Integrative Analysis of tRNA-Derived Fragments in Plant Adaptation to Biotic Stress: A Comparative Study and Database

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Swain, Niyati Bisht, Shailesh Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5813390/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2025 Read the published version in Functional & Integrative Genomics → Version 1 posted 11 You are reading this latest preprint version Abstract Plants face significant challenges from biotic stresses, that adversely impact their growth and development. Amongst the various regulatory molecules, transfer RNA-derived fragments (tRFs) play crucial roles in modulating adaptive defense mechanisms. Although the role of tRFs in response to biotic stresses is still emerging, it is evident that different biotic stressors elicit distinct regulatory pathways. This study investigates the involvement of tRFs in stress response and resistance across three plant species: Arabidopsis thaliana , Oryza sativa , and Solanum lycopersicum . Our findings reveal a complex regulatory network where tRFs interact with mRNA targets, miRNAs, and transposable elements, underscoring their significance in adaptive biotic stress responses. This research advances the understanding of tRF regulatory mechanisms and lays the foundation for new strategies to enhance resilience against biotic stress. The database supporting this study is freely accessible at http://www.nipgr.ac.in/PbtRFdb , providing a valuable resource for further research on the tRFs in plant biotic stress responses. tRFs tRNA fragments tncRNAs Biotic stress Genome Regulation Plant Database Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The majority of the human genome encodes for non-coding RNAs (ncRNAs) which are now extensively studied for their crucial roles in regulating gene expression and genome organization (Nemeth et al., 2023 ). The endonucleolytic cleavage of precursor tRNA (pre-tRNA) or mature tRNAs, which constitute 4–10% of total cellular RNA, results the formation of a pool of tRNA-derived fragments (Lee et al., 2009 ; Zhao et al., 2023 ). It constitutes the short fragments of length 14 to 30 nucleotides (nt) called tRNA derived fragments (tRFs or tDRs), longer tRNA halves (tRHs or tiRNAs) of approximately 30 to 50 nt (Sun et al., 2020 ; Zhu, Ge, et al., 2019 ), and other tRFs from the internal region of tRNAs. All the classes termed as tRNA-derived non-coding RNAs (tncRNAs) are conserved across most organisms. These genome regulatory molecules are further classified based on the tRNA from which they are derived. The tRFs derived from the 5' end are tRF-5, and the 3' end generates tRF-3, resulting from cleavage in the D and T regions, respectively. Additionally, pre-tRNA generates 5' U-tRFs (leader tRF) and tRF-1/tsRNA (30 U-tRFs) from the 5' leader and 3' trailer regions, respectively (Fig. 1 ). In the case of tRNA halves, 5' and 3' tRHs are produced from cleavage in the anticodon region, containing the 5' and 3' portions of the mature tRNA. Therefore, the identified tRFs are classified into tRF-5, tRF-3, tRF-1, leader-tRF, 5' tRH, 3' tRH, and a miscellaneous group labeled as other tRFs. The originating tRNA type, cell type, developmental stage, and stress conditions influence the specific cleavage of tRNA that generates these fragments. (Zahra et al., 2021 ). These tRFs can be produced by both DICER-dependent and -independent manner. RNase P eliminates the leader sequence and RNase Z cleaves the trailer sequence precisely at the discriminator base of the precursor-tRNA leading to the production of leader-tRF and tRF-1/tsRNA, respectively (Hak Kyun Kim, 2020 ). Processing of certain types of tRNA halves requires both Dicer and angiogenin (Liu et al., 2018 ). The regulatory role of tRFs includes, 1) regulation of mRNA stability as well as their potential to induce mRNA cleavage (Yu et al., 2021 ), 2) translation inhibition (Kumar et al., 2016 ), 3) regulation of ribosome production (H. K. Kim et al., 2017 ), 4) control on apoptosis of cells depending on the diseases and target mRNA (Cui et al., 2022 ; Gong et al., 2023 ), and 5) facilitates amplification of viral propagation (Fu et al., 2015 ). Exploration of tRFs in plants has been growing rapidly due to the exposure of plants to various biotic and abiotic stresses, which significantly impact their development (C. Wang et al., 2023 ). To manage these challenging conditions, plants have developed complex response mechanisms that involve multiple gene regulatory processes, including transcriptional (Chang et al., 2020 ) and post-transcriptional regulation (Jones-Rhoades et al., 2006 ). It is shown that tRFs in plants operate similarly to those in animals which could be due to their similarity in biogenesis, suggesting an evolutionary connection (Cao et al., 2022 ; Lalande et al., 2020 ). The studies on tRFs related to biotic stresses are relatively less developed compared to research on abiotic stresses (C. Wang et al., 2023 ). Their functional roles were thoroughly investigated in both the leaves and roots of black pepper affected by Phytophthora capsici (Asha & Soniya, 2016 ). In A. thaliana , Botrytis cinerea infection was seen to be significantly downregulating the 5’ tRNA-derived small RNAs (Gu et al., 2022 ) whereas in Solanum lycopersicum , differentially expressed tRFs were characterized in plants subjected to Tomato Mosaic Virus (ToMV) (Zahra et al., 2021 ). Identification and characterization of tRFs and their specific possible targets in plants, particularly under biotic stress conditions, alongside an in-depth understanding of tRF-guided stress regulatory networks, can greatly contribute to enduring agricultural production. In this study, we identified a diverse range of tRFs across three angiosperms viz. Arabidopsis thaliana , Oryza sativa , and Solanum lycopersicum using publicly available small RNA-seq (sRNA-seq) datasets of plants under various biotic stresses. Differential expression (DE) analysis revealed stress-specific tRFs, and common tRNA sources, followed by their mRNA targets, complemented by functional enrichment and pathway analysis. Analysis of RNA-Seq datasets further highlighted the DE patterns of mRNA targets, and interactions between tRFs and transposable elements, offering insights into their roles in stress adaptation. These findings were integrated into PbtRFdb, a comprehensive database with advanced search features, visualization tools, and dynamic graphs for exploring tRFs under biotic stress conditions. An overview of each step followed during the study is shown in Fig. 2 . Methodology Data Retrieval and Processing A total of 112, 188, and 44 sRNA-seq samples of Arabidopsis thaliana , Oryza sativa , and Solanum lycopersicum , sequenced under various biotic stress conditions, were downloaded from the NCBI SRA Toolkit (v3.0.2) (S. Sherry et al., 2023 ). Genome files for the respective plants viz. Arabidopsis thaliana (TAIR10.1), Oryza sativa japonica (Build 4.0; organellar: IRGSP-1.0), and Solanum lycopersicum (nuclear: SL4.0; organellar: SL3.0), were obtained from EnsemblPlants ( https://plants.ensembl.org/index.html ) (Yates et al., 2022 ). To facilitate efficient secondary analysis, the headers for the corresponding plant genome files were substituted with “chr” followed by the chromosome numbers (Zahra et al., 2021 ). The raw FASTQ reads were processed by Trim Galore v0.6.6 ( https://github.com/FelixKrueger/TrimGalore ) to eliminate the low-quality bases and adapter contaminations (J. Wang et al., 2022 ). The FASTQ data quality comparison was then performed with FastQC v.0.11.5 ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ). tRF Identification and Filtration For each sRNA-Seq sample, the tncRNA toolkit ( https://github.com/skbinfo/tncRNA-Toolkit.git ) was employed to identify the tRFs and their associated tRNAs (Zahra et al., 2023 ). The genomes were indexed using the integrated Bowtie v.1.3.0 tool in this toolkit, and this indexed genome was utilized to further locate the tRFs in the sequencing samples. The outputs from samples of each plant were merged, and tRFs with individual expression values of ≥ 10 per million reads were shortlisted (Alves et al., 2017 ). Unique sequences were subsequently identified, with the criterion that tRFs with identical sequences but differing modification sites were treated as distinct tRFs. The tRFs were named according to their respective species i.e. A. thaliana tRFs were designated as tRFs; O. sativa tRFs were labeled as OStRFs; and S. lycopersicum tRFs were referred to as SLtRFs (Table S1 ). Differential Expression of tRFs A matrix was constructed in R, comprising tRFs and their corresponding read counts across the various samples (Table S1 ). This matrix was utilized as “countData” for DE analysis, with sample names and their descriptions designated as “colData”, as detailed in Table S2 . For the identification of differentially expressed tRFs under various stress conditions, the DESeq2 R package was used (Love et al., 2014 ). Only samples with corresponding treated and control pairs for each condition were considered. The threshold to identify the DE genes was an absolute fold change (|log2FC|) ≥ 1 or ≤ 1, along with an adjusted p-value < 0.05 (K. Kim et al., 2024 ). The bar plots and heatmaps were created using the “ggplot2” package in R. Target Prediction and Functional Enrichment Analysis The highly, differentially expressed tRFs that were present in at least two infections were considered as query for target prediction against the cDNA sequence of the considered plants. The tRFtarget-pipeline v0.3.0 was used to detect the respective transcripts for the tRFs at default parameters ( https://github.com/ZWang-Lab/tRFtarget-pipeline.git ) (N. Li et al., 2021 ). Moreover, it also facilitates the computational calculation of the binding energy between the tRFs and its target transcript (Tan et al., 2024 ). On the basis of minimum free energy (mfe) for each tRFs, top 10 targets were subjected to GO and pathway enrichment followed by further characterization. Functional enrichment of the target genes was done on g:profiler ( https://biit.cs.ut.ee/gprofiler/gost ) (Raudvere et al., 2019 ); Database for Annotation, Visualization, and Integrated Discovery (DAVID) (Dennis et al., 2003 ) (Saif et al., 2022 ); UniProt ( https://www.uniprot.org/uniprotkb/ ) (Bateman et al., 2021 ), and KEGG ( https://www.genome.jp/kegg/ ) (Kanehisa, 2000 ). The significance threshold while using g: profiler was set to Benjamini-Hochberg FDR with a user threshold of 0.05 (Garay-Baquero et al., 2020 ). The DAVID analysis parameters were configured with an EASE score threshold of 0.1, the inclusion of FDR for result display, and a requirement for at least two hub genes in the analysis (Mukherjee & Sudandiradoss, 2021 ). We employed a combination of databases for functional analysis since many tomato genes have not yet been annotated in g: profiler and DAVID. To address this, individual genes were further analyzed using UniProt which also helped in minimizing false positives. Only the most highly enriched terms across the databases were selected for downstream analysis (Saif et al., 2022 ). RNA-Seq Analysis and Co-Expression Profiling of Differentially Expressed Genes The reference genome was indexed using HISAT2 (D. Kim et al., 2019 ), which was also utilized to align the filtered reads to the indexed genome. The aligned reads were assembled into potential transcripts using StringTie(Pertea et al., 2015 ), and the transcript abundance was quantified as FPKM values [total exon fragments/mapped reads (millions) x exon length (kB)]. The number of mapped reads and transcript length in the samples were normalized to accurately reflect each transcript’s expression level and a gene count matrix consisting of raw read counts was constructed (N. Kim et al., 2024 ). To evaluate whether the target genes were upregulated or downregulated during the infection, differential expression analysis was performed using DESeq2, employing the same parameters previously used for the DE tRF study. The volcano plot was generated using the SRplot web server ( https://www.bioinformatics.com.cn/srplot ) (Tang et al., 2023 ). The target genes that were downregulated in the samples were shortlisted and further underwent co-expression analysis. The Venn diagrams were generated using the jvenn web server ( https://jvenn.toulouse.inrae.fr/app/index.html ) (Bardou et al., 2014 ). The Pearson pairwise correlation analysis was conducted on the normalized data obtained from the differential expression study using the "corrplot" and "hclust" R packages. The threshold for the Pearson correlation coefficient was set at ≥ 0.9 between genes with a significance p-value of 0.05 to ensure that only highly co-expressed genes are considered (Pirona et al., 2023 ). The functional correlation of the co-expressed genes in A. thaliana and O.sativa was investigated using ATTED-II ( https://atted.jp/)(Obayashi et al., 2022) and RiceFREND ( https://ricefrend.dna.affrc.go.jp/ ) (Sato et al., 2013 ), respectively. Genes that were positively co-expressed with the target gene and also showed downregulation under the same infection condition in S. lycopersicum were selected for GO enrichment using UniProt. Cytoscape software version 3.10.2 was used to visualize and construct the various biological networks required for the study (Shannon et al., 2003 ). Identification of Potential Transposable Element Targets Details of Transposable Elements (TEs) for the respective organism were obtained from the APTEdb website ( http://apte.cp.utfpr.edu.br/ ) (Pedro