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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Plant Biol . 2026 Mar 6;26:668. doi: 10.1186/s12870-026-08485-y Search in PMC Search in PubMed View in NLM Catalog Add to search Identification and analysis of microRNAs responsible for brown planthopper resistance in BPH14 and BPH15 pyramiding rice Liang Hu Liang Hu 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China Find articles by Liang Hu 1, # , Dabing Yang Dabing Yang 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China Find articles by Dabing Yang 1, # , Hongbo Wang Hongbo Wang 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China Find articles by Hongbo Wang 1, # , Xueshu Du Xueshu Du 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China Find articles by Xueshu Du 1 , Liang Lu Liang Lu 2 Key Laboratory of Integrated Pests Management on Crops in Central China, Ministry of Agriculture and Rural Affairs/Hubei Key Laboratory of Crop Diseases, Insect Pests and Weeds Control, Institute of Plant Protection and Soil Science, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China Find articles by Liang Lu 2 , Jinbo Li Jinbo Li 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China 3 Hubei Hongshan Laboratory, Wuhan, 430070 China Find articles by Jinbo Li 1, 3 , Mingyuan Xia Mingyuan Xia 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China Find articles by Mingyuan Xia 1 , Huaxiong Qi Huaxiong Qi 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China Find articles by Huaxiong Qi 1 , Wenjun Zha Wenjun Zha 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China Find articles by Wenjun Zha 1 , Yan Wu Yan Wu 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China Find articles by Yan Wu 1 , Tongmin Mou Tongmin Mou 4 National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China Find articles by Tongmin Mou 4 , Aiqing You Aiqing You 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China 3 Hubei Hongshan Laboratory, Wuhan, 430070 China Find articles by Aiqing You 1, 3, ✉ , Bingliang Wan Bingliang Wan 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China Find articles by Bingliang Wan 1, ✉ Author information Article notes Copyright and License information 1 Key Laboratory of Crop Molecular Breeding, Ministry of Agriculture and Rural Affairs, Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, Institute of Food Crops, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China 2 Key Laboratory of Integrated Pests Management on Crops in Central China, Ministry of Agriculture and Rural Affairs/Hubei Key Laboratory of Crop Diseases, Insect Pests and Weeds Control, Institute of Plant Protection and Soil Science, Hubei Academy of Agricultural Sciences, Wuhan, 430064 China 3 Hubei Hongshan Laboratory, Wuhan, 430070 China 4 National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070 China ✉ Corresponding author. # Contributed equally. Received 2024 Oct 2; Accepted 2026 Feb 27; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13077905 PMID: 41787315 Abstract Background Rice farming faces a significant challenge from the brown planthopper (BPH), a destructive pest that threatens crop yields. Developing BPH-resistant rice varieties is critical for ensuring food security. The pyramiding of BPH resistance genes, BPH14 and BPH15, has proven effective in providing protection in elite rice strains. MicroRNAs (miRNAs) play a pivotal role in plant defense by fine-tuning resistance responses through the modulation of genes involved in various signaling pathways and metabolic processes. B1415, the pyramiding line containing BPH14 and BPH15, exhibits stronger BPH resistance compared to its recurrent parent, Wushansimiao, without affecting other important agronomic traits. However, the molecular basis underlying the resistance conferred by the BPH14/BPH15 pyramiding rice remains largely unclear, particularly with respect to the potential regulatory role of miRNAs. Studying miRNAs in resistance gene pyramiding lines like B1415 is essential for advancing rice breeding efforts, as such research can uncover regulatory networks that enhance pest resistance and identify miRNA-mRNA interactions as potential targets for genetic manipulation. Results The study investigated miRNA levels in B1415 and their recurrent parent (RP) under BPH infestation employing high-throughput sequencing and revealed 136 differentially expressed miRNAs (DEMs) among 550 known miRNAs. An integrated analysis highlighted that 587 miRNA-mRNA pairs linking 95 DEMs to 537 targeted genes were enriched