability of miRNA-target interactions can be significantly improved by calculating free energies. This approach enables the prioritization of potential regulatory interactions, ultimately enhancing the accuracy of miRNA target prediction. These analyses are crucial to gaining a better and more assertive understanding of miRNA-mediated gene regulation in organisms such as Oryza sativa , as well as pathogens like RTBV and RTSV. The Table 4 and Table 5 shows the list of various miRNAs and their hybridization with mRNA with duplex and binding energies against RTBV as well as in RTSV. Table 4 Illustration of the free energies of consensus miRNAs as predicted to target the genome of RTBV genome sequence. miRNAs miRNA-mRNA heteroduplex ΔG Duplex (Kcal/mol) ΔG Binding (Kcal/mol) osa-miR156g-3p CGACUGUCUCUCUC-UUCACUCG ::. :::::: :.:: ::: AAAGAUUAAGAGAGCAGGUUAGC -12.60 -12.01 osa-miR166a-5p GGAACUUGGUCUGUUGUAAGG .:::::. ::: :. : ::: UCUUGAUUGAGAGAGAAAUCC -7.60 -4.08 osa-miR160a-5p ACCGUAUG—UCCCUCGGUCCGU :::: :: :::..::: : GAUAAUACUGAGCGAGUUAGGAA -10.80 -7.41 osa-miR414 CACGUCUUGUUCACUUUGAUC :::. :. ::::.::.: GAACAGUGAAGAUGAAGCUGG -9.00 -6.70 osa-miR426 GCAUUCCUGUUUGAAGGUUUU :.:::::::::.:: UUCACUAAUAAACUUCCAGAA -5.70 -4.22 Table 5 Illustration of the free energies of consensus miRNAs predicted to target the genome of RTSV. miRNAs miRNA-mRNA heteroduplex ΔG Duplex (Kcal/mol) ΔG Binding (Kcal/mol) osa-miR164b ACGUGCACGGGACGAAGAGGU : ::::: : : :.:::: UUGAGGUGCCAUACAUUUCCA -6.10 -2.76 osa-miR156h-3p CGACUGUCUUUCUCUUCACUCG : :: :. :.:. :::::.:. GAUGUCGAAGAAGCAAGUGGGU -9.80 -10.01 osa-miR160a-5p ACCGUAUGUCCCUCGGUCCGU ::: :.:: :::: .: :: UGGAUUGCAUGGAGGUACGCU -18.20 -14.71 osa-miR166c-3p CCCCUUACUUCGGACCAGGCU ::::: ::::::: .: GCAAAAUGA-GCCUGGUGUGC -16.90 -15.47 osa-miR414 CCUGCUACUACUACUCCUACU :: .: ::::: ::::::: GGCUGCCAAUGAU-AGGAUGA -5.40 -5.31 osa-miR390-5p CCGCGAUAGGGAGGACUCGAA .:. : ..:::::: : :: ACUGUGACUUCUCCUGCGAUU -14.00 -12.25 osa-miR530-3p CAACGUAGACGGAGACGUGGA :::: :::. :: :.: AAAAGAUCUACCUUGGCUCUU -8.30 -3.87 3.9 Structural Annotation of miRNA-mRNA interactions in RTBV & RTSV The secondary structure and free energies of miRNAs is imperative for prediction in highly accurate way for gaining insights into the stability and conformational flexibility of miRNA. This is highly crucial to understand the functional characteristics of miRNAs which can target mRNA. The secondary structure is predicted by researchers who can pinpoint the regions that are critical for target binding and assess the accessibility of miRNA binding sites. The structural illustration of interaction between miRNAs and mRNA against RTBV and RTSV was shown in Figs. 12 and 13 respectively. Different rice derived miRNAs selected from miRNA database, mirBase and their interaction with mRNAs is clearly shown in Figs. 12 and 13 . 3.10 Site Conservation Analysis of RTBV & RTSV isolates Analyzing site conservation of RTBV and RTSV isolates involves examining the degree of similarity or conservation of specific regions across different strains or isolates of these viruses. This analysis helps identify regions that are functionally important or evolutionary conserved, providing insights into potential targets for intervention strategies. By comparing sequences from various isolates, researchers can discern conserved motifs or sites crucial for virus replication, transmission, or host interaction. In the Fig. 14 below, some sequences show 100 percent conservation, showing that these sites in RTBV are potential targets for miRNAs derived from Oryza sativa . Likewise, Fig. 15 indicates the target site conservation analysis present in RTSV genome in response to binding of selected rice miRNAs. 3.11 Phylogenetic Tree Construction by RTBV and RTSV The phylogenetic tree constructed to illustrate the evolutionary relationship between RTBV and RTSV viruses clearly demonstrates their genetic divergence and common ancestry. This analysis provides crucial insights into the evolutionary history of these pathogens, which is essential for understanding their spread, adaptation, and potential future implications for rice cultivation. The Fig. 16th shows the phylogenetic analysis of various taxas of RTBV and RTSV respectively. 4. DISCUSSION The present study made significant progress in identifying possible outcomes to silence and inactivate RTBV and RTSV genomes. The research work successfully identified five Oryza sativa -encoded miRNAs (miRNA osa-miR166a-5p, osa-miR156g-3p, osa-miR413, osa-miR426, osa-miR160a-5p), which can further be investigated to explore their potential target sites to inactivate the RTBV genome. Additionally, seven Oryza sativa -encoded miRNAs (osa-miR530-3p, osa-miR414, osa-miR390-5p, osa-miR156h-3p, osa-miR164b, osa-miR166c-3p, osa-miR160a-5p) were identified that manifested immense capability to silence RTSV genome. It also offers a promising solution for further research work in developing effective strategies to control RTBV. The selected miRNAs underwent rigorous evaluations through free energy and secondary structure prediction. Additionally, our site conservation analysis was implemented to uncover conserved sequence domains serve as potential targets for predicted miRNAs. The phylogenetic trees were constructed which revealed the evolutionary relationships among closely related species with precision. The study of miRNA-mediated target prediction and gene silencing 19 has been extensively explored in previous research, with various computational methods and algorithms developed to predict the repression strength of miRNAs and their target mRNAs. RTBV, RTSV 20 , RYMV 21 , CMD 22 , CLCuV 23 , MCMV 24 , and ZYMV 25 are few examples of studies conducted on miRNA-based target prediction. These studies have employed bioinformatics tools and computational algorithms to predict miRNA-target interactions and assess their efficacy in silencing viral genes 26 . By targeting essential viral genes or regulatory elements, miRNAs offer a promising approach for inhibiting viral replication and reducing disease severity 27 . The current study involves the utilization of four computational target prediction tools, namely, psRNATarget, RNA22, miRanda, and RNAhybrid with default parameters 28 . The miRNAs encoded by rice, predicted by four different tools, were rigorously evaluated and short-listed using the Intersection method. Through this process, only five miRNAs were selected which were common in at least three computational algorithms, and have the potential to effectively target the genome of RTBV 29 . Similarly, seven miRNAs were chosen from the consensus of three tools, to target the genome of RTSV. The miRNAs have been identified with high confidence and can potentially serve as effective targets for predicted ORFs on the genome of RTBV & RTSV 30 . These miRNAs will inhibit the translation of these ORFs into functional proteins vital for virus assembly and its replication 30 . The estimation of free energies for the predicted miRNAs has provided important insights into the strength and stability of the miRNA-mRNA duplex bond. The results indicate that the bond between the two is spontaneous and highly favorable, suggesting that the interaction between miRNA and mRNA is robust and reliable. Site conservation analysis has also been performed to identify the potential sites for miRNA targets. This analysis has revealed important information about the conservation of miRNA target sites across different species, highlighting the potential biological significance of these sites. The prediction and validation of candidate miRNAs from Oryza sativa to target the RTV genome has the potential to revolutionize the field of plant virology. By uncovering the molecular mechanisms underlying miRNA-mediated antiviral responses, researchers can pave the way for developing RNA-based strategies to enhance crop resilience against viral pathogens. Moreover, the insights gained from this study could have far-reaching implications for understanding host-pathogen interactions and advancing biotechnological approaches in agriculture. CONCLUSION In conclusion, the investigation of candidate miRNAs from Oryza sativa as prospective regulators of Rice Tungro Virus (RTV) represents a promising avenue of research with significant implications for crop protection and agricultural sustainability. The miRNAs hypothesized by consensus in this study are highly specific to RTBV (miRNA osa-miR166a-5p, osa-miR156g-3p, osa-miR413, osa-miR426, osa-miR160a-5p) & RTSV (osa-miR530-3p, osa-miR414, osa-miR390-5p, osa-miR156h-3p, osa-miR164b, osa-miR166c-3p, osa-miR160a-5p) and exhibit minimal off-target effects. Additionally, target-site conservation analysis demonstrates a high degree of conserved regions. Furthermore, predicted miRNAs were evaluated by free energies and their secondary structures, and the findings indicate that the interaction between miRNA and their target mRNA is highly favorable. By leveraging the intricate interplay between host miRNAs and viral genomes, researchers can unlock new strategies to mitigate the impact of RTV on rice production. Continued research in this field holds immense potential to transform our comprehension of plant-virus interactions and shape the future of crop protection strategies. Declarations Acknowledgment The authors are thankful to the Deanship of Scientific Research, King Khalid University, Abha, Saudi Arabia for supporting this work through the Large Research Group Project under Grant no. R.G.P.2/44/45. Funding No funding was available for this work. Author Contributions M.F.A. did original research and wrote original manuscript. T.S. conducted formal analysis and R.B. collected the whole data and arranged for analysis. S.I. gave his expertise and provided training to students for the usages of various computational tools and analysis. A.I. reviewed the manuscript and helped