et al., 2021 ). Only known and characterized TEs were retained for further analysis. To determine the DE status of the genes, their coordinates were cross-referenced with the DE data. The interaction of the tRFs with the TE was visualized using tRFtarget-pipeline v0.3.0. To categorize the classes of TEs involved in interactions with tRFs, a chord diagram was created using the "circlize" package in R. (H. Wang et al., 2019 ). Detection and Verification of miRNAs for the target mRNA The top 10 targets were shortlisted based on ‘mfe’ of each tRF, and were also examined for potential miRNA interactions. The miRNA information for A. thaliana and O. sativa was sourced from the PMRD database ( https://bioinformatics.cau.edu.cn/PMRD/ ) (Z. Zhang et al., 2010 ) while data for S. lycopersicum was obtained from the TarDB database ( http://www.biosequencing.cn/TarDB/ ) (J. Liu et al., 2021 ) respectively. After conducting the mRNA-miRNA interaction study using the tRFtarget pipeline v0.3.0, the interactions between tRFs and miRNAs with their respective target genes were compared on the basis of binding energy and the location of binding on the mRNA. Subsequently, the presence of the miRNA in the sample was detected using the BrumiR toolkit ( https://github.com/camoragaq/BrumiR ) (Moraga et al., 2022 ). Database Development The data analysis was compiled into a database called “PbtRFdb”. Web development for this database was carried out using the XAMPP package (v.8.0.30) ( https://www.apachefriends.org/ ), which facilitates the integration of Apache, MySQL, and PHP in a local server environment. The front-end web interface was designed using HTML, CSS, and JavaScript. MySQL handled data management and processing, providing a robust platform for storing and efficiently retrieving data. The PHP scripts interacted with MySQL to deliver query results to the user. Results Comprehensive Profiling and Differential Expression of tRFs under biotic stress conditions across three plants Using the tncRNA-toolkit, a total of 277,183 tRFs were identified in A. thaliana , 268,032 in O. sativa , and 92,263 in S. lycopersicum . After an initial filtration based on individual expression level (Fig. S1), and retaining the top 50% of tRFs, a final unique set of 2,505 tRFs for A. thaliana , 3,078 for O. sativa , and 843 for S. lycopersicum was obtained. Based on individual expression levels, other-tRFs categories were more abundant (Table S1). The number of tRFs remaining after each filtration step is shown in Fig. 2. We analyzed the expression of these tRFs under various biotic stress conditions in plants, revealing distinct variations in both expression patterns and the types of tRFs across different groups of biotic stress infections (Fig. S1), and in response to each specific infection (Fig. S2). A significant number of tRFs, across various types, were predominantly upregulated in all the plants (Fig. S3). The classes tRF-5, other-tRFs, and 5' tRH showed significant differential expression. Alongside the stress response, we also observed tissue-specific variations in tRFs expression. Notably, tRF-5 was highly upregulated and downregulated in leaf tissue at different developmental stages under various infections (Fig. 3). The majority of the tRF-5 fragments were found in the developmental stages ranging from mature leaf (21–30 days old) to young leaf (15 days). Additionally, other tRFs were found to be DE across major tissue types under prominent biotic stress conditions, including various developmental stages of leaves and in 15-day-old roots of O. sativa . GluTTC exhibits maximum tRNA cleavage in response to biotic stress All the DE tRFs were analysed for the tRNA origin information. It has been found that the GluTTC generated the highest number of tRFs across various biotic stress conditions in all studied plants (Fig. S4). Different isoacceptors produced various types of tRFs and showed differences between plants (Zahra et al., 2021). Specifically, the isoacceptors of tRNA-Glu i.e., TTC and CTC, contributed to the majority of DE tRF production across all plants, with GluTTC producing the highest amount of tRF-5 in A. thaliana , O. sativa , and S. lycopersicum , respectively (Fig. 4). Other tRFs were also observed to be produced from Glu-TTC in both A. thaliana and S. lycopersicum . Various anticodons contributed to the production of tRF-5 across all plants. For example, in A. thaliana , tRF-5 was also generated by GlyTCC, AspGTC, GlyGCC, and MetCAT, among others. To assess the impact of tRFs generated from GluTTC, their mRNA targets were identified and shortlisted based on binding energy. The identified targets were then subjected to GO enrichment analysis, revealing a strong enrichment for the terms "response to heat" and "cellular component organization" in A. thaliana (Fig. S5). These terms fall under the broader categories of "response to stimulus" and "cellular process". The targets of tRFs generated by GluTTC in O. sativa were found to play a role in “galactose metabolism”, suggesting that these tRFs may regulate genes involved in this metabolic pathway. None of the genes showed enrichment in S. lycopersicum. Enrichment of mRNA targets in distinct stress response mechanisms tRFs may function as post-transcriptional regulators by binding complementarily to their mRNA targets, leading to the degradation of these mRNAs. To explore the functional roles of tRFs, we conducted target predictions (Fig. 5, Table S3) and pathway enrichment analyses, for tRFs expressed in at least two infectious conditions in plants. Our analysis revealed that the majority of target genes were associated with responses to various stress types, including cold stress (abiotic) and bacterial stress (biotic) (Fig. S6 ). The cellular mechanisms activated to cope with stressors, such as "signal transduction", "transport", and various "metabolic processes", were found to be enriched across all plant species analyzed. Within the transport processes, "microtubule-based movement" was particularly prominent. Among biological processes, the term "biosynthetic process" was observed to be the most highly enriched across the plants. Under molecular functions, "catalytic activity" was extensively abundant. Consistent with the findings of Zahra et al., 2021, our tRF targets were also prominently associated with "transferase" and "kinase" activities. Additionally, "binding" was highly enriched in A. thaliana and O. sativa . "ATP binding" was significantly enriched and the most prevalent term, while "microtubule binding" exhibited the highest level of significance. In A. thaliana and O. sativa , the term "membrane" was highly and significantly enriched, whereas in S. lycopersicum , the term "nucleus" was extensively enriched. These findings suggest distinct functional adaptations among plant species, reflecting their specific stress response mechanisms and highlighting the role of tRFs in regulating various biological processes (Fig. S7). Identification of differentially expressed mRNA targets RNA-seq analysis followed by DE gene identification was conducted to examine the expression of target genes in the samples under biotic stress conditions. This approach also aimed to assess the potential influence of tRFs on regulating these genes within the samples. In O. sativa and S. lycopersicum , most of the genes were observed to be downregulated, whereas, in A. thaliana , the majority of the genes were predominantly upregulated (Fig. 6). No common genes were identified across all conditions in all plant species; however, overlapping genes in at least two conditions are evident in Fig. S8. Most of the 76 genes in A. thaliana were found to overlap between those upregulated during Tobacco Mosaic Virus (TMV) infection and those downregulated in response to Turnip Crinkle Virus (TCV). A similar trend was observed in O. sativa and S. lycopersicum , where gene overlaps were detected in at least two infection conditions. In O. sativa , 11 genes predominantly overlapped between those downregulated by Brown Planthopper (BP) infection and Xanthomonas oryzae (XO) infection. Meanwhile, in S. lycopersicum , a substantial overlap of 150 genes was noted between those downregulated during Meloidogyne incognita (MI) infection and those upregulated in response to ToMV. In A. thaliana , the overlapping genes were significantly enriched in pathways related to "intracellular protein transport," "ARF protein signal transduction," and "post-transcriptional gene silencing” (Fig. S9). These terms generally fall under the category of stress response. In O. sativa , "monoterpenoid biosynthesis" was prominently featured, while in S. lycopersicum , the "ubiquitin conjugation pathway" was notably highlighted which also indicated the stress-related process. Upregulated tRFs may contribute to the downregulation of target mRNAs and co-expressed genes, thereby influencing shared biological pathways We observed that the majority of shortlisted mRNA targets were downregulated during DE analysis. With the hypothesis that the upregulated tRF could be a contributing factor to the downregulation of its target mRNA (Zong et al., 2021), we selected the downregulated target mRNAs from the list of DE genes in each plant species. The co-expression analysis revealed that genes co-expressed with the target gene were also downregulated in response to the infection. AT2G39950, a flocculation protein and a potential target mRNA for tRF1492 in A. thaliana , was significantly downregulated during infection with Alternaria brassicicola (AB), showing a log2FC value of -1.66. Additionally, its co-expressed gene AT5G60170 was also found to be downregulated during the same infection, showing a log2FC value of -1.05 (Table S4). The functional analysis revealed that genes AT5G60170 and AT2G28540 both belong to the RNA-binding protein family. At the same time, AT3G22270 is associated with topoisomerase II which may facilitate mRNA decay and enhance post-transcriptional reprogramming https://www.arabidopsis.org/locus?name=AT3G22270 (Fig. 7a). In O. sativa , the gene OsCesA5 (Os03g0837100), which encodes a cellulose synthase A5 protein, was significantly downregulated during the BP infection with a notable negative log2FC value of -3.075. This gene is targeted by OstRF1920 and OstRF1928, which were significantly upregulated during the same infection. All the co-expressed genes, including CESA6 (Os07g0252400), OsGT47A (Os01g0926600), and OsFLA3 (Os08g0321000), were significantly downregulated during BP infection; however, the Os07g0633600 gene did not show any DE in the samples. The CESA6 and OsGT47A are involved in “plant-type cell wall biogenesis” and “polysaccharide biosynthetic process” (Fig. 7b). These genes along with the target mRNA, OsCesA5, play a role in the biosynthesis and modification of the plant cell wall. The other co-expressed gene sets for the mRNA targets OsWD40-125 (Os06g0143900) and Os09g0283600 did not show DE under any of the infectious conditions. In S. lycopersicum , the majority of significant co-expression was observed for the target gene Solyc02g080340.3 with SLtRF725 and SLtRF26 under ToMV infection, specifically in processes related to “fatty acid metabolism and lipid degradation” and “defense response to other organisms”. The target gene was downregulated with a log2FC value of -0.96, while the tRFs were upregulated with log2FC values of 28.054 and 36.855, respectively, during the infection. All the co-expressed genes were downregulated under ToMV stress. Among them, the Solyc01g104210.2 gene is also involved in “fatty acid metabolism (elongation)” and the “sphingolipid biosynthetic process” (Fig. 7c). Two of the co-expressed genes, Solyc06g082340.2 and Solyc12g056130.2 , are involved in ubiquitin-dependent pathways, specifically the ubiquitin-dependent protein catabolic process and the ubiquitin ligase conjugation pathway, respectively. In contrast, most of the remaining genes were not found to be significantly enriched. Moreover, the gene Solyc06g048950.3 , which encodes a non-specific serine/threonine protein kinase (STPK), was significantly downregulated during infection by the Alternaria fungus (AF). This gene showed considerable co-expression with a retrotransposon-derived protein that was also downregulated under the same infection. Additionally, the target gene Solyc01g106770.3 , another STPK-TOR, which is targeted by the upregulated SLtRF723, was downregulated under ToMV infection, along with its co-expressed gene Solyc01g008740.2 , a calcium-dependent kinase involved in intracellular signal transduction. miRNAs did not influence target mRNA expression Figure S10a illustrates that several mRNA transcripts in A. thaliana have the potential to be regulated by specific miRNAs capable of binding to them. In contrast, in O. sativa , two genes were identified to bind to miRNAs, as depicted in Fig. S10b potentially. No miRNAs were found to potentially bind to any predicted target genes in S. lycopersicum . A comparison of the binding energies and binding coordinates between miRNA and tRF reveals that, although both exhibited highly negative binding energies, the binding coordinates on the target gene differed (Table 1). Additionally, these mRNA targets did not show DE under any infection conditions in either A. thaliana or O. sativa . Furthermore, none of these miRNAs were detected in the samples considered in this study. Moreover, this supports the previous hypothesis that the downregulated potential target mRNA and its corresponding co-expressed downregulated mRNA may be DE primarily due to their interaction with the tRFs, which are upregulated