in phenylpropanoid and lignin metabolism, circadian rhythms, and amino acid metabolism. The candidate DEMs, miR172d-3p, and miR396 family members were identified as negative regulators to decrease their target genes Os06g0708700 (encoding a nodulin-like protein) and Os11g0129700 (encoding an AP2 domain transcription factor), suggesting their key roles in rice against BPH. Conclusions Our investigation provides the first insights into miRNA-mediated defense mechanisms in the B1415. Identifying miRNAs and their target mRNAs in BPH resistance opens a new avenue for rice breeding programs, offering potential targets for improving pest resistance. Understanding these molecular interactions paves the way for developing more resistant rice cultivars, thereby contributing to sustainable rice production and food security. Supplementary Information The online version contains supplementary material available at 10.1186/s12870-026-08485-y. Keywords: Brown planthopper, Rice, MicroRNA, mRNA, BPH14 / BPH15 Background Rice ( Oryza sativa L.), critical for sustaining over half of the global population [ 1 ], faces significant threats from pests like the brown planthopper ( Nilaparvata lugens Stål, BPH) [ 2 ]. This pest inflicts considerable harm to rice production, feeding on the plant’s phloem sap and causing stunting, wilting, and death. BPH also transmits harmful viruses, causing grassy and leaf dwarf diseases and crop losses. To manage BPH, rice has developed basic and resistance gene-mediated defense mechanisms [ 3 ]. Incorporating BPH resistance genes presents an economical and sustainable strategy for controlling this pest. So far, researchers have cloned 17 BPH resistance genes [ 4 ]. Of them, BPH14 and BPH15 were the earliest to be cloned [ 5 , 6 ]. BPH14 encodes a coiled-coil nucleotide-binding site leucine-rich repeat protein, which binds to the BPH14-interacting salivary protein, triggering host resistance [ 7 ]. The interaction forms complexes that stabilize the transcription factors OsWRKY46 and OsWRKY72. This stabilization activates defense genes, which leads to increased salicylic acid signaling, the accumulation of reactive oxygen species, and callose deposition to combat BPH [ 8 ]. BPH15 encodes a lectin receptor kinase localized in the plasma membrane, which detects plant damage or herbivore-associated molecular patterns, initiating lasting, broad-spectrum resistance against BPH and other pathogens [ 6 ]. As BPH populations adapt and evolve, single gene-driven resistance in rice cultivars is often overcome, reducing its effectiveness over time [ 9 ]. Pyramiding diverse BPH resistance genes offers a more durable, wide-range solution against BPH infestations [ 10 ]. By employing marker-assisted selection (MAS), breeders have incorporated BPH14 and BPH15 genes into high-performing rice varieties, significantly enhancing their resistance to BPH [ 11 – 13 ]. For example, Wang et al. significantly enhanced the BPH resistance of the rice variety ‘Wushansimiao’ by precisely pyramiding BPH14 and BPH15 without compromising other agronomic traits [ 12 ]. Despite the successful application of these resistant rice varieties in production and their effectiveness in controlling BPH, the underlying molecular mechanisms that confer enhanced resistance in the BPH14 / BPH15 pyramiding line (thereafter referred to as B1415) remain largely unknown. This knowledge gap limits the deeper understanding and application of BPH-resistant genes. MicroRNAs (miRNAs) are regulatory RNAs, approximately 21–24 nucleotides long, formed by endonucleolytic cleavage of single-stranded hairpin precursors [ 14 ]. They control target gene expression through pairing with complementary regions on mRNAs, leading to their degradation and translation inhibition [ 15 ]. In rice, miRNAs are vital for managing development and stress responses [ 16 ]. Additionally, they contribute significantly to the immune responses of rice against pathogens and herbivores [ 17 ]. For example, miR160a [ 18 ], miR398b [ 19 ], miR159a [ 20 ], miR166k-166 h [ 21 ], miR169 [ 22 ], miR164a [ 23 ], miR1432 [ 24 ], miR319 [ 25 ], and miR528 [ 26 ] have been linked to rice pathogen resistance. In contrast, fewer reports have been on miRNAs in insect-plant interactions. Notably, miR156 and miR396 are shown to attenuate BPH resistance, impacting jasmonic acid and flavonoid biosynthesis via the miR156-OsMPK3/6-OsWRKY70 and miR396-OsGRF8-OsF3H-flavonoid pathways, respectively [ 27 , 28 ]. Recent studies show that other miRNAs, including miR159, miR319, and miR162a, also