in software usages. M.U.A gave his expertise in interpretation of results research. M.Y. also reviewed the final manuscript while S.O.R. provided resources for the work. All authors approved the final manuscript. Data Availability All data used in this study is provided in the manuscript in different forms and in various sections. The online sequences data and its information is already provided as accession numbers within manuscript. Ethical Approval and Consent to Participate The study was conducted as according to institutional and national guidelines. It does not involve the physical usages of any living organisms, plants, animals and human therefore ethical approval is non applicable. This study was based on computational analysis and no participation consent was needed. Competing Interests The authors claim no competing interests. References Bua, B. & Ojirot, M. Assessing the importance of rice as food and income security crop in Puti-Puti sub-county, Pallisa district, Uganda. (2014). Bashir, B., Nawaz, B. & Sattar, S. An Economic Analysis of Rice Production in Pakistan: A Case Study. Asian Journal of Agricultural and Horticultural Research , 1-13 (2019). https://doi.org:10.9734/ajahr/2019/v4i330025 Shahzadi, N., Akhter, M., Ali, M. & Misbah, R. Economic aspects of basmati rice in Pakistan. Rice Research Institute, Kala Shah Kaku, Agriculture Department Punjab, Pakistan. Open access. DOI 10 , 2375-4338 (2018). Shanker, A. & Shanker, C. Abiotic and biotic stress in plants: recent advances and future perspectives. (2016). Anand, A., Pinninti, M., Tripathi, A., Mangrauthia, S. K. & Sanan-Mishra, N. Coordinated Action of RTBV and RTSV Proteins Suppress Host RNA Silencing Machinery. Microorganisms 10 (2022). https://doi.org:10.3390/microorganisms10020197 Bunawan, H., Dusik, L., Bunawan, S. N. & Amin, N. M. Rice tungro disease: From identification to disease control. World Appl. Sci. J 31 , 1221-1226 (2014). Srilatha, P. et al. Physical interaction of RTBV ORFI with D1 protein of Oryza sativa and Fe/Zn homeostasis play a key role in symptoms development during rice tungro disease to facilitate the insect mediated virus transmission. Virology 526 , 117-124 (2019). Verma, V., Sharma, S., Devi, S. V., Rajasubramaniam, S. & Dasgupta, I. Delay in virus accumulation and low virus transmission from transgenic rice plants expressing Rice tungro spherical virus RNA. Virus Genes 45 , 350-359 (2012). Ammar, E.-D. & Nault, L. R. Virus transmission by leafhoppers, planthoppers and treehoppers (Auchenorrhyncha, Homoptera). (2002). Norliza, A., Lau, H. Y., Habibuddin, H. & Tan, C. S. Cloning, expression and purification of recombinant RTSV and RTBV coat proteins for polyclonal antibody production. (2014). Anand, A., Pinninti, M., Tripathi, A., Mangrauthia, S. K. & Sanan-Mishra, N. Coordinated action of RTBV and RTSV proteins suppress host RNA silencing machinery. Microorganisms 10 , 197 (2022). Ghosh, D. & Chakraborty, S. Impact of viral silencing suppressors on plant viral synergism: a global agro-economic concern. Applied Microbiology and Biotechnology 105 , 6301-6313 (2021). Song, X. et al. Predicting miRNA-mediated gene silencing mode based on miRNA-target duplex features. Computers in Biology and Medicine 42 , 1-7 (2012). Zhou, M. & Luo, H. MicroRNA-mediated gene regulation: potential applications for plant genetic engineering. Plant molecular biology 83 , 59-75 (2013). Mohamed, N. A. et al. Candidate miRNAs from Oryza sativa for Silencing the Rice Tungro Viruses. Agriculture 13 , 651 (2023). Riffo-Campos, Á. L., Riquelme, I. & Brebi-Mieville, P. Tools for sequence-based miRNA target prediction: what to choose? International journal of molecular sciences 17 , 1987 (2016). Pratt, A. J. & MacRae, I. J. The RNA-induced silencing complex: a versatile gene-silencing machine. Journal of Biological Chemistry 284 , 17897-17901 (2009). Yousef, M., Showe, L. & Showe, M. A study of microRNAs in silico and in vivo: bioinformatics approaches to microRNA discovery and target identification. The FEBS journal 276 , 2150-2156 (2009). Tripathy, R. & Mishra, D. A Computational Approach of Rice (Oryza Sativa) Plant miRNA Target Prediction against Tungro Virus. Procedia engineering 38 , 1357-1361 (2012). Jabbar, B. et al. Target prediction of candidate miRNAs from Oryza sativa for silencing the RYMV genome. Computational biology and chemistry 83 , 107127 (2019). Ashraf, M. A., Ali, B., Brown, J. K., Shahid, I. & Yu, N. In Silico Identification of Cassava Genome-Encoded MicroRNAs with Predicted Potential for Targeting the ICMV-Kerala Begomoviral Pathogen of Cassava. Viruses 15 , 486 (2023). Ashraf, M. A., Brown, J. K., Iqbal, M. S. & Yu, N. Genome-Wide Identification of Cotton MicroRNAs Predicted for Targeting Cotton Leaf Curl Kokhran Virus-Lucknow. Microbiology Research 15 , 1-19 (2023). Iqbal, M. S. et al. In silico MCMV silencing concludes potential host-derived miRNAs in maize. Frontiers in plant science 8 , 372 (2017). Shahid, M. N., Rashid, S., Iqbal, M. S., Jamal, A. & Khalid, S. In silico prediction of potential mirnas to target zymv in cucumis melo. Pak. J. Bot 54 , 1319-1325 (2022). Ekimler, S. & Sahin, K. Computational methods for microRNA target prediction. Genes 5 , 671-683 (2014). Min, H. & Yoon, S. Got target?: computational methods for microRNA target prediction and their extension. Experimental & molecular medicine 42 , 233-244 (2010). Dai, X., Zhuang, Z. & Zhao, P. X. psRNATarget: a plant small RNA target analysis server (2017 release). Nucleic Acids Research 46 , W49-W54 (2018). https://doi.org:10.1093/nar/gky316 Loher, P. & Rigoutsos, I. Interactive exploration of RNA22 microRNA target predictions. Bioinformatics 28 , 3322-3323 (2012). Enright, A. et al. MicroRNA targets in Drosophila. Genome biology 4 , 1-27 (2003). Krüger, J. & Rehmsmeier, M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic acids research 34 , W451-W454 (2006). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4422179","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305862696,"identity":"e7a7b9e8-460a-426c-bd54-d3fb0f8bc74b","order_by":0,"name":"Mudassar Fareed Awan","email":"data:image/png;base64,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","orcid":"","institution":"Department of Biotechnology, Knowledge Unit of Science, University of Management and Technology Sialkot Campus, Punjab","correspondingAuthor":true,"prefix":"","firstName":"Mudassar","middleName":"Fareed","lastName":"Awan","suffix":""},{"id":305862697,"identity":"97866227-5d45-49ed-972c-428700774878","order_by":1,"name":"Tauheed Suddal","email":"","orcid":"","institution":"Department of Biotechnology, Knowledge Unit of Science, University of Management and Technology Sialkot Campus, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Tauheed","middleName":"","lastName":"Suddal","suffix":""},{"id":305862698,"identity":"8c0bfd61-b247-4d9b-96ea-809ca9a5d02e","order_by":2,"name":"Rozina Bibi","email":"","orcid":"","institution":"Department of Biotechnology, Knowledge Unit of Science, University of Management and Technology Sialkot Campus, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Rozina","middleName":"","lastName":"Bibi","suffix":""},{"id":305862699,"identity":"55d9585d-2654-4de0-a17b-549c30bd1431","order_by":3,"name":"Muhammad Shahzad Iqbal","email":"","orcid":"","institution":"Department of Biochemistry, University of Okara, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Shahzad","lastName":"Iqbal","suffix":""},{"id":305862700,"identity":"2e867cb0-7c1c-4e47-be1c-1e4de9bd5c0e","order_by":4,"name":"Asma Irshad","email":"","orcid":"","institution":"School of Biochemistry and Biotechnology, University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Asma","middleName":"","lastName":"Irshad","suffix":""},{"id":305862701,"identity":"dde978ba-da86-4bdf-8133-d028452b0e6a","order_by":5,"name":"Muhammad Umair Ahsan","email":"","orcid":"","institution":"Tamworth Agricultural Institute","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Umair","lastName":"Ahsan","suffix":""},{"id":305862702,"identity":"0944b7e4-36dd-494a-99af-dfdd16ec4814","order_by":6,"name":"Muhammad Yahya","email":"","orcid":"","institution":"University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Yahya","suffix":""},{"id":305862703,"identity":"0ea2a99f-9229-4368-aebb-cff520b905f8","order_by":7,"name":"Sofia Obaidur Rab","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Sofia","middleName":"Obaidur","lastName":"Rab","suffix":""}],"badges":[],"createdAt":"2024-05-15 03:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4422179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4422179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57104140,"identity":"8561358e-9c32-419d-afcb-6c54ff8883fa","added_by":"auto","created_at":"2024-05-24 16:43:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56029,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic workflow of the tools used in this research work.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/cc1a79a34836076dfa0c856d.jpg"},{"id":57103807,"identity":"310cf293-7c2b-4c7c-8983-71c3d959d4a1","added_by":"auto","created_at":"2024-05-24 16:35:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe figure illustrates\u003c/strong\u003ethe sequence annotation and ORF prediction in the genome of RTBV \u0026amp; RTSV. \u003cstrong\u003e(A)\u003c/strong\u003eIt shows that three ORFs were predicted in the RTBV genome at various locations \u003cstrong\u003e(B)\u003c/strong\u003e It illustrates that four ORFs were predicted in the RTSV genome at different locations. It is important to note that each representation showcases distinct locations, sizes, and lengths of nucleotides and amino acids, along with their encoding proteins.