during various biotic stresses. tRFs regulate the activity of the transposable elements The chord diagram in Fig. 8 collectively demonstrates that most tRFs across all plant species exhibited a strong affinity for binding to LTR/gypsy TE, supporting the findings of (Zahra et al., 2021). In A. thaliana , tRF-5 was predominantly associated with LTR/gypsy elements, while tRF-3-CCA primarily interacted with SINE/Unknown elements (Table S5). It was observed that TE transcripts were predominantly downregulated during TCV infection, with tRF-5 and 5’tRH showing strong interactions with TE transcripts in O. sativa . Notably, tRF-5 mainly interacted with Helitron/Helitron elements, while 5’tRH was primarily associated with TIR/Dada elements. Significant downregulation of TE transcripts was observed during BP infection, with some downregulation also occurring in XO and Meloidogyne graminicola (MG) infections. In S. lycopersicum , most tRF-5s and other-tRFs showed a preference for interacting with LTR/gypsy elements, with some interaction observed with LTR/Copia and LTR/Unknown elements. The strongest downregulation of TE transcripts was observed during AF infection, with notable downregulation also seen in MI and ToMV infections. This supports the conclusion highlighted in the review article of (Y. Zhang et al., 2024), that tsRNA can regulate cellular responses by affecting the activity of retrotransposons. It was observed that tRNAMetCAT generates tRF-s which specifically targets the LTR retrotransposon, Athila6A (Martinez et al., 2017). Since both tRFs and TEs are known to be expressed and accumulate under stress, it would seem that the regulation of tRF production is connected to TE activity. PbtRF database A total of 637,476 entries are stored in PbtRFdb, comprising 277,181 entries for A. thaliana , 268,032 for O. sativa , and 92,263 for S. lycopersicum . For differentially expressed tRFs, A. thaliana has 2,260 entries, O. sativa has 6,663, and S. lycopersicum has 901 entries. Detailed information on tRFs can be retrieved through two main modules: the “Search” and “Browse” modules. The Search module allows users to extract detailed information based on sequences, tRF types, amino acids, anticodons, modifications, and differentially expressed tRFs. The “DE tRFs” section enables users to query biotic stress-specific studies across the three species, with filters for various stress conditions and expression levels. The Browse module allows users to extract information across the three species via four categories: tissue type, amino acid, anticodon, and stress. Each query provides users with specific details such as SRA accession, organism, tissue, developmental stage, tRF type, locus, position, length, stress, sequence, and modification. The query also provides log2fold change data and expression information for differential expression, including upregulation and downregulation. Each SRA accession is hyperlinked to the NCBI SRA page, offering additional information. Users can visualize tRFs using the “JBrowse” module for the species of interest. The module allows users to select tracks, including the reference sequence, general feature file track (GFF), and tRF track. Users can zoom in on specific locations to view tRFs and gene features. The tRF track includes sequence information, tRF type, tRNA derivatives, and the length of the tRF. PbtRFdb also includes a “Statistics” module, which allows users to visualize tRFs across various stress conditions. The most abundant classes of tRFs and parental tRNAs under different conditions are displayed using graphs. DE tRFs are presented with user-friendly visualizations such as line graphs, bar plots, and pie charts. Additional features include a “Download” module, allowing users to download results for each plant species separately. A “Help” module is also provided to guide users through navigating the database. Discussion Our study provides a comprehensive profile of tRFs in response to various biotic stresses and examines their potential roles in regulating the mRNA target, TEs, and miRNA activity amongst three species: Arabidopsis thaliana , Oryza sativa , and Solanum lycopersicum . Among the identified tRF subclasses, a notable abundance of other-tRFs was observed, with significant differential expression observed in the tRF-5, other-tRFs, and 5’tRH classes under stress conditions. This aligns with findings by Thompson et al., 2008 , which demonstrated that oxidative stress induces the endonucleolytic cleavage of tRNAs in plants. Similarly, Alves et al., 2017 highlighted the association of tRF-5 with distinct plant AGOs, supporting its production under abiotic stress. The higher prevalence of other-tRFs during our initial identification suggests that these less-defined tRFs may play critical roles in mediating plant responses to various biotic stressors. The data highlights the cleavage of tRNA-GluTTC, a well-known stress-responsive biomarker, as a key produces of tRFs involved in both biotic and abiotic responses, paralleling findings in human cancers, such as gastric and thyroid cancers (Mao et al., 2024 ; Shan et al., 2021 ; Zhu, Li, et al., 2019 ). In plants, tRNA-GluTTC cleavage is significantly elevated under biotic stress across all the studied species, corroborating the findings of Zahra et al., 2021 for Oryza sativa , and Solanum lycopersicum . Notably, tRNA-GluTTC generated the highest levels of tRF-5 across all species. Enrichment analyses revealed that tRFs derived from tRNA-GluTTC were associated with heat stress and cellular processes for Arabidopsis thaliana , while in Oryza sativa , they were linked with galactose metabolism. Heat stress-induced tRF accumulation, documented across multiple plant species, (Y. Wang et al., 2016 ; Zahra et al., 2021 ), underscores their role in the stress-response signaling pathway (Huang & Hopper, 2016 ). Furthermore, in Oryza sativa , abiotic stress was closely associated with the activation of galactose metabolism pathways, suggesting potential stress-adaptive mechanisms (H. Wang et al., 2024 ). These findings position tRNA-GluTTC-derived crucial as key modulators of stress-response pathways, emphasizing the need for further investigation into their mRNA targeting mechanisms. Evidence suggests that tRFs play a role in influencing mRNA degradation (Alves et al., 2017 ; Li et al., 2023 ; C. Wang et al., 2023 ). Our findings reveal that tRFs-mRNA are enriched in stress-response mechanisms such as signal transduction, metabolic process, and catalytic activity. Stress-induced intracellular remodeling in plants, (Yagyu & Yoshimoto, 2024 ) appears to impact intracellular protein transport, as highlighted by the GO enrichment analysis for Arabidopsis thaliana . This aligns with studies emphasizing the importance of post-transcriptional gene regulation in abiotic stress responses (Floris et al., 2009 ), further corroborated by the enrichment of the monoterpenoid biosynthesis pathway identified in GO analysis for Oryza sativa. Additionally, the ubiquitin-26S proteasome system (UPS), in conjunction with abscisic acid (ABA), has been implicated in modulating abiotic stress responses (Doroodian & Hua, 2021 ) supporting the enrichment of the ubiquitin conjugation pathway observed in Solanum lycopersicum. Collectively, these insights emphasize the multifaceted role of tRFs in regulating stress-adaptive mechanisms through targeted mRNA interactions and gene regulation pathways across different plant species. Recent studies suggest that tRFs may influence mRNA transport, with potential implications for transgenerational memory (George et al., 2022 ). Li et al., 2023 reported that tRF-Ala interacts with splicing factor, SR34 weakened the binding ability of SR34 demonstrating the regulation of target mRNAs by tRF-Ala. In our study, we observed that in Arabidopsis thaliana , the target gene AT2G39950 alongside its co-expressed AT5G60170, was downregulated during Alternaria brassicicola infection. Similarly, in Oryza sativa , gene OsCesA5 and its co-expressed genes, were downregulated during Brown planthopper infection. Solanum lycopersicum gene Solyc02g080340.3 and its co-expressed genes were downregulated during Meloidogyne incognita and upregulated during ToMV infection. We hypothesize that upregulated tRFs may mediate the targeted downregulation of mRNAs and associated genes to shared stress-related pathways. This regulatory interplay between tRFs and mRNAs highlights their potential as key players in plant stress adaptation. The role of miRNAs in regulating mRNA stability is well-documented (Dalmay, 2013 ). Pioneering studies in Arabidopsis thaliana revealed that miRNAs target transcription factors mRNAs (Bartel, 2004 ). In our investigation of miRNA-mediated regulation of target mRNAs, we identified that Arabidopsis thaliana , gene AT1G05580, targeted by the stress-related ath-miR414 participates in the phosphatidylinositol signaling system (Xie et al., 2015 ). Additionally, the gene AT3G0200, targeted by ath-miR163, is potentially linked to adaptations in nutrient-deficient environments (Fasani et al., 2021 ). In Oryza sativa , the gene Os05g0590000, targeted by osa-miR2103, was found to play a critical role in rice immunity against Magnaporthe oryzae (Fan et al., 2020 ). Despite these insights, our analysis did not detect miRNAs in the samples, suggesting that tRF-mediated mRNA downregulation may function independently of miRNAs. This observation highlights the potential of tRFs as distinct regulatory molecules in mRNA targeting, operating alongside or as an alternative to miRNA-based mechanisms. The interaction between tRFs and TEs has been explored in only a few studies across both plants and mammals (Martinez, 2018 ; Slotkin et al., 2009 ; Zahra et al., 2021 ). an interact with transcripts, altering expression levels by acting as novel promoters or influencing regulatory pathways (Hirsch & Springer, 2017). In our data, diverse TE families were observed in different species. In Arabidopsis thaliana , LTR-Gypsy and SINE/Unknown families were identified, with LTR-Gypsy associated with heat stress responses (Pietzenuk et al., 2016 ). Oryza sativa exhibited Helitron/Helitron and TIR/Dada elements, while Solanum lycopersicum showed the presence of LTR/Gypsy, LTR/Copia, and LTR/Unknown elements. Notably, Gypsy and Copia elements are known to be responsive to various stress conditions, contributing to adaptive responses under adverse environmental conditions (Bolger et al., 2014 ). These findings suggest a potential regulatory interplay between tRFs and TE activity, particularly under stress conditions. Furthermore, we developed a database to catalog and analyze tRF profiles across Arabidopsis thaliana , Oryza sativa , and Solanum lycopersicum , enabling detailed exploration of tRFs in various biotic stresses. This resource provides a platform for future research into tRF-mediated regulatory mechanisms under biotic stress conditions. Our in-silico analysis provides valuable insights into the regulatory functions of tRNA-derived fragments in response to biotic stress in plants. The identification of distinct tRFs profiles along with their potential interactions with mRNA targets and transposable elements, underscores the complexity and specificity of stress response pathways across different plant species. These findings highlight the potential role of tRFs in modulating cellular responses to stress. However, further experimental validation and in-depth mechanistic studies are required to confirm these interactions and elucidate the molecular pathways through which tRFs contribute to cellular adaptation under biotic stress conditions Conclusion tRFs are small non-coding RNAs that act as novel regulators of gene expression, impacting both transcriptional and post-transcriptional processes (Martinez et al., 2017 ). Numerous studies have characterized tRFs in fungi, viruses, and plants (Asha & Soniya, 2016 ; Gu et al., 2022 ; Gupta et al., 2018 ; Zahra et al., 2021 , 2022 ). Our study focuses on tRFs in response to biotic stress across Arabidopsis thaliana , Oryza sativa , and Solanum lycopersicum , highlighting their regulating role of mRNA targets, miRNA pathways, and transposable elements. Notably, our study found tRNA-GluTTC to be abundantly cleaved and differentially expressed under biotic stress conditions, with distinct roles in stress-response pathways, including intracellular protein transport, signal transduction, and metabolic processes. The tRFs demonstrated the interaction with mRNA targets essential for the plant's adaption to stress, suggesting that differential expression of tRFs may influence mRNA regulation. Additionally, tRF-mediated stress regulatory mechanisms may act independently of miRNA pathways, providing insights into post-transcriptional gene silencing. The association of tRFs and TEs can lead to a complex regulatory network influencing stress adaptation. The database supporting this study is available at http://www.nipgr.ac.in/PbtRFdb , providing a valuable resource for further research in tRFs in plant biotic stress response. Declarations Competing interests The authors have no conflicts of interest to declare. Availability of data and materials All the fusion data generated is available in a database named ‘PBtRFDB’, http://www.nipgr.ac.in/PbtRFdb. Funding This research is supported by the BT/PR40146/BTIS/137/4/2020, BT/PR40169/BTIS/137/71/2023, BT/PR40160/BTIS/137/64/2023, BT/PR40261/ BTIS/137/55/2023 project grants by Department of Biotechnology (DBT), Government of India and by the core grant of the National Institute of Plant Genome Research (NIPGR) in the laboratory of SK. Author contributions Conceptualization: SPS Data curation: SPS and NB Formal analysis: SPS and NB Web-interface development: NB Funding acquisition, Project administration, Resources, and Supervision: SK Roles/Writing - Original draft: AA, SA, and FH; and Writing - review & editing: SK. Acknowledgments The authors are thankful to the Department of Biotechnology (DBT)-eLibrary Consortium, India, for providing access to e-resources. 