contribute to BPH resistance. For instance, miR159 enhances resistance via the miR159-OsGAMYBL2-GS3 pathway, miR319 regulates resistance through the miR319-OsPCF5-OsMYB30C axis affecting the phenylpropanoid pathway, and miR162a acts via cross-kingdom RNAi to reduce BPH reproduction by targeting NlTOR gene [ 29 – 31 ]. These findings indicate that diverse miRNAs target multiple transcription factors and signaling components to mediate BPH defense. However, the roles of miRNAs in resistance gene pyramiding lines, such as the BPH14 and BPH15 pyramiding line B1415, remain largely unexplored. Investigating miRNAs in such lines is therefore essential for uncovering miRNA-mediated regulatory networks that enhance BPH resistance and identifying potential targets for rice improvement. Rice relies on resistant miRNAs and genes to implement intricate defense strategies against BPH invasion. Therefore, integrated studies of BPH-responsive miRNAs and their target genes become key for understanding these defense mechanisms. Upon BPH attack, miRNAs show differential expression in the BPH15 introgression lines and their susceptible counterpart, with several miRNAs downregulating target genes involved in pathways linked to basal and BPH-specific defenses [ 32 ]. Additionally, integrated miRNA and mRNA expression profiling has revealed that miRNA-mRNA modules are crucial in BPH6 -mediated resistance [ 33 ]. Identifying and validating BPH-responsive miRNAs and their targets in IR56 rice, responding to different BPH populations, also highlight how BPH genetic variation influences rice-BPH interactions [ 34 ]. Recently, Yu et al. examined differential miRNA and mRNA responses in YHY15 rice when exposed to BPH, broadening our comprehension of rice defense mechanisms [ 35 ]. This study leveraged high-throughput sequencing to assess miRNA profiles in B1415 and its recurrent parent (RP) before and after BPH infestation. By integrating miRNA profiles with previously identified mRNA data, we aimed to unravel the key molecular factors contributing to BPH resistance in rice. Given the broad adoption of BPH14 and BPH15 in breeding programs for durable and stable resistance, understanding the molecular details of this resistance is significant. Our findings will contribute to future genome-wide studies on the BPH resistance mechanisms in BPH14 / BPH15 pyramiding rice varieties. Results Summary of small RNA sequencing data To uncover miRNAs responsible for BPH resistance in B1415, we conducted small RNA sequencing and characterized small RNAs in the B1415 and RP plants infested by BPH at onset (B1415_0, RP_0), early infestation stage (B1415_early, RP_early), and late infestation stage (B1415_late, RP_late) based on their performance data against BPH published previously [ 36 ]. Across the 18 RP and B1415 libraries, the number of raw reads ranged from 15,309,819 in the B1415_early-2 library to 29,603,720 in the RP_early-3 library (Supplementary Table 1). After adapter and low-quality read removal and size filtration (18–36 nt), clean reads ranged from 11,482,450 in the B1415_early-2 library to 19,790,307 in the RP_early-3 library across 18 libraries (Supplementary Table 1). Most reads were 24 nt with 21 nt and 23 nt following in abundance, aligning with previous reports on rice small RNAs (Fig. 1 A) [ 32 ]. Clean reads were matched to the Rfam (ver. 13.0) and miRBase (ver. 22.0) databases (Supplementary Table 1), resulting in 550 miRNAs in 72 families (Supplementary Table 2). Principal component analysis (PCA) unveiled distinct differentiation among all groups, validating the data’s reliability for further analyses (Fig. 1 B). Notably, the B1415_early group displayed significant differences in miRNA profiles compared to other groups, indicating miRNA profile shift during early BPH infestation (Fig. 1 B). Fig. 1. Open in a new tab Overview of small RNA sequencing results A The length distribution of sequenced reads in B1415 and RP plants at non-infested, early and late infestation stages. 0: non-infested; early: early infestation stage; late: late infestation stage B PCA of miRNA sequencing data from RP_0, RP_early, RP_late, B1415_0, B1415_early and B1415_late. Samples from different groups are represented by different colors and each group has three biological replicates Expression analysis of differentially expressed miRNAs (DEMs) in B1415 and RP during BPH infestation After normalization, the mean count of normalized reads from three biological repeats was analyzed to identify DEMs based on |log 2 Fold Change (FC)| > 1 and P < 0.05. 