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/977f6a111ed5730c3c1ea9f4.jpg"},{"id":57104141,"identity":"8acf7a62-8e3d-4ac0-9a40-99f2be4cbe67","added_by":"auto","created_at":"2024-05-24 16:43:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47380,"visible":true,"origin":"","legend":"\u003cp\u003eInsights into the circular view of RTBV and RTSV genomes with this concise information. It shows that RTBV comprised of 1167bp with three ORFs while RTSV indicated 2109bp size having three ORFs respectively.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/2201763847c4f113e099fd25.jpg"},{"id":57103802,"identity":"b53ba6d5-4b13-44f4-873a-c464f3a3d589","added_by":"auto","created_at":"2024-05-24 16:35:27","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38069,"visible":true,"origin":"","legend":"\u003cp\u003eDepicting the applicability of psRNATarget tool predicting miRNA target locations in RTBV \u0026amp; RTSV genome. The colored dots represent putative binding sites showing interacting regions between miRNA and their target transcripts.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/336d65ccaf18a9636c73b4b2.jpg"},{"id":57103813,"identity":"e2145c63-2e24-4185-911d-3b758a4c7af7","added_by":"auto","created_at":"2024-05-24 16:35:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":28081,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the utilization of the RNA22 algorithm for predicting miRNA target sites within the genome of RTBV \u0026amp; RTSV. The colored dots represent potential target binding sites, indicating regions of complementarity between miRNA and target mRNA sequences.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/7c29f928b0e55bbbd3cf9d7e.jpg"},{"id":57103815,"identity":"278a53b7-1f28-452f-9a63-2c435624ec91","added_by":"auto","created_at":"2024-05-24 16:35:28","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":24227,"visible":true,"origin":"","legend":"\u003cp\u003eDepicts the computational analysis conducted with the miRanda algorithm to predict potential miRNA target sites within the genome of RTBV \u0026amp; RTSV. The colored dots indicate putative target binding sites on respective genomes\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/9383c0d932407c341703d1d2.jpg"},{"id":57103814,"identity":"cb872c6e-e0d9-4ca0-8fa9-0b4c198ed684","added_by":"auto","created_at":"2024-05-24 16:35:28","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":32957,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the computational prediction of miRNA target sites within the genome of RTBV \u0026amp; RTSV using the RNAHybrid algorithm. The colored dots represent potential binding sites, indicating hybridization between miRNA and target mRNA sequences.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/9591dc7d81fdad8384a0bbdb.jpg"},{"id":57103808,"identity":"779c8a41-2607-48fc-a489-89da29408005","added_by":"auto","created_at":"2024-05-24 16:35:27","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":38642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eThe Venn diagram highlights the total number of \u003cem\u003eOryza sativa\u003c/em\u003e encoded miRNAs targeting the RTBV genome, that have been predicted by four different algorithms: miRanda, psRNATarget, RNA22, and RNAhybrid. Our selection criteria for miRNAs is extremely stringent, as only those miRNAs that have been predicted by a consensus of three diverse algorithms have been chosen. \u003cstrong\u003e(B)\u003c/strong\u003e The Intersection plot provides a detailed insight into the predicted miRNAs, and it has conclusively identified five miRNAs that are common across at least three of the computational algorithms used.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/a88999b45ed166ce6b396db5.jpg"},{"id":57103812,"identity":"34897431-c0b3-429d-9470-1a0929fe9773","added_by":"auto","created_at":"2024-05-24 16:35:28","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":39829,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eThe Venn diagram depicts the number of \u003cem\u003eOryza sativa\u003c/em\u003e encoded miRNAs targeting the RTSV genome, as predicted by four distinct algorithms: miRanda, psRNATarget, RNA22, and RNAhybrid \u003cstrong\u003e(B)\u003c/strong\u003e The Intersection plot provides a detailed insight into the predicted miRNAs, and it has conclusively identified seven miRNAs that are common across at least three computational algorithms used.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/62b8c4d7677a169e6807c9b5.jpg"},{"id":57103817,"identity":"3cc35c32-d9a1-404a-9bc1-0685eed92305","added_by":"auto","created_at":"2024-05-24 16:35:28","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":76256,"visible":true,"origin":"","legend":"\u003cp\u003eThe Circos plots depict the rice-encoded miRNAs that have been meticulously selected by a consensus method. \u003cstrong\u003ePlot\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e explicitly showcases the consensus miRNAs that are proficient in targeting the genome of RTBV, while \u003cstrong\u003ePlot (B)\u003c/strong\u003e showcases the consensus miRNAs that are proficient in targeting the genome of RTSV. Each miRNA is vividly displayed with its respective location on the RTBV \u0026amp; RTSV genome—interaction Circos maps for seed-based RNAHybrid algorithm.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/70fae149dbd7a000cca757d3.jpg"},{"id":57103803,"identity":"87907b10-f771-4712-bebd-6a98195a43a0","added_by":"auto","created_at":"2024-05-24 16:35:27","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":82547,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction Circos map for the seed-based psRNATarget algorithm. \u003cstrong\u003e(A)\u003c/strong\u003e Circos map showing \u003cem\u003eOryza sativa\u003c/em\u003e encoded osa-miRNAs targeting the ORFs of RTBV. \u003cstrong\u003e(B)\u003c/strong\u003eCircos map showing \u003cem\u003eOryza sativa\u003c/em\u003e encoded osa-miRNAs targeting the ORFs of RTSV.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/f2fbea17daacadfa736740a9.jpg"},{"id":57103818,"identity":"f51400ae-6f8f-4848-b5cf-40686c2e0006","added_by":"auto","created_at":"2024-05-24 16:35:28","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":58413,"visible":true,"origin":"","legend":"\u003cp\u003eThe RNAfold web server predicted the folding structures of miRNAs, these structures significantly aid in identifying putative target sites for RTBV genome silencing\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/5f314d2a4f4b3d176e40fcfd.jpg"},{"id":57103805,"identity":"8a72e345-ca61-4732-885b-22676db48a13","added_by":"auto","created_at":"2024-05-24 16:35:27","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":61123,"visible":true,"origin":"","legend":"\u003cp\u003eThe RNAfold web server predicted the folding structures of miRNAs, and provided valuable insights aiding in the identification of putative target sites for RTSV genome silencing.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/04ade7b09a72c43418508de3.jpg"},{"id":57103811,"identity":"7ab94fea-caf7-475b-a5e6-cc105f4833fd","added_by":"auto","created_at":"2024-05-24 16:35:28","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":165433,"visible":true,"origin":"","legend":"\u003cp\u003eThe conserved sequences of RTBV isolates indicate a significant level of conservation in the conserved regions, suggesting that these regions could be effectively targeted by miRNAs.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/6f284730d7c529b24daff42f.jpg"},{"id":57103809,"identity":"b9d692bb-cda9-4938-a47d-73a56b948b4c","added_by":"auto","created_at":"2024-05-24 16:35:28","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":222487,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe figure s\u003c/strong\u003ehows the conserved sequences of RTSV isolates. The conserved sequences of RTSV isolates exhibit an impressive degree of conservation in the conserved regions, making these sites highly promising targets for miRNAs.\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/f683524dc88c418126ac7615.jpg"},{"id":57103816,"identity":"0b5d397a-e1ab-41e2-b8ab-92e14cf31590","added_by":"auto","created_at":"2024-05-24 16:35:28","extension":"jpg","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":79192,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(RTBV): \u003c/strong\u003eShows the phylogenetic tree of RTBV isolates generated by using CLC Genomics Workbench. The highlighted part shows the strain under study and the isolate on the same node represents sister taxas. The branches of the tree show the Bootstrap value and the length of the branch shows the amount of evolutionary time the two species diverge from each other.\u003cstrong\u003e (RTSV): \u003c/strong\u003eShows the phylogenetic tree of RTSV isolates generated by using CLC Genomics Workbench. The highlighted part shows the strain under study and the isolate on the same node represents sister taxas. The branches of the tree show the Bootstrap value and the length of the branch shows the amount of evolutionary time the two species diverge from each other.\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/9ed90e84cc3423bd0cf72e99.jpg"},{"id":57872826,"identity":"ae22d03f-0421-40b1-93bc-54f2adf9af22","added_by":"auto","created_at":"2024-06-06 18:34:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2082698,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4422179/v1/b13dd45b-8a98-49d8-809c-2c4b8d234668.