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PtncRNAdb: plant transfer RNA-derived non-coding RNAs (tncRNAs) database. 3 Biotech , 12 (5), 105. https://doi.org/10.1007/s13205-022-03174-7 Zahra, S., Singh, A., & Kumar, S. (2023). tncRNA Toolkit: A pipeline for convenient identification of RNA (tRNA)-derived non-coding RNAs. MethodsX , 10 , 101991. https://doi.org/10.1016/j.mex.2022.101991 Zahra, S., Singh, A., Poddar, N., & Kumar, S. (2021). Transfer RNA-derived non-coding RNAs (tncRNAs): Hidden regulation of plants’ transcriptional regulatory circuits. Computational and Structural Biotechnology Journal , 19 , 5278–5291. https://doi.org/10.1016/j.csbj.2021.09.021 Zhang, Y., Gu, X., Li, Y., Huang, Y., & Ju, S. (2024). Multiple regulatory roles of the transfer RNA-derived small RNAs in cancers. Genes & Diseases , 11 (2), 597–613. https://doi.org/10.1016/j.gendis.2023.02.053 Zhang, Z., Yu, J., Li, D., Zhang, Z., Liu, F., Zhou, X., Wang, T., Ling, Y., & Su, Z. (2010). PMRD: plant microRNA database. Nucleic Acids Research , 38 (suppl_1), D806–D813. https://doi.org/10.1093/nar/gkp818 Zhao, R., Yang, Z., Zhao, B., Li, W., Liu, Y., Chen, X., Cao, J., Zhang, J., Guo, Y., Xu, L., Wang, J., Sun, Y., Liu, M., & Tian, L. (2023). A novel tyrosine tRNA-derived fragment, tRFTyr, induces oncogenesis and lactate accumulation in LSCC by interacting with LDHA. Cellular & Molecular Biology Letters , 28 (1), 49. https://doi.org/10.1186/s11658-023-00463-8 Zhu, L., Ge, J., Li, T., Shen, Y., & Guo, J. (2019). tRNA-derived fragments and tRNA halves: The new players in cancers. Cancer Letters , 452 , 31–37. https://doi.org/10.1016/j.canlet.2019.03.012 Zhu, L., Li, T., Shen, Y., Yu, X., Xiao, B., & Guo, J. (2019). Using tRNA halves as novel biomarkers for the diagnosis of gastric cancer. Cancer Biomarkers , 25 (2), 169–176. https://doi.org/10.3233/CBM-182184 Zong, T., Yang, Y., Zhao, H., Li, L., Liu, M., Fu, X., Tang, G., Zhou, H., Aung, L. H. H., Li, P., Wang, J., Wang, Z., & Yu, T. (2021). tsRNAs: Novel small molecules from cell function and regulatory mechanism to therapeutic targets. Cell Proliferation , 54 (3). https://doi.org/10.1111/cpr.12977 Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx SupplementaryFigures.docx Cite Share Download PDF Status: Published Journal Publication published 25 Mar, 2025 Read the published version in Functional & Integrative Genomics → Version 1 posted Editorial decision: Revision requested 09 Feb, 2025 Reviews received at journal 08 Feb, 2025 Reviews received at journal 08 Feb, 2025 Reviews received at journal 06 Feb, 2025 Reviewers agreed at journal 19 Jan, 2025 Reviewers agreed at journal 18 Jan, 2025 Reviewers agreed at journal 16 Jan, 2025 Reviewers invited by journal 16 Jan, 2025 Editor assigned by journal 14 Jan, 2025 Submission checks completed at journal 14 Jan, 2025 First submitted to journal 12 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-5813390","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402268462,"identity":"7cff87fc-e612-438e-9701-f6ec3961864e","order_by":0,"name":"Supriya P. Swain","email":"","orcid":"","institution":"National Institute of Plant Genome Research","correspondingAuthor":false,"prefix":"","firstName":"Supriya","middleName":"P.","lastName":"Swain","suffix":""},{"id":402268463,"identity":"06ddaf9d-53f4-426c-9cb3-15aedd9d4e6e","order_by":1,"name":"Niyati Bisht","email":"","orcid":"","institution":"National Institute of Plant Genome Research","correspondingAuthor":false,"prefix":"","firstName":"Niyati","middleName":"","lastName":"Bisht","suffix":""},{"id":402268464,"identity":"07db3742-c234-4602-9ba1-61f0514ce16b","order_by":2,"name":"Shailesh Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCQaGAw8YGBL4GRgboEJwBh4tCUAtkg2kaGEAaTE4QKy75Gc3PzyQUFGXZ3y7ue3h1z0M8vwNzG0P8GkxuHPM4EDCmcPFZncOthvLPGMwnHGAsd0ArxYJoJMS2w4kbruR2CYtcYCBcQMDY5sEXofNSP9wIPFfXeLmGRAt9gS1MNzIAdrSwJy4QSKxTfLDAYZEgloMbuQUHEg4djhxxo3EdmOGAxLJMw4TdtjmDx9q6hL7Z6Q/e/jjgI1tf3v7M/wOQwJszDygaGImVj1IC+MPElSPglEwCkbByAEAYPNSPdhmvIAAAAAASUVORK5CYII=","orcid":"","institution":"National Institute of Plant Genome Research","correspondingAuthor":true,"prefix":"","firstName":"Shailesh","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2025-01-12 11:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5813390/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5813390/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10142-025-01576-3","type":"published","date":"2025-03-25T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73859196,"identity":"5f5c7aba-7f02-4bfd-80fc-67f8f9d74953","added_by":"auto","created_at":"2025-01-15 10:39:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3877747,"visible":true,"origin":"","legend":"\u003cp\u003eClassification and biogenesis of tRNA-derived fragments (tRFs). These fragments are generated through specific cleavage mechanisms involving enzymes such as DICER and angiogenin (ANG). tRFs are produced under various biotic conditions, including those caused by nematodes, fungi, viruses, bacteria, and insects, which influence tRNA cleavage and tRF production. The resulting tRFs play diverse biological roles, such as association with RISC complexes, regulation of translation, and apoptosis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/1286ceaa5f4bbaadec050222.png"},{"id":73859189,"identity":"9f5a9fa7-b448-4872-a691-4a4492c63ca9","added_by":"auto","created_at":"2025-01-15 10:39:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4859728,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow for the Identification and Analysis of tRNA-derived Fragments (tRFs).\u003cem\u003e \u003c/em\u003eThe workflow outlines a systematic pipeline for identifying and analyzing tRFs under biotic conditions. Initial datasets consist of 112 samples across 13 biotic conditions for \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, 188 samples across 8 biotic conditions for \u003cem\u003eOryza sativa\u003c/em\u003e, and 44 samples across 5 biotic conditions for \u003cem\u003eSolanum lycopersicum\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/0a14e8d8f0eb5c88b59f34ce.png"},{"id":73859192,"identity":"f3175433-06d4-415c-9cef-e6bf2d44e404","added_by":"auto","created_at":"2025-01-15 10:39:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3081581,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap representation of the abundance of tRFs subclasses viz. 3’tRH, 5’tRH, other-tRF, leader-tRF, tRF-1, tRF-3, tRF-3-CCA, tRF-5 and 3’tRH-CCA across tissue types under biotic stress conditions. The heatmaps illustrate the upregulation and downregulation patterns of tRFs under various biotic stress conditions in three species: (a) \u003cem\u003eArabidopsis thalian\u003c/em\u003ea: (i) Upregulated tRFs and (ii) Downregulated tRFs. (b) \u003cem\u003eOryza sativa\u003c/em\u003e: (i) Upregulated tRFs and (ii) Downregulated tRFs. (c) \u003cem\u003eSolanum lycopersicum\u003c/em\u003e: (i) Upregulated tRFs and (ii) Downregulated tRFs.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/3edc71e6600159fb7496ca2e.png"},{"id":73859425,"identity":"b9ba10be-de55-48ce-8216-da7f1dbe5751","added_by":"auto","created_at":"2025-01-15 10:47:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1962377,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap representation of the abundance of tRFs subclasses viz. 3’tRH, 5’tRH, other-tRF, leader-tRF, tRF-1, tRF-3, tRF-3-CCA, tRF-5 and 3’tRH-CCA, originating from each tRNA issoacceptors (specified using their respective anticodon) in three species: (a) \u003cem\u003eArabidopsis thalian\u003c/em\u003ea, (b) \u003cem\u003eOryza sativa \u003c/em\u003eand (c) \u003cem\u003eSolanum lycopersicum\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/47e3adf9550095928e9c530c.png"},{"id":73859426,"identity":"cd9f7bdb-f4a7-46aa-a488-2261010256a2","added_by":"auto","created_at":"2025-01-15 10:47:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4829028,"visible":true,"origin":"","legend":"\u003cp\u003emRNA targets of the most recurrent tRFs identified through tRFtarget-pipeline v0.3.0 (https://github.com/ZWang-Lab/tRFtarget-pipeline.git). The figure highlights the top three mRNA targets for each species with their minimum free energy (mfe): (a) \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, (b) \u003cem\u003eOryza sativa\u003c/em\u003e, and (c) \u003cem\u003eSolanum lycopersicum\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/69a5d3f38afccd8ccf7c3ce9.png"},{"id":73859429,"identity":"798a1f0f-365c-42c8-ab59-6a82070ae25b","added_by":"auto","created_at":"2025-01-15 10:47:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1692825,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plots showing the gene expression in response to biotic stress conditions. The plots illustrate the upregulation and downregulation of genes for each species: (a) \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, (b) \u003cem\u003eOryza sativa\u003c/em\u003e, and (c) \u003cem\u003eSolanum lycopersicum\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/acb9c49e9fa8fa5d7cdfee4b.png"},{"id":73859188,"identity":"b4f2b3b9-314e-48c2-8cd8-68d57960c056","added_by":"auto","created_at":"2025-01-15 10:39:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2848764,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating the co-expression of mRNA targets with other genes and their functional annotation for each species: (a) \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, (b) \u003cem\u003eOryza sativa\u003c/em\u003e, and (c) \u003cem\u003eSolanum lycopersicum\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/8d9f085bccac036096fc0651.png"},{"id":73859199,"identity":"824fa19a-5bba-4168-ae3c-b969904d80ca","added_by":"auto","created_at":"2025-01-15 10:39:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":11407478,"visible":true,"origin":"","legend":"\u003cp\u003eChord diagram shows that the majority of tRFs across all plant species display a strong binding affinity to transposable elements, LTR/Gypsy TEs.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/810291fadf253dbfaec1f1a6.png"},{"id":79604907,"identity":"7f59ecd0-e0f8-4266-8f6c-40f814ac70e5","added_by":"auto","created_at":"2025-03-31 16:08:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":32544429,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/ac010f50-ce78-4e1e-8e74-496e5682e47a.pdf"},{"id":73859423,"identity":"b2a3d9e1-d086-4df3-85c3-f637c61bb66b","added_by":"auto","created_at":"2025-01-15 10:47:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18547,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/e40bb46e02d1940ba7fea487.docx"},{"id":73859424,"identity":"ea68f17a-eddc-4700-99ff-7f144ec36981","added_by":"auto","created_at":"2025-01-15 10:47:42","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3052586,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/794eaedde611b2352b0817f5.xlsx"},{"id":73860905,"identity":"f41beb4b-5273-465e-95a0-c8397b777a5b","added_by":"auto","created_at":"2025-01-15 10:55:43","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":26607,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/71100bd507935ff691faae2e.xlsx"},{"id":73859204,"identity":"1fdfd1df-0377-499e-8418-1ffef489900e","added_by":"auto","created_at":"2025-01-15 10:39:43","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":511596,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/6495038bbb81f067d211a688.xlsx"},{"id":73859207,"identity":"c8c3cb3e-4e72-459c-947d-37daf692521b","added_by":"auto","created_at":"2025-01-15 10:39:43","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11752,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/9f5fc8180791a4e34f8a0cf3.xlsx"},{"id":73859208,"identity":"8e851944-1ef5-4c9f-8465-4645041f082c","added_by":"auto","created_at":"2025-01-15 10:39:43","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":40625,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/79fa45adc15b0c253af72039.xlsx"},{"id":73859435,"identity":"3f97b823-6806-4d62-b385-73fb297258ad","added_by":"auto","created_at":"2025-01-15 10:47:44","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":3283831,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5813390/v1/51b8acf78336af1ec2472ae5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative Analysis of tRNA-Derived Fragments in Plant Adaptation to Biotic Stress: A Comparative Study and Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe majority of the human genome encodes for non-coding RNAs (ncRNAs) which are now extensively studied for their crucial roles in regulating gene expression and genome organization (Nemeth et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The endonucleolytic cleavage of precursor tRNA (pre-tRNA) or mature tRNAs, which constitute 4\u0026ndash;10% of total cellular RNA, results the formation of a pool of tRNA-derived fragments (Lee et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It constitutes the short fragments of length 14 to 30 nucleotides (nt) called tRNA derived fragments (tRFs or tDRs), longer tRNA halves (tRHs or tiRNAs) of approximately 30 to 50 nt (Sun et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhu, Ge, et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and other tRFs from the internal region of tRNAs. All the classes termed as tRNA-derived non-coding RNAs (tncRNAs) are conserved across most organisms.