136 DEMs were detected after merging overlapping entries, including 93 DEMs (with three overlapping DEMs) between varieties (B1415_0/RP_0, B1415_early/RP_early, and B1415_late/RP_late) and 128 DEMs (with 21 overlapping DEMs) across infestation stages (RP_early/RP_0, RP_late/RP_0, B1415_early/B1415_0, and B1415_late/B1415_0) (Fig. 2 A, Supplementary Tables 3, 4 and 5). Specifically, 6 DEMs were detected in B1415_0/RP_0, 85 in B1415_early/RP_early, 5 in B1415_late/RP_late, 12 in RP_early/RP_0, 23 in RP_late/RP_0, 104 in B1415_early/B1415_0, and 18 in B1415_late/B1415_0 (Fig. 2 A). These findings demonstrate that BPH infestation triggers DEMs in both varieties, with distinct regulatory patterns at different stages (Fig. 2 A). Interestingly, B1415 exhibited far more DEMs during the early infestation stage compared to RP, indicating a faster and more robust defense response in B1415 against BPH invasion (Fig. 2 A). To verify the reliability of the sequencing data, nine miRNAs were selected for quantitative stem-loop RT-PCR (qRT-PCR) analysis based on their expression abundance, differential expression patterns, and suitability for primer design. The expression trends obtained by qRT-PCR were consistent with the sequencing results (Fig. 2 B). Fig. 2. Open in a new tab Differentially expressed miRNAs (DEMs) in all comparisons in the RP and B1415 plants before and after BPH infestation A Contrast between up-regulated and down-regulated DEMs in all comparisons. miRNA up represents the number of DEMs that were up-regulated in the compared group. miRNA down represents the number of DEMs that were down-regulated in the compared group. miRNA total represents the total number of DEMs in the compared group (log 2 FC > 1 or log 2 FC < -1; P < 0.05) B qRT-PCR to verify miRNA expression patterns in the RP and B1415 plants compared to sequencing results. The U6 was used as an internal reference. Data represent the mean ± SE of three independent biological repeats. Data were subjected to Student’s t-test, and asterisks indicate significant differences between RP_0 and the other indicated group (* P < 0.05; ** P < 0.01) Identification of BPH resistance-related DEMs before and after BPH infestation To explore BPH resistance-associated miRNA, we analyzed DEMs between B1415 and RP (B1415_0/RP_0, B1415_early/RP_early, and B1415_late/RP_late) using Venn diagrams. Initially, we focused on miRNA expression differences between B1415 and RP prior to BPH infestation by examining the B1415_0 vs.RP_0 comparison. All 6 DEMs (miR1882e-3p, miR1882h, miR399g, miR1847.1, miR1865-3p and miR5513) were down-regulated in B1415_0/RP_0, with some previously linked to responses to pathogens and herbivores (Fig. 3 A, Supplementary Table 4). Notably, miR1882e-3p showed extreme down-regulation in B1415_0/RP_0 and has been implicated in the response to rice blast infection (Fig. 3 B), potentially contributing to basal resistance against Magnaporthe oryzae [ 37 ]. Similarly, miR1882h was also down-regulated in B1415_0/RP_0 (Fig. 3 B) and may influence BPH fecundity [ 38 ]. These findings indicate that the regulation of these DEMs primes the rice for enhanced resistance, potentially conferring a defensive advantage to B1415 plants prior to BPH infestation. Fig. 3. Open in a new tab Analysis of DEMs related to BPH resistance between B1415 and RP plants before and after BPH infestation A Venn diagrams of the unique and shared DEMs in the B1415 plant compared to RP before (B1415_0/RP_0) and after BPH infestation (B1415_early/RP_early and B1415_late/RP_late). Venn diagram showed the number of DEMs of B1415 compared to RP at the non-infested stage (left), early infestation stage (right), and late infestation stage (bottom) B Hierarchical clustering analysis of the potential candidate DEMs related to BPH resistance between the different varieties. The color bar represents fold-change values shown in the log 2 scale and asterisks indicate significant differences between the indicated comparisons (* P < 0.05; ** P < 0.01) After BPH infestation, an additional 87 DEMs were unveiled in B1415_early/RP_early and B1415_late/RP_late. Most of these DEMs were observed during the early infestation stage, suggesting that miRNAs are part of an active and rapid defense response against BPH (Fig. 3 A, Supplementary Table 4). For example, miR160f-3p and miR827 were down-regulated, whilst miR1859, miR5072, and the miR530 family were up-regulated in B1415_early/RP_early (Fig. 3 B). These miRNAs are recognized to affect plant stress responses and are likely involved in the swift quick