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Potential of Oryza sativa derived Candidate miRNAs to Target Rice Tungro Bacilliform Virus (RTBV) Genome","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e) belongs to family Poaceae and is a highly nutritious cereal grain crop that sustains a significant numbers of world\u0026rsquo;s population. This grain crop is particularly prevalent in regions where food security is highly dependent upon economic stability\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Rice is one of the largest crop accounting for 1.3% of the country\u0026rsquo;s total GDP\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Pakistan is considered not only the 11th major producer but also the 4th largest rice exporter globally. The rice production accounts for 3.2% of value-added in agriculture\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, the productivity and yield potential of rice are constantly threatened by a myriad of multiple biotic stresses especially viruses that can induce havoc on agricultural production\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Rice Tungro Bacilliform Virus (RTBV in association with Rice Tungro Spherical Virus (RTSV) causes a deadly rice disease which is designated as Rice Tungro Disease (RTD). This disease remains a gigantic threat to rice productivity all over the world, particularly in Asia. The spreading of RTD results from the complex interaction happened between \u003cem\u003eRice Tungro Bacilliform Virus (RTBV)\u003c/em\u003e and \u003cem\u003eRice Tungro Spherical Virus (RTSV)\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The RTBV belongs to para-retrovirus with double-stranded DNA genome causing this viral infection. On the other hand, the RTSV is an RNA virus which only exhibited disease symptoms after interacting with RTBV\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The transmission of RTBV occurs through green leafhopper (\u003cem\u003eNephotettix virescens)\u003c/em\u003e which acts as a vector, gets accumulated in the nucleus of phloem cells in infected rice\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The two viruses work in an intricate mutual relationship having RTBV encoding suppression activity for handling localized silencing activity of the host, while RTSV components aid in suppression of viral propagation through cell-to-cell facilitating the spread of viral infection\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The interaction between these two viruses within single host plant is very complex leading to further complicating RTD progression and obstruct its management\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The virus is equally devastating as it infects both the indica and japonica subspecies of rice widely cultivated across Asia. Additionally, the complicated interaction between RTBV \u0026amp; RTSV and their vector makes the control difficult through conventional methods\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong diverse rice afflicting pathogens, \u003cem\u003eRice Tungro Bacilliform Virus (RTBV)\u003c/em\u003e emerged as a formidable adversary, causing discoloration, stunted growth, less number of tillers, sterile and partly filling grains debilitating tungro disease that poses a serious challenge to rice production worldwide, causing substantial economic losses\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe genetic manipulation is so far considered a promising approach against RTD which help in creating virus resistant rice lines. But the acceptability of GMOs food crop is still the biggest challenge in most of the world. In order to avoid any controversy and to enhance rice resistance against RTBV, gene silencing may be a procedure which involve no genetic manipulation or introduction of any foreign protein expression in rice. Recent studies have screened multiple potential miRNAs derived from \u003cem\u003eOryza sativa\u003c/em\u003e possessing immense potential for silencing RTBV.\u003c/p\u003e \u003cp\u003eThe RTD can be controlled by the most promising approach gene silencing which identified particular target positions in viral genomic regions for miRNA\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The miRNAs are small non-coding RNAs regulating gene expression through binding to complementary sequences in target mRNAs causing degradation or translational repression. Recent years in scientific eras, the miRNAs for RNAi-mediated gene silencing has gained considerable attention as a potent tool for combating viral infections in plants\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The present research work was aimed to predict potential candidate miRNAs derived from \u003cem\u003eOryza sativa\u003c/em\u003e and helped in discovering locations in mRNA employing computational algorithms and bioinformatics tools that can effectively interfere with viral replication and mitigate the possibility of Tungro disease infection in rice crop\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have worked in identifying specific rice-derived miRNAs that could target RTBV at certain positions. Computational analyses have been employed to predict candidate miRNAs in rice like osa-miR5510 and osa-miR3980a-3p showed efficacy against RTBV and potentially prevented the expression of crucial viral proteins\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The RTBV has also evolved procedure to suppress miRNA functions leading to create complications in RTBV resistance mechanism. The RTBV protein P4 has been shown to alter the siRNA profiles within infected cells, modulating the plant's innate defense responses and enhancing viral survival and replication\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Advancements in computational biology and employments of latest bioinformatics tools, have allowed for the in silico prediction of miRNA targets within the RTBV genome, offering insights into potential resistance mechanisms. These research methodologies provide a blueprint for genetic engineers make strategies for enhancing rice resistance against tungro disease\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe investigation of miRNA-based techniques for combating RTD is a significant development in the fields of plant virology and agricultural biotechnology. By utilizing the regulatory power of \u003cem\u003eOryza sativa\u003c/em\u003e derived miRNAs specifically target and silence viral genetic sequences, the present research work focused on employing multiple bioinformatics tools to determine potential target sites in RTBV genome through identified miRNAs. These tools work on the basis of multi-dimensional parameters and explored potential target sites in RTBV genome which interact with rice miRNAs by releasing free energies and folding energy. The study also identified those miRNAs appeared and suggested by all four computational algorithms. Such candidate miRNAs are probably the most effective promising miRNAs against RTBV. This research aims to predict candidate rice derived miRNAs exploring targets in RTBV genome to make solid interaction with viral mRNA for degradation of RTBV. Such novel developments will cause employing breakthroughs in rice pathology and agricultural practices.\u003c/p\u003e"},{"header":"2. MATERIALS \u0026 METHODS","content":"\u003cp\u003e\u003cstrong\u003e2.1 Acquisition of \u003cem\u003eOryza sativa\u003c/em\u003e miRNAs from miRBase Repository\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e50 mature\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cem\u003eOryza sativa\u003c/em\u003e miRNAs were extracted from miRBase, a comprehensive database of miRNA sequence annotations. The latest version of miRBase was employed to retrieve a curated collection of known miRNAs specific to \u003cem\u003eOryza sativa\u003c/em\u003e (https://www.mirbase.org/browse/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Retrieval of RTBV \u0026amp; RTSV Genomes from NCBI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe complete genome sequences, RTBV and RTSV were retrieved from the National Center for Biotechnology Information (NCBI) database. Accession numbers for RTBV (\u0026gt;HM149532.1) and RTSV (\u0026gt;U70989.1) genomes were obtained for subsequent analysis.(https://www.ncbi.nlm.nih.gov/)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Analysis of Open Reading Frames of RTBV \u0026amp; RTSV by CLC Genomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CLC Genomics Workbench software (version X) was employed to analyze open reading frames (ORFs) present within RTBV and RTSV genomic sequences. The ORFs were identified and annotated on the basis of their sequence homology and codon usage. It provides wonderful insights into potential protein-coding regions exist within the viral genomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Utilization of miRNA Target Prediction Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA variety of bioinformatics tools were employed for miRNA target prediction, details are presented in figure 1 \u0026amp; table 1 below. Various bioinformatics tools working on different parameters were employed to predict target sites in RTBV and RTSV genomes through multiple miRNAs. The various parameters and their details are mentioned in the table 1 and figure 1. Each computational tool worked in differential parameter to predict potential target sites in viruses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eThe\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003elist of various computational miRNA target prediction algorithms with respective parameters employed in the present study.