\u003c/p\u003e \u003cp\u003eThese genome regulatory molecules are further classified based on the tRNA from which they are derived. The tRFs derived from the 5' end are tRF-5, and the 3' end generates tRF-3, resulting from cleavage in the D and T regions, respectively. Additionally, pre-tRNA generates 5' U-tRFs (leader tRF) and tRF-1/tsRNA (30 U-tRFs) from the 5' leader and 3' trailer regions, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the case of tRNA halves, 5' and 3' tRHs are produced from cleavage in the anticodon region, containing the 5' and 3' portions of the mature tRNA. Therefore, the identified tRFs are classified into tRF-5, tRF-3, tRF-1, leader-tRF, 5' tRH, 3' tRH, and a miscellaneous group labeled as other tRFs. The originating tRNA type, cell type, developmental stage, and stress conditions influence the specific cleavage of tRNA that generates these fragments. (Zahra et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These tRFs can be produced by both DICER-dependent and -independent manner. RNase P eliminates the leader sequence and RNase Z cleaves the trailer sequence precisely at the discriminator base of the precursor-tRNA leading to the production of leader-tRF and tRF-1/tsRNA, respectively (Hak Kyun Kim, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Processing of certain types of tRNA halves requires both Dicer and angiogenin (Liu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The regulatory role of tRFs includes, 1) regulation of mRNA stability as well as their potential to induce mRNA cleavage (Yu et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), 2) translation inhibition (Kumar et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), 3) regulation of ribosome production (H. K. Kim et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), 4) control on apoptosis of cells depending on the diseases and target mRNA (Cui et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and 5) facilitates amplification of viral propagation (Fu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExploration of tRFs in plants has been growing rapidly due to the exposure of plants to various biotic and abiotic stresses, which significantly impact their development (C. Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To manage these challenging conditions, plants have developed complex response mechanisms that involve multiple gene regulatory processes, including transcriptional (Chang et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and post-transcriptional regulation (Jones-Rhoades et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). It is shown that tRFs in plants operate similarly to those in animals which could be due to their similarity in biogenesis, suggesting an evolutionary connection (Cao et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lalande et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The studies on tRFs related to biotic stresses are relatively less developed compared to research on abiotic stresses (C. Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Their functional roles were thoroughly investigated in both the leaves and roots of black pepper affected by \u003cem\u003ePhytophthora capsici\u003c/em\u003e (Asha \u0026amp; Soniya, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In \u003cem\u003eA. thaliana\u003c/em\u003e, \u003cem\u003eBotrytis cinerea\u003c/em\u003e infection was seen to be significantly downregulating the 5\u0026rsquo; tRNA-derived small RNAs (Gu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) whereas in \u003cem\u003eSolanum lycopersicum\u003c/em\u003e, differentially expressed tRFs were characterized in plants subjected to \u003cem\u003eTomato Mosaic Virus\u003c/em\u003e (ToMV) (Zahra et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Identification and characterization of tRFs and their specific possible targets in plants, particularly under biotic stress conditions, alongside an in-depth understanding of tRF-guided stress regulatory networks, can greatly contribute to enduring agricultural production. In this study, we identified a diverse range of tRFs across three angiosperms viz. \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, \u003cem\u003eOryza sativa\u003c/em\u003e, and \u003cem\u003eSolanum lycopersicum\u003c/em\u003e using publicly available small RNA-seq (sRNA-seq) datasets of plants under various biotic stresses. Differential expression (DE) analysis revealed stress-specific tRFs, and common tRNA sources, followed by their mRNA targets, complemented by functional enrichment and pathway analysis. Analysis of RNA-Seq datasets further highlighted the DE patterns of mRNA targets, and interactions between tRFs and transposable elements, offering insights into their roles in stress adaptation. These findings were integrated into PbtRFdb, a comprehensive database with advanced search features, visualization tools, and dynamic graphs for exploring tRFs under biotic stress conditions. An overview of each step followed during the study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Retrieval and Processing\u003c/h2\u003e \u003cp\u003eA total of 112, 188, and 44 sRNA-seq samples of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, \u003cem\u003eOryza sativa\u003c/em\u003e, and \u003cem\u003eSolanum lycopersicum\u003c/em\u003e, sequenced under various biotic stress conditions, were downloaded from the NCBI SRA Toolkit (v3.0.2) (S. Sherry et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Genome files for the respective plants viz. \u003cem\u003eArabidopsis thaliana\u003c/em\u003e (TAIR10.1), \u003cem\u003eOryza sativa japonica\u003c/em\u003e (Build 4.0; organellar: IRGSP-1.0), and \u003cem\u003eSolanum lycopersicum\u003c/em\u003e (nuclear: SL4.0; organellar: SL3.0), were obtained from EnsemblPlants (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plants.ensembl.org/index.html\u003c/span\u003e\u003cspan address=\"https://plants.ensembl.org/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Yates et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To facilitate efficient secondary analysis, the headers for the corresponding plant genome files were substituted with \u0026ldquo;chr\u0026rdquo; followed by the chromosome numbers (Zahra et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The raw FASTQ reads were processed by Trim Galore v0.6.6 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/FelixKrueger/TrimGalore\u003c/span\u003e\u003cspan address=\"https://github.com/FelixKrueger/TrimGalore\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to eliminate the low-quality bases and adapter contaminations (J. Wang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The FASTQ data quality comparison was then performed with FastQC v.0.11.5 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003etRF Identification and Filtration\u003c/h3\u003e\n\u003cp\u003eFor each sRNA-Seq sample, the tncRNA toolkit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/skbinfo/tncRNA-Toolkit.git\u003c/span\u003e\u003cspan address=\"https://github.com/skbinfo/tncRNA-Toolkit.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to identify the tRFs and their associated tRNAs (Zahra et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The genomes were indexed using the integrated Bowtie v.1.3.0 tool in this toolkit, and this indexed genome was utilized to further locate the tRFs in the sequencing samples. The outputs from samples of each plant were merged, and tRFs with individual expression values of \u0026ge;\u0026thinsp;10 per million reads were shortlisted (Alves et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Unique sequences were subsequently identified, with the criterion that tRFs with identical sequences but differing modification sites were treated as distinct tRFs. The tRFs were named according to their respective species i.e. \u003cem\u003eA. thaliana\u003c/em\u003e tRFs were designated as tRFs; \u003cem\u003eO. sativa\u003c/em\u003e tRFs were labeled as OStRFs; and \u003cem\u003eS. lycopersicum\u003c/em\u003e tRFs were referred to as SLtRFs (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDifferential Expression of tRFs\u003c/h3\u003e\n\u003cp\u003eA matrix was constructed in R, comprising tRFs and their corresponding read counts across the various samples (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This matrix was utilized as \u0026ldquo;countData\u0026rdquo; for DE analysis, with sample names and their descriptions designated as \u0026ldquo;colData\u0026rdquo;, as detailed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. For the identification of differentially expressed tRFs under various stress conditions, the DESeq2 R package was used (Love et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Only samples with corresponding treated and control pairs for each condition were considered. The threshold to identify the DE genes was an absolute fold change (|log2FC|)\u0026thinsp;\u0026ge;\u0026thinsp;1 or \u0026le;\u0026thinsp;1, along with an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (K. Kim et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The bar plots and heatmaps were created using the \u0026ldquo;ggplot2\u0026rdquo; package in R.\u003c/p\u003e\n\u003ch3\u003eTarget Prediction and Functional Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eThe highly, differentially expressed tRFs that were present in at least two infections were considered as query for target prediction against the cDNA sequence of the considered plants. The tRFtarget-pipeline v0.3.0 was used to detect the respective transcripts for the tRFs at default parameters (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ZWang-Lab/tRFtarget-pipeline.git\u003c/span\u003e\u003cspan address=\"https://github.com/ZWang-Lab/tRFtarget-pipeline.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (N. Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, it also facilitates the computational calculation of the binding energy between the tRFs and its target transcript (Tan et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the basis of minimum free energy (mfe) for each tRFs, top 10 targets were subjected to GO and pathway enrichment followed by further characterization. Functional enrichment of the target genes was done on g:profiler (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biit.cs.ut.ee/gprofiler/gost\u003c/span\u003e\u003cspan address=\"https://biit.cs.ut.ee/gprofiler/gost\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Raudvere et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Database for Annotation, Visualization, and Integrated Discovery (DAVID) (Dennis et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) (Saif et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); UniProt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/uniprotkb/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/uniprotkb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Bateman et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and KEGG (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Kanehisa, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The significance threshold while using g: profiler was set to Benjamini-Hochberg FDR with a user threshold of 0.05 (Garay-Baquero et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The DAVID analysis parameters were configured with an EASE score threshold of 0.1, the inclusion of FDR for result display, and a requirement for at least two hub genes in the analysis (Mukherjee \u0026amp; Sudandiradoss, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We employed a combination of databases for functional analysis since many tomato genes have not yet been annotated in g: profiler and DAVID. To address this, individual genes were further analyzed using UniProt which also helped in minimizing false positives. Only the most highly enriched terms across the databases were selected for downstream analysis (Saif et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eRNA-Seq Analysis and Co-Expression Profiling of Differentially Expressed Genes\u003c/h3\u003e\n\u003cp\u003eThe reference genome was indexed using HISAT2 (D. Kim et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which was also utilized to align the filtered reads to the indexed genome. The aligned reads were assembled into potential transcripts using StringTie(Pertea et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and the transcript abundance was quantified as FPKM values [total exon fragments/mapped reads (millions) x exon length (kB)]. The number of mapped reads and transcript length in the samples were normalized to accurately reflect each transcript\u0026rsquo;s expression level and a gene count matrix consisting of raw read counts was constructed (N. Kim et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To evaluate whether the target genes were upregulated or downregulated during the infection, differential expression analysis was performed using DESeq2, employing the same parameters previously used for the DE tRF study. The volcano plot was generated using the SRplot web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn/srplot\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn/srplot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Tang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The target genes that were downregulated in the samples were shortlisted and further underwent co-expression analysis. The Venn diagrams were generated using the jvenn web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jvenn.toulouse.inrae.fr/app/index.html\u003c/span\u003e\u003cspan address=\"https://jvenn.toulouse.inrae.fr/app/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Bardou et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The Pearson pairwise correlation analysis was conducted on the normalized data obtained from the differential expression study using the \"corrplot\" and \"hclust\" R packages. The threshold for the Pearson correlation coefficient was set at \u0026ge;\u0026thinsp;0.9 between genes with a significance p-value of 0.05 to ensure that only highly co-expressed genes are considered (Pirona et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The functional correlation of the co-expressed genes in \u003cem\u003eA. thaliana\u003c/em\u003e and \u003cem\u003eO.sativa\u003c/em\u003e was investigated using ATTED-II (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://atted.jp/)(Obayashi et al., 2022)\u003c/span\u003e\u003cspan address=\"https://atted.jp/)(Obayashi et al., 2022)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and RiceFREND (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ricefrend.dna.affrc.go.jp/\u003c/span\u003e\u003cspan address=\"https://ricefrend.dna.affrc.go.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Sato et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), respectively. Genes that were positively co-expressed with the target gene and also showed downregulation under the same infection condition in \u003cem\u003eS. lycopersicum\u003c/em\u003e were selected for GO enrichment using UniProt. Cytoscape software version 3.10.2 was used to visualize and construct the various biological networks required for the study (Shannon et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Potential Transposable Element Targets\u003c/h2\u003e \u003cp\u003eDetails of Transposable Elements (TEs) for the respective organism were obtained from the APTEdb website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://apte.cp.utfpr.edu.br/\u003c/span\u003e\u003cspan address=\"http://apte.cp.utfpr.edu.br/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Pedro et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Only known and characterized TEs were retained for further analysis. To determine the DE status of the genes, their coordinates were cross-referenced with the DE data. The interaction of the tRFs with the TE was visualized using tRFtarget-pipeline v0.3.0. To categorize the classes of TEs involved in interactions with tRFs, a chord diagram was created using the \"circlize\" package in R. (H. Wang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDetection and Verification of miRNAs for the target mRNA\u003c/h3\u003e\n\u003cp\u003eThe top 10 targets were shortlisted based on \u0026lsquo;mfe\u0026rsquo; of each tRF, and were also examined for potential miRNA interactions. The miRNA information for \u003cem\u003eA. thaliana\u003c/em\u003e and \u003cem\u003eO. sativa\u003c/em\u003e was sourced from the PMRD database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.cau.edu.cn/PMRD/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.cau.edu.cn/PMRD/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Z. Zhang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) while data for \u003cem\u003eS. lycopersicum\u003c/em\u003e was obtained from the TarDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.biosequencing.cn/TarDB/\u003c/span\u003e\u003cspan address=\"http://www.biosequencing.cn/TarDB/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (J. Liu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) respectively. After conducting the mRNA-miRNA interaction study using the tRFtarget pipeline v0.3.0, the interactions between tRFs and miRNAs with their respective target genes were compared on the basis of binding energy and the location of binding on the mRNA. Subsequently, the presence of the miRNA in the sample was detected using the BrumiR toolkit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/camoragaq/BrumiR\u003c/span\u003e\u003cspan address=\"https://github.com/camoragaq/BrumiR\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Moraga et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDatabase Development\u003c/h3\u003e\n\u003cp\u003eThe data analysis was compiled into a database called \u0026ldquo;PbtRFdb\u0026rdquo;. Web development for this database was carried out using the XAMPP package (v.8.0.30) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.apachefriends.org/\u003c/span\u003e\u003cspan address=\"https://www.apachefriends.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which facilitates the integration of Apache, MySQL, and PHP in a local server environment. The front-end web interface was designed using HTML, CSS, and JavaScript. MySQL handled data management and processing, providing a robust platform for storing and efficiently retrieving data. The PHP scripts interacted with MySQL to deliver query results to the user.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eComprehensive Profiling and Differential Expression of tRFs under biotic stress conditions across three plants\u003c/h2\u003e\n \u003cp\u003eUsing the tncRNA-toolkit, a total of 277,183 tRFs were identified in \u003cem\u003eA. thaliana\u003c/em\u003e, 268,032 in \u003cem\u003eO. sativa\u003c/em\u003e, and 92,263 in \u003cem\u003eS. lycopersicum\u003c/em\u003e. After an initial filtration based on individual expression level (Fig. S1), and retaining the top 50% of tRFs, a final unique set of 2,505 tRFs for \u003cem\u003eA. thaliana\u003c/em\u003e, 3,078 for \u003cem\u003eO. sativa\u003c/em\u003e, and 843 for \u003cem\u003eS. lycopersicum\u003c/em\u003e was obtained. Based on individual expression levels, other-tRFs categories were more abundant (Table S1). The number of tRFs remaining after each filtration step is shown in Fig.\u0026nbsp;2. We analyzed the expression of these tRFs under various biotic stress conditions in plants, revealing distinct variations in both expression patterns and the types of tRFs across different groups of biotic stress infections (Fig. S1), and in response to each specific infection (Fig. S2). A significant number of tRFs, across various types, were predominantly upregulated in all the plants (Fig. S3). The classes tRF-5, other-tRFs, and 5' tRH showed significant differential expression. Alongside the stress response, we also observed tissue-specific variations in tRFs expression. Notably, tRF-5 was highly upregulated and downregulated in leaf tissue at different developmental stages under various infections (Fig.\u0026nbsp;3). The majority of the tRF-5 fragments were found in the developmental stages ranging from mature leaf (21–30 days old) to young leaf (15 days). Additionally, other tRFs were found to be DE across major tissue types under prominent biotic stress conditions, including various developmental stages of leaves and in 15-day-old roots of \u003cem\u003eO. sativa\u003c/em\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eGluTTC exhibits maximum tRNA cleavage in response to biotic stress\u003c/h2\u003e\n \u003cp\u003eAll the DE tRFs were analysed for the tRNA origin information. It has been found that the GluTTC generated the highest number of tRFs across various biotic stress conditions in all studied plants (Fig. S4). Different isoacceptors produced various types of tRFs and showed differences between plants (Zahra et al., 2021). Specifically, the isoacceptors of tRNA-Glu i.e., TTC and CTC, contributed to the majority of DE tRF production across all plants, with GluTTC producing the highest amount of tRF-5 in \u003cem\u003eA. thaliana\u003c/em\u003e, \u003cem\u003eO. sativa\u003c/em\u003e, and \u003cem\u003eS. lycopersicum\u003c/em\u003e, respectively (Fig.\u0026nbsp;4). Other tRFs were also observed to be produced from Glu-TTC in both \u003cem\u003eA. thaliana\u003c/em\u003e and \u003cem\u003eS. lycopersicum\u003c/em\u003e. Various anticodons contributed to the production of tRF-5 across all plants. For example, in \u003cem\u003eA. thaliana\u003c/em\u003e, tRF-5 was also generated by GlyTCC, AspGTC, GlyGCC, and MetCAT, among others. To assess the impact of tRFs generated from GluTTC, their mRNA targets were identified and shortlisted based on binding energy. The identified targets were then subjected to GO enrichment analysis, revealing a strong enrichment for the terms \"response to heat\" and \"cellular component organization\" in \u003cem\u003eA. thaliana\u003c/em\u003e (Fig. S5). These terms fall under the broader categories of \"response to stimulus\" and \"cellular process\". The targets of tRFs generated by GluTTC in \u003cem\u003eO. sativa\u003c/em\u003e were found to play a role in “galactose metabolism”, suggesting that these tRFs may regulate genes involved in this metabolic pathway. None of the genes showed enrichment in \u003cem\u003eS. lycopersicum.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eEnrichment of mRNA targets in distinct stress response mechanisms\u003c/h2\u003e\n \u003cp\u003etRFs may function as post-transcriptional regulators by binding complementarily to their mRNA targets, leading to the degradation of these mRNAs. To explore the functional roles of tRFs, we conducted target predictions (Fig.\u0026nbsp;5, Table S3) and pathway enrichment analyses, for tRFs expressed in at least two infectious conditions in plants. Our analysis revealed that the majority of target genes were associated with responses to various stress types, including cold stress (abiotic) and bacterial stress (biotic) (Fig. S6\u003cstrong\u003e).\u003c/strong\u003e The cellular mechanisms activated to cope with stressors, such as \"signal transduction\", \"transport\", and various \"metabolic processes\", were found to be enriched across all plant species analyzed. Within the transport processes, \"microtubule-based movement\" was particularly prominent. Among biological processes, the term \"biosynthetic process\" was observed to be the most highly enriched across the plants. Under molecular functions, \"catalytic activity\" was extensively abundant. Consistent with the findings of Zahra et al., 2021, our tRF targets were also prominently associated with \"transferase\" and \"kinase\" activities. Additionally, \"binding\" was highly enriched in \u003cem\u003eA. thaliana\u003c/em\u003e and \u003cem\u003eO. sativa\u003c/em\u003e. \"ATP binding\" was significantly enriched and the most prevalent term, while \"microtubule binding\" exhibited the highest level of significance. In \u003cem\u003eA. thaliana\u003c/em\u003e and \u003cem\u003eO. sativa\u003c/em\u003e, the term \"membrane\" was highly and significantly enriched, whereas in \u003cem\u003eS. lycopersicum\u003c/em\u003e, the term \"nucleus\" was extensively enriched. These findings suggest distinct functional adaptations among plant species, reflecting their specific stress response mechanisms and highlighting the role of tRFs in regulating various biological processes (Fig. S7).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eIdentification of differentially expressed mRNA targets\u003c/h2\u003e\n \u003cp\u003eRNA-seq analysis followed by DE gene identification was conducted to examine the expression of target genes in the samples under biotic stress conditions. This approach also aimed to assess the potential influence of tRFs on regulating these genes within the samples. In \u003cem\u003eO. sativa\u003c/em\u003e and \u003cem\u003eS. lycopersicum\u003c/em\u003e, most of the genes were observed to be downregulated, whereas, in \u003cem\u003eA. thaliana\u003c/em\u003e, the majority of the genes were predominantly upregulated (Fig.\u0026nbsp;6). No common genes were identified across all conditions in all plant species; however, overlapping genes in at least two conditions are evident in Fig. S8. Most of the 76 genes in \u003cem\u003eA. thaliana\u003c/em\u003e were found to overlap between those upregulated during \u003cem\u003eTobacco Mosaic Virus\u003c/em\u003e (TMV) infection and those downregulated in response to \u003cem\u003eTurnip Crinkle Virus\u003c/em\u003e (TCV). A similar trend was observed in \u003cem\u003eO. sativa\u003c/em\u003e and \u003cem\u003eS. lycopersicum\u003c/em\u003e, where gene overlaps were detected in at least two infection conditions. In \u003cem\u003eO. sativa\u003c/em\u003e, 11 genes predominantly overlapped between those downregulated by \u003cem\u003eBrown Planthopper\u003c/em\u003e (BP) infection and \u003cem\u003eXanthomonas oryzae\u003c/em\u003e (XO) infection. Meanwhile, in \u003cem\u003eS. lycopersicum\u003c/em\u003e, a substantial overlap of 150 genes was noted between those downregulated during \u003cem\u003eMeloidogyne incognita\u003c/em\u003e (MI) infection and those upregulated in response to ToMV. In \u003cem\u003eA. thaliana\u003c/em\u003e, the overlapping genes were significantly enriched in pathways related to \"intracellular protein transport,\" \"ARF protein signal transduction,\" and \"post-transcriptional gene silencing” (Fig. S9). These terms generally fall under the category of stress response. In \u003cem\u003eO. sativa\u003c/em\u003e, \"monoterpenoid biosynthesis\" was prominently featured, while in \u003cem\u003eS. lycopersicum\u003c/em\u003e, the \"ubiquitin conjugation pathway\" was notably highlighted which also indicated the stress-related process.