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.80130293159609%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTools\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.22801302931596%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.97068403908795%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.80130293159609%\" valign=\"top\"\u003e\n \u003cp\u003epsRNATarget\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.22801302931596%\" valign=\"top\"\u003e\n \u003cp\u003e(Expectation = 8, penalty for opening gap = 2, penalty for extending gap = 0.5, penalty for G.U pair = 1, seed region = 2\u0026ndash;7 nt and HPS size = 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.97068403908795%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ehttps://www.zhaolab.org/psRNATarget/\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.80130293159609%\" valign=\"top\"\u003e\n \u003cp\u003eRNA22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.22801302931596%\" valign=\"top\"\u003e\n \u003cp\u003eMaximum folding energy = \u0026minus;12 kcal/mol, Minimum number of paired-up bases = 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.97068403908795%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ehttps://cm.jefferson.edu/rna22/Interactive/\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.80130293159609%\" valign=\"top\"\u003e\n \u003cp\u003emiRanda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.22801302931596%\" valign=\"top\"\u003e\n \u003cp\u003eAlignment Score Threshold = 140, Energy Threshold = -20kcal/mol \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.97068403908795%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cem\u003ehttp://www.microrna.org/microrna/getDownloads.do\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.80130293159609%\" valign=\"top\"\u003e\n \u003cp\u003eRNA hybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.22801302931596%\" valign=\"top\"\u003e\n \u003cp\u003eEnergy Threshold = -20kcal/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.97068403908795%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ehttps://bibiserv.cebitec.uni-bielefeld.de/rnahybrid\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 The Target Prediction of RTBV Genome through psRNATarget Algorithm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe\u0026nbsp;\u003c/strong\u003epsRNATarget is a computational tool designed for the prediction of small mRNA targets against pathogens. It was utilized to identify potential miRNA binding sites within the RTBV and RTSV genomes. The algorithm employs a schema-based approach to identify complementary patterns between small RNAs and target transcripts, providing a list of putative miRNA target matches\u0026nbsp;\u003cem\u003ehttps://www.zhaolab.org/psRNATarget/\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 RNA22 Algorithm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA22 algorithm, known for its pattern-based approach to miRNA target prediction, was employed to predict miRNA binding sites within the RTBV and RTSV genomes. RNA22 identifies potential targets based on folding energy and pattern recognition, allowing for the prediction of non-canonical targets and targets beyond the 3 untranslated region (3-UTR)\u0026nbsp;\u003cem\u003ehttps://cm.jefferson.edu/rna22/Interactive/\u003c/em\u003e\u003cem\u003e. \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 The Target Prediction sites in Viral Genomes through miRanda\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emiRanda is considered a widely employed miRNA target prediction tool, utilized to identify putative miRNA binding sites within the RTBV and RTSV genomes. The algorithm employs sequence complementarity and thermodynamic stability criteria to predict miRNA-target interactions, providing valuable insights into potential regulatory mechanisms (http://www.microrna.org/microrna/getDownloads.do).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Target Prediction through RNAHybrid in RTBV and RTSV Genomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRNAHybrid, a tool is employed to predict the hybridization of a short miRNA sequence with targeted mRNA sequence. It was also employed for miRNA target prediction in the context of RTBV and RTSV genomes. These algorithms calculate minimum hybridization capability on the basis of free energy, facilitating the identification of potential miRNA binding sites (https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Structural Annotation of Potential miRNA-mRNA Interactions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNAfold and RNAcofold are bioinformatics tools manifested for to represent miRNAs interactions with viral mRNAs. These tools help not only predicting secondary structure, and folding energy estimations. The stability of miRNAs-mRNA interactions was also ascertained through these tools help making RNA hetero-duplexes. These heteroduplexes formed between potential consensus miRNAs and their target regions in RTBV and RTSV through http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAcofold.cgi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 The Visualization of miRNAs Data in Circos Plots through R Studio\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical analysis of miRNA target prediction data was evaluated and represented by graphs from RStudio which is an integrated development environment for the R programming language. The tabulated data obtained from results using four different computational tools were graphically represented as scatter plots and intersection plots by through R.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 Target-site Conservation Analysis in RTBV and RTSV Genomic Sequences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conservation analysis of miRNA target sites within RTBV and RTSV genomes was conducted to assess their evolutionary lineages and conservation of miRNA-target interactions across different virants. Multipl Sequence Alignment (MSA) was executed through MUSCLE with default settings in Jalview (http://www.jalview.org/), to study the target-site conservation analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.12 Construction of Phylogenetic Trees\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe phylogenetic analysis of RTBV and RTSV isolates was manifested to determine their evolutionary relationships and genetic diversity was also explored. Multiple Sequence Alignments (MSA) of viral genomes were executed by employing CLC Genomics Workbench, followed by the construction of phylogenetic trees through distance-based methods such as neighbor-joining with bootstrap value 100.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sequence Annotation \u0026amp; ORF Prediction of RTBV \u0026amp; RTSV\u003c/h2\u003e \u003cp\u003eIn this section, we analyzed the complete genome sequences of RTBV and RTSV using CLC Genomics Workbench. The identification and annotation of open reading frames (ORFs) within the viral genomes were conducted to gain insights into potential protein-coding regions. Additionally, sequence characteristics and structural features of RTBV and RTSV genomes were examined to provide a comprehensive understanding of viral genome organization and composition in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicates the details of ORF and their location of both genomes in more details. The circular view of both viral genomes was also shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\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\u003eRepresenting RTBV Genome contains a specific number of ORFs, each with a defined location, starting codon, length, and the corresponding number of nucleotides.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of ORFs\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStart\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnd\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLength (nt| aa)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrand\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStart Codon\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1164\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1164 | 388\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 | 41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 | 30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\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\u003eRepresentation of the the number of ORFs, their location, starting codons, lengths and no. of their corresponding nucleotides in RTSV Genome.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of ORFs\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStart\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnd\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLength (nt | aa)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrand\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStart Codon\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2109\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2109 | 703\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207 | 69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e515\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 | 18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1853\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2107\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254 | 84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eATG\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2. The miRNA Target-site Prediction by Four Algorithms\u003c/h2\u003e \u003cp\u003eIn this study, we employed four different bioinformatics tools for miRNA target prediction using miRanda, psRNATarget, RNA22, and RNAhybrid. These tools are based on different parameters to identify potential miRNA binding sites within the genomes of RTBV and RTSV. The miRanda employed sequence complementarity and thermodynamic stability criteria, while psRNATarget used a schema-based approach. The highly precise RNA22 tool identified targets based on folding energy and pattern recognition while RNAhybrid predicts hybridization between short and long RNA sequences. By integrating results from these tools, we aimed to analyze miRNA-target interactions in viral genomes in highly comprehensively manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of miRNA target by psRNATarget Algorithm\u003c/h2\u003e \u003cp\u003eThe psRNATarget algorithm is used to identify possible targets of miRNA by searching for complementary sequences between miRNA and target transcripts. The