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eUpregulated tRFs may contribute to the downregulation of target mRNAs and co-expressed genes, thereby influencing shared biological pathways\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe observed that the majority of shortlisted mRNA targets were downregulated during DE analysis. With the hypothesis that the upregulated tRF could be a contributing factor to the downregulation of its target mRNA (Zong et al., 2021), we selected the downregulated target mRNAs from the list of DE genes in each plant species. The co-expression analysis revealed that genes co-expressed with the target gene were also downregulated in response to the infection. AT2G39950, a flocculation protein and a potential target mRNA for tRF1492 in \u003cem\u003eA. thaliana\u003c/em\u003e, was significantly downregulated during infection with \u003cem\u003eAlternaria brassicicola\u003c/em\u003e (AB), showing a log2FC value of -1.66. Additionally, its co-expressed gene AT5G60170 was also found to be downregulated during the same infection, showing a log2FC value of -1.05 (Table S4). The functional analysis revealed that genes AT5G60170 and AT2G28540 both belong to the RNA-binding protein family. At the same time, AT3G22270 is associated with topoisomerase II which may facilitate mRNA decay and enhance post-transcriptional reprogramming https://www.arabidopsis.org/locus?name=AT3G22270 (Fig.\u0026nbsp;7a). In \u003cem\u003eO. sativa\u003c/em\u003e, the gene OsCesA5 (Os03g0837100), which encodes a cellulose synthase A5 protein, was significantly downregulated during the BP infection with a notable negative log2FC value of -3.075. This gene is targeted by OstRF1920 and OstRF1928, which were significantly upregulated during the same infection. All the co-expressed genes, including CESA6 (Os07g0252400), OsGT47A (Os01g0926600), and OsFLA3 (Os08g0321000), were significantly downregulated during BP infection; however, the Os07g0633600 gene did not show any DE in the samples. The CESA6 and OsGT47A are involved in “plant-type cell wall biogenesis” and “polysaccharide biosynthetic process” (Fig.\u0026nbsp;7b). These genes along with the target mRNA, OsCesA5, play a role in the biosynthesis and modification of the plant cell wall. The other co-expressed gene sets for the mRNA targets OsWD40-125 (Os06g0143900) and Os09g0283600 did not show DE under any of the infectious conditions. In \u003cem\u003eS. lycopersicum\u003c/em\u003e, the majority of significant co-expression was observed for the target gene \u003cem\u003eSolyc02g080340.3\u003c/em\u003e with SLtRF725 and SLtRF26 under ToMV infection, specifically in processes related to “fatty acid metabolism and lipid degradation” and “defense response to other organisms”. The target gene was downregulated with a log2FC value of -0.96, while the tRFs were upregulated with log2FC values of 28.054 and 36.855, respectively, during the infection. All the co-expressed genes were downregulated under ToMV stress. Among them, the \u003cem\u003eSolyc01g104210.2\u003c/em\u003e gene is also involved in “fatty acid metabolism (elongation)” and the “sphingolipid biosynthetic process” (Fig.\u0026nbsp;7c). Two of the co-expressed genes, \u003cem\u003eSolyc06g082340.2\u003c/em\u003e and \u003cem\u003eSolyc12g056130.2\u003c/em\u003e, are involved in ubiquitin-dependent pathways, specifically the ubiquitin-dependent protein catabolic process and the ubiquitin ligase conjugation pathway, respectively. In contrast, most of the remaining genes were not found to be significantly enriched. Moreover, the gene \u003cem\u003eSolyc06g048950.3\u003c/em\u003e, which encodes a non-specific serine/threonine protein kinase (STPK), was significantly downregulated during infection by the \u003cem\u003eAlternaria\u003c/em\u003e fungus (AF). This gene showed considerable co-expression with a retrotransposon-derived protein that was also downregulated under the same infection. Additionally, the target gene \u003cem\u003eSolyc01g106770.3\u003c/em\u003e, another STPK-TOR, which is targeted by the upregulated SLtRF723, was downregulated under ToMV infection, along with its co-expressed gene \u003cem\u003eSolyc01g008740.2\u003c/em\u003e, a calcium-dependent kinase involved in intracellular signal transduction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003emiRNAs did not influence target mRNA expression\u003c/h2\u003e\n \u003cp\u003eFigure S10a illustrates that several mRNA transcripts in \u003cem\u003eA. thaliana\u003c/em\u003e have the potential to be regulated by specific miRNAs capable of binding to them. In contrast, in \u003cem\u003eO. sativa\u003c/em\u003e, two genes were identified to bind to miRNAs, as depicted in Fig. S10b potentially. No miRNAs were found to potentially bind to any predicted target genes in \u003cem\u003eS. lycopersicum\u003c/em\u003e. A comparison of the binding energies and binding coordinates between miRNA and tRF reveals that, although both exhibited highly negative binding energies, the binding coordinates on the target gene differed (Table\u0026nbsp;1). Additionally, these mRNA targets did not show DE under any infection conditions in either \u003cem\u003eA. thaliana\u003c/em\u003e or \u003cem\u003eO. sativa\u003c/em\u003e. Furthermore, none of these miRNAs were detected in the samples considered in this study. Moreover, this supports the previous hypothesis that the downregulated potential target mRNA and its corresponding co-expressed downregulated mRNA may be DE primarily due to their interaction with the tRFs, which are upregulated during various biotic stresses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003etRFs regulate the activity of the transposable elements\u003c/h2\u003e\n \u003cp\u003eThe chord diagram in Fig.\u0026nbsp;8 collectively demonstrates that most tRFs across all plant species exhibited a strong affinity for binding to LTR/gypsy TE, supporting the findings of (Zahra et al., 2021). In \u003cem\u003eA. thaliana\u003c/em\u003e, tRF-5 was predominantly associated with LTR/gypsy elements, while tRF-3-CCA primarily interacted with SINE/Unknown elements (Table S5). It was observed that TE transcripts were predominantly downregulated during TCV infection, with tRF-5 and 5’tRH showing strong interactions with TE transcripts in \u003cem\u003eO. sativa\u003c/em\u003e. Notably, tRF-5 mainly interacted with Helitron/Helitron elements, while 5’tRH was primarily associated with TIR/Dada elements. Significant downregulation of TE transcripts was observed during BP infection, with some downregulation also occurring in XO and \u003cem\u003eMeloidogyne graminicola\u003c/em\u003e (MG) infections. In \u003cem\u003eS. lycopersicum\u003c/em\u003e, most tRF-5s and other-tRFs showed a preference for interacting with LTR/gypsy elements, with some interaction observed with LTR/Copia and LTR/Unknown elements. The strongest downregulation of TE transcripts was observed during AF infection, with notable downregulation also seen in MI and ToMV infections. This supports the conclusion highlighted in the review article of (Y. Zhang et al., 2024), that tsRNA can regulate cellular responses by affecting the activity of retrotransposons. It was observed that tRNAMetCAT generates tRF-s which specifically targets the LTR retrotransposon, Athila6A (Martinez et al., 2017). Since both tRFs and TEs are known to be expressed and accumulate under stress, it would seem that the regulation of tRF production is connected to TE activity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003ePbtRF database\u003c/h2\u003e\n \u003cp\u003eA total of 637,476 entries are stored in PbtRFdb, comprising 277,181 entries for \u003cem\u003eA. thaliana\u003c/em\u003e, 268,032 for \u003cem\u003eO. sativa\u003c/em\u003e, and 92,263 for \u003cem\u003eS. lycopersicum\u003c/em\u003e. For differentially expressed tRFs, \u003cem\u003eA. thaliana\u003c/em\u003e has 2,260 entries, \u003cem\u003eO. sativa\u003c/em\u003e has 6,663, and \u003cem\u003eS. lycopersicum\u003c/em\u003e has 901 entries. Detailed information on tRFs can be retrieved through two main modules: the “Search” and “Browse” modules. The Search module allows users to extract detailed information based on sequences, tRF types, amino acids, anticodons, modifications, and differentially expressed tRFs. The “DE tRFs” section enables users to query biotic stress-specific studies across the three species, with filters for various stress conditions and expression levels. The Browse module allows users to extract information across the three species via four categories: tissue type, amino acid, anticodon, and stress. Each query provides users with specific details such as SRA accession, organism, tissue, developmental stage, tRF type, locus, position, length, stress, sequence, and modification. The query also provides log2fold change data and expression information for differential expression, including upregulation and downregulation. Each SRA accession is hyperlinked to the NCBI SRA page, offering additional information. Users can visualize tRFs using the “JBrowse” module for the species of interest. The module allows users to select tracks, including the reference sequence, general feature file track (GFF), and tRF track. Users can zoom in on specific locations to view tRFs and gene features. The tRF track includes sequence information, tRF type, tRNA derivatives, and the length of the tRF. PbtRFdb also includes a “Statistics” module, which allows users to visualize tRFs across various stress conditions. The most abundant classes of tRFs and parental tRNAs under different conditions are displayed using graphs. DE tRFs are presented with user-friendly visualizations such as line graphs, bar plots, and pie charts. Additional features include a “Download” module, allowing users to download results for each plant species separately. A “Help” module is also provided to guide users through navigating the database.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study provides a comprehensive profile of tRFs in response to various biotic stresses and examines their potential roles in regulating the mRNA target, TEs, and miRNA activity amongst three species: \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, \u003cem\u003eOryza sativa\u003c/em\u003e, and \u003cem\u003eSolanum lycopersicum\u003c/em\u003e. Among the identified tRF subclasses, a notable abundance of other-tRFs was observed, with significant differential expression observed in the tRF-5, other-tRFs, and 5\u0026rsquo;tRH classes under stress conditions. This aligns with findings by Thompson et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, which demonstrated that oxidative stress induces the endonucleolytic cleavage of tRNAs in plants. Similarly, Alves et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e highlighted the association of tRF-5 with distinct plant AGOs, supporting its production under abiotic stress. The higher prevalence of other-tRFs during our initial identification suggests that these less-defined tRFs may play critical roles in mediating plant responses to various biotic stressors.\u003c/p\u003e \u003cp\u003eThe data highlights the cleavage of tRNA-GluTTC, a well-known stress-responsive biomarker, as a key produces of tRFs involved in both biotic and abiotic responses, paralleling findings in human cancers, such as gastric and thyroid cancers (Mao et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shan et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhu, Li, et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In plants, tRNA-GluTTC cleavage is significantly elevated under biotic stress across all the studied species, corroborating the findings of Zahra et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e for \u003cem\u003eOryza sativa\u003c/em\u003e, and \u003cem\u003eSolanum lycopersicum\u003c/em\u003e. Notably, tRNA-GluTTC generated the highest levels of tRF-5 across all species. Enrichment analyses revealed that tRFs derived from tRNA-GluTTC were associated with heat stress and cellular processes for \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, while in \u003cem\u003eOryza sativa\u003c/em\u003e, they were linked with galactose metabolism. Heat stress-induced tRF accumulation, documented across multiple plant species, (Y. Wang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zahra et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), underscores their role in the stress-response signaling pathway (Huang \u0026amp; Hopper, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, in \u003cem\u003eOryza sativa\u003c/em\u003e, abiotic stress was closely associated with the activation of galactose metabolism pathways, suggesting potential stress-adaptive mechanisms (H. Wang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings position tRNA-GluTTC-derived crucial as key modulators of stress-response pathways, emphasizing the need for further investigation into their mRNA targeting mechanisms.\u003c/p\u003e \u003cp\u003eEvidence suggests that tRFs play a role in influencing mRNA degradation (Alves et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; C. Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our findings reveal that tRFs-mRNA are enriched in stress-response mechanisms such as signal transduction, metabolic process, and catalytic activity. Stress-induced intracellular remodeling in plants, (Yagyu \u0026amp; Yoshimoto, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) appears to impact intracellular protein transport, as highlighted by the GO enrichment analysis for \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. This aligns with studies emphasizing the importance of post-transcriptional gene regulation in abiotic stress responses (Floris et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), further corroborated by the enrichment of the monoterpenoid biosynthesis pathway identified in GO analysis for Oryza sativa. Additionally, the ubiquitin-26S proteasome system (UPS), in conjunction with abscisic acid (ABA), has been implicated in modulating abiotic stress responses (Doroodian \u0026amp; Hua, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) supporting the enrichment of the ubiquitin conjugation pathway observed in \u003cem\u003eSolanum lycopersicum.