colored dots in the Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e below represents the target sites predicted by psRNATarget on the genome of RTBV and RTSV. The figure indicates that the criteria in psRNATarget is very low and lenient indicating highest possibilities of miRNA location in various viral genomic regions. The expectation rate in RTSV is much lower as compared to RTBV genome sequence as Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Identification of miRNA target by RNA22\u003c/h2\u003e \u003cp\u003eThe RNA22 algorithm predicts potential miRNA targets in the RTBV \u0026amp; RTSV genomes based on sequence complementarity and structural accessibility. This computational tool worked by estimating folding energy (kcal/mol) according to various genomic positions. Seven purple colored dots representing miRNAs with highest folding energies are targeted before 300bp. Five miRNAs target 600bp-800bp while only four miRNAs comes after 1000bp location. The Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e below shows the potential target sites predicted by RNA22. Similarly, the lowest numbers of miRNAs are placed after 2000bps in RTSV genome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Identification of miRNA target by miRanda Algorithm\u003c/h2\u003e \u003cp\u003eThe miRanda algorithm reliably identified candidate miRNA targets by assessing sequence complementarity and thermodynamic stability based on folding energy (kcal/mol). The miRanda explored that only single miRNA targeted between 100-200bps while three miRNAs targeted between 800-1000bp in RTBV. Likewise, miRanda tool indicates one miRNA target at nearly 250-260bp position while two miRNAs make interaction with 1800-2000bps location in RTSV. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e below, the miRanda algorithm has accurately predicted the potential target on the genome of RTBV\u0026amp; RTSV.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Identification of miRNA targets through RNAHybrid Algorithm\u003c/h2\u003e \u003cp\u003eThe RNAHybrid algorithm effectively identifies potential miRNA targets by assessing the hybridization between miRNAs and target mRNAs. The Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e below conclusively displays the miRNAs that RNAHybrid has predicted and their respective target sites on the genome of RTBV and RTSV.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Graphical representation of Consensus of predicted miRNAs\u003c/h2\u003e \u003cp\u003eThe comprehensive graphical representation by R studio is constructed to showcase the consensus of predicted miRNAs. This visualization interpreted data from diverse prediction tools which offers a holistic view of potential miRNA target sites. Through graphical denotation, the consensus sequence patterns of miRNAs binding across the genome are elaborated helps in the identification of robust target candidates for further analysis and validation. The consensus selected miRNAs in rice against RTBV as Venn diagram is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB. Similarly, RTSV target points by screened miRNAs from rice are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB. Likewise, the circos plots are drawn as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB from input data with the help from R studio packages. Additionally, the circos plots also depicted seed regions through psRNATarget and interaction of miRNA-mRNA from RTSV was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Estimation of Free Energies of RTBV\u003c/h2\u003e \u003cp\u003eThe evaluation of the thermodynamic stability of miRNA-target interactions can be significantly improved by calculating free energies. This approach enables the prioritization of potential regulatory interactions, ultimately enhancing the accuracy of miRNA target prediction. These analyses are crucial to gaining a better and more assertive understanding of miRNA-mediated gene regulation in organisms such as \u003cem\u003eOryza sativa\u003c/em\u003e, as well as pathogens like RTBV and RTSV. The Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the list of various miRNAs and their hybridization with mRNA with duplex and binding energies against RTBV as well as in RTSV.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIllustration of the free energies of consensus miRNAs as predicted to target the genome of RTBV genome sequence.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNAs\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emiRNA-mRNA heteroduplex\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔG Duplex (Kcal/mol)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔG Binding (Kcal/mol)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR156g-3p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGACUGUCUCUCUC-UUCACUCG\u003c/p\u003e \u003cp\u003e::. :::::: :.:: :::\u003c/p\u003e \u003cp\u003eAAAGAUUAAGAGAGCAGGUUAGC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-12.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR166a-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGAACUUGGUCUGUUGUAAGG\u003c/p\u003e \u003cp\u003e.:::::. ::: :. : :::\u003c/p\u003e \u003cp\u003eUCUUGAUUGAGAGAGAAAUCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.08\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR160a-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCGUAUG—UCCCUCGGUCCGU\u003c/p\u003e \u003cp\u003e:::: :: :::..::: :\u003c/p\u003e \u003cp\u003eGAUAAUACUGAGCGAGUUAGGAA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.41\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR414\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACGUCUUGUUCACUUUGAUC\u003c/p\u003e \u003cp\u003e:::. :. ::::.::.:\u003c/p\u003e \u003cp\u003eGAACAGUGAAGAUGAAGCUGG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.70\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR426\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCAUUCCUGUUUGAAGGUUUU\u003c/p\u003e \u003cp\u003e:.:::::::::.::\u003c/p\u003e \u003cp\u003eUUCACUAAUAAACUUCCAGAA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.22\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIllustration of the free energies of consensus miRNAs predicted to target the genome of RTSV.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNAs\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emiRNA-mRNA heteroduplex\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔG Duplex (Kcal/mol)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔG Binding (Kcal/mol)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR164b\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACGUGCACGGGACGAAGAGGU\u003c/p\u003e \u003cp\u003e: ::::: : : :.::::\u003c/p\u003e \u003cp\u003eUUGAGGUGCCAUACAUUUCCA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.76\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR156h-3p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGACUGUCUUUCUCUUCACUCG\u003c/p\u003e \u003cp\u003e: :: :. :.:. :::::.:.\u003c/p\u003e \u003cp\u003eGAUGUCGAAGAAGCAAGUGGGU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR160a-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCGUAUGUCCCUCGGUCCGU\u003c/p\u003e \u003cp\u003e::: :.:: :::: .: ::\u003c/p\u003e \u003cp\u003eUGGAUUGCAUGGAGGUACGCU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-18.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-14.71\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR166c-3p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCCCUUACUUCGGACCAGGCU\u003c/p\u003e \u003cp\u003e::::: ::::::: .:\u003c/p\u003e \u003cp\u003eGCAAAAUGA-GCCUGGUGUGC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-16.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-15.47\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR414\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCUGCUACUACUACUCCUACU\u003c/p\u003e \u003cp\u003e:: .: ::::: :::::::\u003c/p\u003e \u003cp\u003eGGCUGCCAAUGAU-AGGAUGA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.31\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR390-5p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCGCGAUAGGGAGGACUCGAA\u003c/p\u003e \u003cp\u003e.:. : ..:::::: : ::\u003c/p\u003e \u003cp\u003eACUGUGACUUCUCCUGCGAUU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-14.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.25\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eosa-miR530-3p\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAACGUAGACGGAGACGUGGA\u003c/p\u003e \u003cp\u003e:::: :::. :: :.:\u003c/p\u003e \u003cp\u003eAAAAGAUCUACCUUGGCUCUU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.87\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Structural Annotation of miRNA-mRNA interactions in RTBV \u0026amp; RTSV\u003c/h2\u003e \u003cp\u003eThe secondary structure and free energies of miRNAs is imperative for prediction in highly accurate way for gaining insights into the stability and conformational flexibility of miRNA. This is highly crucial to understand the functional characteristics of miRNAs which can target mRNA. The secondary structure is predicted by researchers who can pinpoint the regions that are critical for target binding and assess the accessibility of miRNA binding sites. The structural illustration of interaction between miRNAs and mRNA against RTBV and RTSV was shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e respectively. Different rice derived miRNAs selected from miRNA database, mirBase and their interaction with mRNAs is clearly shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Site Conservation Analysis of RTBV \u0026amp; RTSV isolates\u003c/h2\u003e \u003cp\u003eAnalyzing site conservation of RTBV and RTSV isolates involves examining the degree of similarity or conservation of specific regions across different strains or isolates of these viruses. This analysis helps identify regions that are functionally important or evolutionary conserved, providing insights into potential targets for intervention strategies. By comparing sequences from various isolates, researchers can discern conserved motifs or sites crucial for virus replication, transmission, or host interaction. In the Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e below, some sequences show 100 percent conservation, showing that these sites in RTBV are potential targets for miRNAs derived from \u003cem\u003eOryza sativa\u003c/em\u003e. Likewise, Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e indicates the target site conservation analysis present in RTSV genome in response to binding of selected rice miRNAs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.11 Phylogenetic Tree Construction by RTBV and RTSV\u003c/h2\u003e \u003cp\u003eThe phylogenetic tree constructed to illustrate the evolutionary relationship between RTBV and RTSV viruses clearly demonstrates their genetic divergence and common ancestry. This analysis provides crucial insights into the evolutionary history of these pathogens, which is essential for understanding their spread, adaptation, and potential future implications for rice cultivation. The Fig.\u0026nbsp;16th shows the phylogenetic analysis of various taxas of RTBV and RTSV respectively.\u003c/p\u003e "},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe present study made significant progress in identifying possible outcomes to silence and inactivate RTBV and RTSV genomes. The research work successfully identified five \u003cem\u003eOryza sativa\u003c/em\u003e-encoded miRNAs (miRNA osa-miR166a-5p, osa-miR156g-3p, osa-miR413, osa-miR426, osa-miR160a-5p), which can further be investigated to explore their potential target sites to inactivate the RTBV genome. Additionally, seven \u003cem\u003eOryza sativa\u003c/em\u003e-encoded miRNAs (osa-miR530-3p, osa-miR414, osa-miR390-5p, osa-miR156h-3p, osa-miR164b, osa-miR166c-3p, osa-miR160a-5p) were identified that manifested immense capability to silence RTSV genome. It also offers a promising solution for further research work in developing effective strategies to control RTBV. The selected miRNAs underwent rigorous evaluations through free energy and secondary structure prediction. Additionally, our site conservation analysis was implemented to uncover conserved sequence domains serve as potential targets for predicted miRNAs. The phylogenetic trees were constructed which revealed the evolutionary relationships among closely related species with precision.\u003c/p\u003e\u003cp\u003eThe study of miRNA-mediated target prediction and gene silencing\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e has been extensively explored in previous research, with various computational methods and algorithms developed to predict the repression strength of miRNAs and their target mRNAs. RTBV, RTSV \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, RYMV \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, CMD \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, CLCuV \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, MCMV \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and ZYMV\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e are few examples of studies conducted on miRNA-based target prediction. These studies have employed bioinformatics tools and computational algorithms to predict miRNA-target interactions and assess their efficacy in silencing viral genes\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. By targeting essential viral genes or regulatory elements, miRNAs offer a promising approach for inhibiting viral replication and reducing disease severity \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe current study involves the utilization of four computational target prediction tools, namely, psRNATarget, RNA22, miRanda, and RNAhybrid with default parameters\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The miRNAs encoded by rice, predicted by four different tools, were rigorously evaluated and short-listed using the Intersection method. Through this process, only five miRNAs were selected which were common in at least three computational algorithms, and have the potential to effectively target the genome of RTBV\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Similarly, seven miRNAs were chosen from the consensus of three tools, to target the genome of RTSV. The miRNAs have been identified with high confidence and can potentially serve as effective targets for predicted ORFs on the genome of RTBV \u0026amp; RTSV\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These miRNAs will inhibit the translation of these ORFs into functional proteins vital for virus assembly and its replication\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The estimation of free energies for the predicted miRNAs has provided important insights into the strength and stability of the miRNA-mRNA duplex bond. The results indicate that the bond between the two is spontaneous and highly favorable, suggesting that the interaction between miRNA and mRNA is robust and reliable. Site conservation analysis has also been performed to identify the potential sites for miRNA targets. This analysis has revealed important information about the conservation of miRNA target sites across different species, highlighting the potential biological significance of these sites.\u003c/p\u003e\u003cp\u003eThe prediction and validation of candidate miRNAs from \u003cem\u003eOryza sativa\u003c/em\u003e to target the RTV genome has the potential to revolutionize the field of plant virology. By uncovering the molecular mechanisms underlying miRNA-mediated antiviral responses, researchers can pave the way for developing RNA-based strategies to enhance crop resilience against viral pathogens. Moreover, the insights gained from this study could have far-reaching implications for understanding host-pathogen interactions and advancing biotechnological approaches in agriculture.\u003c/p\u003e"},{"header":"CONCLUSION","content":" \u003cp\u003eIn conclusion, the investigation of candidate miRNAs from Oryza sativa as prospective regulators of Rice Tungro Virus (RTV) represents a promising avenue of research with significant implications for crop protection and agricultural sustainability. The miRNAs hypothesized by consensus in this study are highly specific to RTBV (miRNA osa-miR166a-5p, osa-miR156g-3p, osa-miR413, osa-miR426, osa-miR160a-5p) \u0026amp; RTSV (osa-miR530-3p, osa-miR414, osa-miR390-5p, osa-miR156h-3p, osa-miR164b, osa-miR166c-3p, osa-miR160a-5p) and exhibit minimal off-target effects. Additionally, target-site conservation analysis demonstrates a high degree of conserved regions. Furthermore, predicted miRNAs were evaluated by free energies and their secondary structures, and the findings indicate that the interaction between miRNA and their target mRNA is highly favorable. By leveraging the intricate interplay between host miRNAs and viral genomes, researchers can unlock new strategies to mitigate the impact of RTV on rice production. Continued research in this field holds immense potential to transform our comprehension of plant-virus interactions and shape the future of crop protection strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the Deanship of Scientific Research, King Khalid University, Abha, Saudi Arabia for supporting this work through the Large Research Group Project under Grant no. R.G.P.2/44/45.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was available for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.F.A. did original research and wrote original manuscript. T.S. conducted formal analysis and R.B. collected the whole data and arranged for analysis. S.I. gave his expertise and provided training to students for the usages of various computational tools and analysis. A.I. reviewed the manuscript and helped in software usages. M.U.A gave his expertise in interpretation of results research. M.Y. also reviewed the final manuscript while S.O.R. provided resources for the work. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study is provided in the manuscript in different forms and in various sections. The online sequences data and its information is already provided \u0026nbsp;as accession numbers within manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval \u0026nbsp;and Consent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted as according to institutional and national guidelines. It does not involve the physical usages of any living organisms, plants, animals and human therefore ethical approval is non applicable. This study was based on computational analysis and no participation consent was needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors claim no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBua, B. \u0026amp; Ojirot, M. Assessing the importance of rice as food and income security crop in Puti-Puti sub-county, Pallisa district, Uganda. (2014). \u003c/li\u003e\n\u003cli\u003eBashir, B., Nawaz, B. \u0026amp; Sattar, S. An Economic Analysis of Rice Production in Pakistan: A Case Study. \u003cem\u003eAsian Journal of Agricultural and Horticultural Research\u003c/em\u003e, 1-13 (2019). https://doi.org:10.9734/ajahr/2019/v4i330025\u003c/li\u003e\n\u003cli\u003eShahzadi, N., Akhter, M., Ali, M. \u0026amp; Misbah, R. Economic aspects of basmati rice in Pakistan. \u003cem\u003eRice Research Institute, Kala Shah Kaku, Agriculture Department Punjab, Pakistan. Open access. DOI\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 2375-4338 (2018). \u003c/li\u003e\n\u003cli\u003eShanker, A. \u0026amp; Shanker, C. Abiotic and biotic stress in plants: recent advances and future perspectives. (2016). \u003c/li\u003e\n\u003cli\u003eAnand, A., Pinninti, M., Tripathi, A., Mangrauthia, S. K. \u0026amp; Sanan-Mishra, N. Coordinated Action of RTBV and RTSV Proteins Suppress Host RNA Silencing Machinery. \u003cem\u003eMicroorganisms\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e (2022). https://doi.org:10.3390/microorganisms10020197\u003c/li\u003e\n\u003cli\u003eBunawan, H., Dusik, L., Bunawan, S. N. \u0026amp; Amin, N. M. Rice tungro disease: From identification to disease control. \u003cem\u003eWorld Appl. Sci. J\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1221-1226 (2014). \u003c/li\u003e\n\u003cli\u003eSrilatha, P.