\u003c/em\u003e Collectively, these insights emphasize the multifaceted role of tRFs in regulating stress-adaptive mechanisms through targeted mRNA interactions and gene regulation pathways across different plant species.\u003c/p\u003e \u003cp\u003eRecent studies suggest that tRFs may influence mRNA transport, with potential implications for transgenerational memory (George et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Li et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e reported that tRF-Ala interacts with splicing factor, SR34 weakened the binding ability of SR34 demonstrating the regulation of target mRNAs by tRF-Ala. In our study, we observed that in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, the target gene AT2G39950 alongside its co-expressed AT5G60170, was downregulated during \u003cem\u003eAlternaria brassicicola\u003c/em\u003e infection. Similarly, in \u003cem\u003eOryza sativa\u003c/em\u003e, gene OsCesA5 and its co-expressed genes, were downregulated during \u003cem\u003eBrown planthopper\u003c/em\u003e infection. \u003cem\u003eSolanum lycopersicum\u003c/em\u003e gene Solyc02g080340.3 and its co-expressed genes were downregulated during \u003cem\u003eMeloidogyne incognita\u003c/em\u003e and upregulated during ToMV infection. We hypothesize that upregulated tRFs may mediate the targeted downregulation of mRNAs and associated genes to shared stress-related pathways. This regulatory interplay between tRFs and mRNAs highlights their potential as key players in plant stress adaptation.\u003c/p\u003e \u003cp\u003eThe role of miRNAs in regulating mRNA stability is well-documented (Dalmay, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Pioneering studies in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e revealed that miRNAs target transcription factors mRNAs (Bartel, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In our investigation of miRNA-mediated regulation of target mRNAs, we identified that \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, gene AT1G05580, targeted by the stress-related ath-miR414 participates in the phosphatidylinositol signaling system (Xie et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, the gene AT3G0200, targeted by ath-miR163, is potentially linked to adaptations in nutrient-deficient environments (Fasani et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In \u003cem\u003eOryza sativa\u003c/em\u003e, the gene Os05g0590000, targeted by osa-miR2103, was found to play a critical role in rice immunity against \u003cem\u003eMagnaporthe oryzae\u003c/em\u003e (Fan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite these insights, our analysis did not detect miRNAs in the samples, suggesting that tRF-mediated mRNA downregulation may function independently of miRNAs. This observation highlights the potential of tRFs as distinct regulatory molecules in mRNA targeting, operating alongside or as an alternative to miRNA-based mechanisms.\u003c/p\u003e \u003cp\u003eThe interaction between tRFs and TEs has been explored in only a few studies across both plants and mammals (Martinez, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Slotkin et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zahra et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). an interact with transcripts, altering expression levels by acting as novel promoters or influencing regulatory pathways (Hirsch \u0026amp; Springer, 2017). In our data, diverse TE families were observed in different species. In \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, LTR-Gypsy and SINE/Unknown families were identified, with LTR-Gypsy associated with heat stress responses (Pietzenuk et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). \u003cem\u003eOryza sativa\u003c/em\u003e exhibited Helitron/Helitron and TIR/Dada elements, while \u003cem\u003eSolanum lycopersicum\u003c/em\u003e showed the presence of LTR/Gypsy, LTR/Copia, and LTR/Unknown elements. Notably, Gypsy and Copia elements are known to be responsive to various stress conditions, contributing to adaptive responses under adverse environmental conditions (Bolger et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These findings suggest a potential regulatory interplay between tRFs and TE activity, particularly under stress conditions.\u003c/p\u003e \u003cp\u003eFurthermore, we developed a database to catalog and analyze tRF profiles across \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, \u003cem\u003eOryza sativa\u003c/em\u003e, and \u003cem\u003eSolanum lycopersicum\u003c/em\u003e, enabling detailed exploration of tRFs in various biotic stresses. This resource provides a platform for future research into tRF-mediated regulatory mechanisms under biotic stress conditions.\u003c/p\u003e \u003cp\u003eOur in-silico analysis provides valuable insights into the regulatory functions of tRNA-derived fragments in response to biotic stress in plants. The identification of distinct tRFs profiles along with their potential interactions with mRNA targets and transposable elements, underscores the complexity and specificity of stress response pathways across different plant species. These findings highlight the potential role of tRFs in modulating cellular responses to stress. However, further experimental validation and in-depth mechanistic studies are required to confirm these interactions and elucidate the molecular pathways through which tRFs contribute to cellular adaptation under biotic stress conditions\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003etRFs are small non-coding RNAs that act as novel regulators of gene expression, impacting both transcriptional and post-transcriptional processes (Martinez et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Numerous studies have characterized tRFs in fungi, viruses, and plants (Asha \u0026amp; Soniya, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zahra et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our study focuses on tRFs in response to biotic stress across \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, \u003cem\u003eOryza sativa\u003c/em\u003e, and \u003cem\u003eSolanum lycopersicum\u003c/em\u003e, highlighting their regulating role of mRNA targets, miRNA pathways, and transposable elements. Notably, our study found tRNA-GluTTC to be abundantly cleaved and differentially expressed under biotic stress conditions, with distinct roles in stress-response pathways, including intracellular protein transport, signal transduction, and metabolic processes. The tRFs demonstrated the interaction with mRNA targets essential for the plant's adaption to stress, suggesting that differential expression of tRFs may influence mRNA regulation. Additionally, tRF-mediated stress regulatory mechanisms may act independently of miRNA pathways, providing insights into post-transcriptional gene silencing. The association of tRFs and TEs can lead to a complex regulatory network influencing stress adaptation. The database supporting this study is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nipgr.ac.in/PbtRFdb\u003c/span\u003e\u003cspan address=\"http://www.nipgr.ac.in/PbtRFdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, providing a valuable resource for further research in tRFs in plant biotic stress response.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the fusion data generated is available in a database named \u0026lsquo;PBtRFDB\u0026rsquo;, http://www.nipgr.ac.in/PbtRFdb.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is supported by the BT/PR40146/BTIS/137/4/2020, BT/PR40169/BTIS/137/71/2023, BT/PR40160/BTIS/137/64/2023, BT/PR40261/ BTIS/137/55/2023 project grants by Department of Biotechnology (DBT), Government of India and by the core grant of the National Institute of Plant Genome Research (NIPGR) in the laboratory of SK.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: SPS\u003c/p\u003e\n\u003cp\u003eData curation: SPS and NB\u003c/p\u003e\n\u003cp\u003eFormal analysis: SPS and NB\u003c/p\u003e\n\u003cp\u003eWeb-interface development: NB\u003c/p\u003e\n\u003cp\u003eFunding acquisition, Project administration, Resources, and Supervision: SK\u003c/p\u003e\n\u003cp\u003eRoles/Writing - Original draft: AA, SA, and FH; and Writing - review \u0026amp; editing: SK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the Department of Biotechnology (DBT)-eLibrary Consortium, India, for providing access to e-resources. 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(2021). tsRNAs: Novel small molecules from cell function and regulatory mechanism to therapeutic targets. \u003cem\u003eCell Proliferation\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(3). https://doi.org/10.1111/cpr.12977\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"functional-and-integrative-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fige","sideBox":"Learn more about [Functional \u0026 Integrative Genomics](http://link.springer.com/journal/10142)","snPcode":"10142","submissionUrl":"https://submission.nature.com/new-submission/10142/3","title":"Functional \u0026 Integrative Genomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"tRFs, tRNA fragments, tncRNAs, Biotic stress, Genome Regulation, Plant Database","lastPublishedDoi":"10.21203/rs.3.rs-5813390/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5813390/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePlants face significant challenges from biotic stresses, that adversely impact their growth and development. Amongst the various regulatory molecules, transfer RNA-derived fragments (tRFs) play crucial roles in modulating adaptive defense mechanisms. Although the role of tRFs in response to biotic stresses is still emerging, it is evident that different biotic stressors elicit distinct regulatory pathways. This study investigates the involvement of tRFs in stress response and resistance across three plant species: \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, \u003cem\u003eOryza sativa\u003c/em\u003e, and \u003cem\u003eSolanum lycopersicum\u003c/em\u003e. Our findings reveal a complex regulatory network where tRFs interact with mRNA targets, miRNAs, and transposable elements, underscoring their significance in adaptive biotic stress responses. This research advances the understanding of tRF regulatory mechanisms and lays the foundation for new strategies to enhance resilience against biotic stress. The database supporting this study is freely accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nipgr.ac.in/PbtRFdb\u003c/span\u003e\u003cspan address=\"http://www.nipgr.ac.in/PbtRFdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, providing a valuable resource for further research on the tRFs in plant biotic stress responses.\u003c/p\u003e","manuscriptTitle":"Integrative Analysis of tRNA-Derived Fragments in Plant Adaptation to Biotic Stress: A Comparative Study and Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-15 10:39:37","doi":"10.21203/rs.3.rs-5813390/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-09T22:08:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-08T15:56:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-08T12:10:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-06T16:54:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99035817783797579580966629312895590666","date":"2025-01-19T17:03:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157181756258984376842062482603321772504","date":"2025-01-18T16:42:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154843071231732123578616800436535238503","date":"2025-01-17T04:08:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-16T23:34:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-14T21:42:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-14T09:37:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Functional \u0026 Integrative Genomics","date":"2025-01-12T11:28:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"functional-and-integrative-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fige","sideBox":"Learn more about [Functional \u0026 Integrative Genomics](http://link.springer.com/journal/10142)","snPcode":"10142","submissionUrl":"https://submission.nature.com/new-submission/10142/3","title":"Functional \u0026 Integrative Genomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2dd299ed-bdcf-4eec-bcf6-0a6d13ef4939","owner":[],"postedDate":"January 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-31T16:02:16+00:00","versionOfRecord":{"articleIdentity":"rs-5813390","link":"https://doi.org/10.1007/s10142-025-01576-3","journal":{"identity":"functional-and-integrative-genomics","isVorOnly":false,"title":"Functional \u0026 Integrative Genomics"},"publishedOn":"2025-03-25 15:57:41","publishedOnDateReadable":"March 25th, 2025"},"versionCreatedAt":"2025-01-15 10:39:37","video":"","vorDoi":"10.1007/s10142-025-01576-3","vorDoiUrl":"https://doi.org/10.1007/s10142-025-01576-3","workflowStages":[]},"version":"v1","identity":"rs-5813390","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5813390","identity":"rs-5813390","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}