\u003cem\u003e et al.\u003c/em\u003e Physical interaction of RTBV ORFI with D1 protein of Oryza sativa and Fe/Zn homeostasis play a key role in symptoms development during rice tungro disease to facilitate the insect mediated virus transmission. \u003cem\u003eVirology\u003c/em\u003e \u003cstrong\u003e526\u003c/strong\u003e, 117-124 (2019). \u003c/li\u003e\n\u003cli\u003eVerma, V., Sharma, S., Devi, S. V., Rajasubramaniam, S. \u0026amp; Dasgupta, I. Delay in virus accumulation and low virus transmission from transgenic rice plants expressing Rice tungro spherical virus RNA. \u003cem\u003eVirus Genes\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 350-359 (2012). \u003c/li\u003e\n\u003cli\u003eAmmar, E.-D. \u0026amp; Nault, L. R. Virus transmission by leafhoppers, planthoppers and treehoppers (Auchenorrhyncha, Homoptera). (2002). \u003c/li\u003e\n\u003cli\u003eNorliza, A., Lau, H. Y., Habibuddin, H. \u0026amp; Tan, C. S. Cloning, expression and purification of recombinant RTSV and RTBV coat proteins for polyclonal antibody production. (2014). \u003c/li\u003e\n\u003cli\u003eAnand, A., Pinninti, M., Tripathi, A., Mangrauthia, S. K. \u0026amp; Sanan-Mishra, N. Coordinated action of RTBV and RTSV proteins suppress host RNA silencing machinery. \u003cem\u003eMicroorganisms\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 197 (2022). \u003c/li\u003e\n\u003cli\u003eGhosh, D. \u0026amp; Chakraborty, S. Impact of viral silencing suppressors on plant viral synergism: a global agro-economic concern. \u003cem\u003eApplied Microbiology and Biotechnology\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e, 6301-6313 (2021). \u003c/li\u003e\n\u003cli\u003eSong, X.\u003cem\u003e et al.\u003c/em\u003e Predicting miRNA-mediated gene silencing mode based on miRNA-target duplex features. \u003cem\u003eComputers in Biology and Medicine\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 1-7 (2012). \u003c/li\u003e\n\u003cli\u003eZhou, M. \u0026amp; Luo, H. MicroRNA-mediated gene regulation: potential applications for plant genetic engineering. \u003cem\u003ePlant molecular biology\u003c/em\u003e \u003cstrong\u003e83\u003c/strong\u003e, 59-75 (2013). \u003c/li\u003e\n\u003cli\u003eMohamed, N. A.\u003cem\u003e et al.\u003c/em\u003e Candidate miRNAs from Oryza sativa for Silencing the Rice Tungro Viruses. \u003cem\u003eAgriculture\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 651 (2023). \u003c/li\u003e\n\u003cli\u003eRiffo-Campos, \u0026Aacute;. L., Riquelme, I. \u0026amp; Brebi-Mieville, P. Tools for sequence-based miRNA target prediction: what to choose? \u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 1987 (2016). \u003c/li\u003e\n\u003cli\u003ePratt, A. J. \u0026amp; MacRae, I. J. The RNA-induced silencing complex: a versatile gene-silencing machine. \u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e \u003cstrong\u003e284\u003c/strong\u003e, 17897-17901 (2009). \u003c/li\u003e\n\u003cli\u003eYousef, M., Showe, L. \u0026amp; Showe, M. A study of microRNAs in silico and in vivo: bioinformatics approaches to microRNA discovery and target identification. \u003cem\u003eThe FEBS journal\u003c/em\u003e \u003cstrong\u003e276\u003c/strong\u003e, 2150-2156 (2009). \u003c/li\u003e\n\u003cli\u003eTripathy, R. \u0026amp; Mishra, D. A Computational Approach of Rice (Oryza Sativa) Plant miRNA Target Prediction against Tungro Virus. \u003cem\u003eProcedia engineering\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 1357-1361 (2012). \u003c/li\u003e\n\u003cli\u003eJabbar, B.\u003cem\u003e et al.\u003c/em\u003e Target prediction of candidate miRNAs from Oryza sativa for silencing the RYMV genome. \u003cem\u003eComputational biology and chemistry\u003c/em\u003e \u003cstrong\u003e83\u003c/strong\u003e, 107127 (2019). \u003c/li\u003e\n\u003cli\u003eAshraf, M. A., Ali, B., Brown, J. K., Shahid, I. \u0026amp; Yu, N. In Silico Identification of Cassava Genome-Encoded MicroRNAs with Predicted Potential for Targeting the ICMV-Kerala Begomoviral Pathogen of Cassava. \u003cem\u003eViruses\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 486 (2023). \u003c/li\u003e\n\u003cli\u003eAshraf, M. A., Brown, J. K., Iqbal, M. S. \u0026amp; Yu, N. Genome-Wide Identification of Cotton MicroRNAs Predicted for Targeting Cotton Leaf Curl Kokhran Virus-Lucknow. \u003cem\u003eMicrobiology Research\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1-19 (2023). \u003c/li\u003e\n\u003cli\u003eIqbal, M. S.\u003cem\u003e et al.\u003c/em\u003e In silico MCMV silencing concludes potential host-derived miRNAs in maize. \u003cem\u003eFrontiers in plant science\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 372 (2017). \u003c/li\u003e\n\u003cli\u003eShahid, M. N., Rashid, S., Iqbal, M. S., Jamal, A. \u0026amp; Khalid, S. In silico prediction of potential mirnas to target zymv in cucumis melo. \u003cem\u003ePak. J. Bot\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 1319-1325 (2022). \u003c/li\u003e\n\u003cli\u003eEkimler, S. \u0026amp; Sahin, K. Computational methods for microRNA target prediction. \u003cem\u003eGenes\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 671-683 (2014). \u003c/li\u003e\n\u003cli\u003eMin, H. \u0026amp; Yoon, S. Got target?: computational methods for microRNA target prediction and their extension. \u003cem\u003eExperimental \u0026amp; molecular medicine\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 233-244 (2010). \u003c/li\u003e\n\u003cli\u003eDai, X., Zhuang, Z. \u0026amp; Zhao, P. X. psRNATarget: a plant small RNA target analysis server (2017 release). \u003cem\u003eNucleic Acids Research\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, W49-W54 (2018). https://doi.org:10.1093/nar/gky316\u003c/li\u003e\n\u003cli\u003eLoher, P. \u0026amp; Rigoutsos, I. Interactive exploration of RNA22 microRNA target predictions. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 3322-3323 (2012). \u003c/li\u003e\n\u003cli\u003eEnright, A.\u003cem\u003e et al.\u003c/em\u003e MicroRNA targets in Drosophila. \u003cem\u003eGenome biology\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 1-27 (2003). \u003c/li\u003e\n\u003cli\u003eKr\u0026uuml;ger, J. \u0026amp; Rehmsmeier, M. RNAhybrid: microRNA target prediction easy, fast and flexible. \u003cem\u003eNucleic acids research\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, W451-W454 (2006). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"miRNA, mRNA, RTBV, RTSV, Computational tools","lastPublishedDoi":"10.21203/rs.3.rs-4422179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4422179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Rice tungro disease (RTD) poses a serious threat to global rice cultivation, primarily afflicted by Rice tungro bacilliform virus (RTBV) and Rice tungro spherical virus (RTSV) strains. This challenge demands innovative approaches presenting a computational procedure to identify potential candidate miRNAs from \u003cem\u003eOryza sativa \u003c/em\u003ebased mirBase repository that can predict target sites in RTBV and RTSV genome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThrough our insilico based analysis utilizing different target prediction algorithms, five potential rice derived miRNAs were screened with maximum potential to target the RTBV genome. The candidate miRNAs include osa-miR166a-5p, osa-miR156g-3p, osa-miR413, osa-miR426, and osa-miR160a-5p. Additionally, seven miRNAs were predicted to explore their potential to target the RTSV genome i.e., osa-miR530-3p, osa-miR414, osa-miR390-5p, osa-miR156h-3p, osa-miR164b, osa-miR166c-3p, and osa-miR160a-5p. These miRNAs underwent effective evaluation, including free energy estimation and secondary structures were determined to ensure their efficacy in genome silencing. Moreover, site conservation analysis revealed conserved domains inside target sites. The screened miRNAs underscoring their immense potential to trigger robust mRNA intervention in viral genome. Furthermore, phylogenetic trees were also constructed to interpret the evolutionary relationships among closely related species providing valuable insights into the evolutionary standing of RTBV and RTSV. This study presents a holistic computational framework exploring potential candidate miRNAs derived from \u003cem\u003eOryza sativa\u003c/em\u003e with good capability to target RTBV and RTSV genomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e These findings hold considerable promise for the development of RNA-based strategies aimed at mitigating the impact of rice tungro disease, thereby contributing to sustainable rice production and global food security.\u003c/p\u003e","manuscriptTitle":"Exploring the Potential of Oryza sativa derived Candidate miRNAs to Target Rice Tungro Bacilliform Virus (RTBV) Genome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-24 16:35:22","doi":"10.21203/rs.3.rs-4422179/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79ac3196-23ea-47c7-8eb7-1c464fefdaf4","owner":[],"postedDate":"May 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-06T18:26:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-24 16:35:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4422179","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4422179","identity":"rs-4422179","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}