defense mechanism against BPH, possibly playing key roles in signal transduction of BPH resistance [ 18 , 32 – 35 , 39 ]. Identification of BPH resistance-related DEMs in early and late infestation stages To analyze DEMs linked to BPH resistance, we compared the DEMs before and after BPH infestation using Venn diagrams (RP_early/RP_0, RP_late/RP_0, B1415_early/B1415_0, and B1415_late/B1415_0) (Fig. 4 A, Supplementary Table 5). The B1415_early/B1415_0 comparison group showed approximately eight times higher number of DEMs (104) than the RP_early/RP_0 (12), with only 5 DEMs common to both groups (Fig. 4 A). At the late infestation, the number of DEMs in the B1415_late/B1415_0 and RP_late/RP_0 was similar, with nine shared DEMs (Fig. 4 A). When cross-referencing early and late infestation stages within the same plant, the B1415_early/B1415_0 group had 104 DEMs, far more than the 18 in B1415_late/B1415_0 group. In contrast, RP_early/RP_0 had fewer DEMs (12) than RP_late/RP_0, which contained 23 DEMs (Fig. 4 A). These results indicate that at the early infestation stages, the resistant B1415 plant mounted a rapid and extensive miRNA response, while the susceptible RP plant showed limited miRNA activation. As BPH infestation progressed into the late stage, both B1415 and RP plants began to respond in a more similar manner. Fig. 4. Open in a new tab Analysis of DEMs related to BPH resistance in early and late infestation stages of B1415 and RP plants A Venn diagrams of the unique and shared DEMs in early and late infestation stages of the two varieties. Venn diagram showed the number of DEMs in early infestation stage and late infestation stage of RP (left) and B1415 (right) compared to themselves at the non-infested stage B Hierarchical clustering analysis of the potential candidate DEMs which specifically expressed in the B1415 plant of early infestation stage compared to the the non-infested stage (B1415_early/B1415_0). The color bar represents fold-change values shown in the log 2 scale and asterisks indicate significant differences between the indicated comparisons (** P < 0.01) A total of 99 DEMs were unique to B1415 at early or late BPH infestation stages, with 95 DEMs identified in either B1415_early/B1415_0 or B1415_late/B1415_0, and 4 DEMs expressed in both stages (Fig. 4 A, Supplementary Table 5). After thoroughly evaluating both their expression patterns and relevant literature, we identified eight miRNAs (miR172d-3p [ 32 ], miR396e-5p [ 34 ], miR396f-5p [ 34 ], miR435 [ 34 ], miR444e [ 32 , 35 ], miR444f [ 32 , 35 ], miR528-5p [ 32 , 35 ] and miR5788 [ 40 ]) related to BPH resistance. Except for miR528-5p, most of these miRNAs were rapidly down-regulated in B1415 during the early BPH infestation stage. All have been linked to plant immune responses against BPH or pathogens (Fig. 4 B) [ 26 , 34 , 40 – 45 ]. Cross-analysis of miRNA and mRNA expression profiles miRNAs typically modulate target mRNAs by degrading mRNAs and attenuating translation. To explore this relationship, we used the psRobot toolbox to predict the target genes of miRNAs unveiled in this investigation. We obtained 587 miRNA-mRNA pairs and cross-checked their expression pattern with the miRNA sequencing data and previously published RNA seq-data [ 36 ]. This integrated analysis revealed 537 predicted targeted genes, regulated by 95 miRNAs, with each miRNA targeting an average of 5.65 genes (Supplementary Table 6). To assess whether these genes were associated with BPH resistance, we performed GO and KEGG pathway analyses to categorize their functional roles. GO analysis unveiled that the targeted genes were enriched in biological processes (phenylpropanoid metabolic process and lignin metabolic process), cellular components (the apoplast and extracellular region), and molecular functions (copper ion binding and RNA polymerase II transcription regulatory region and sequence-specific DNA binding) (Fig. 5 A, Supplementary Table 7). KEGG analysis uncovered that the targeted genes were primarily enriched in circadian rhythm-plant and glycine, serine, and threonine metabolism (Fig. 5 B, Supplementary Table 8). To further investigate the role of miRNA-mRNA pairs in resistance, we analyzed the expression levels of BPH14 and BPH15 at different feeding stages in the resistant B1415 plant. qRT-PCR validation revealed that BPH15 expression was significantly up-regulated in response to BPH feeding during both the early and late infestation stages. In contrast, while BPH14 expression was also increased by BPH feeding, no significant difference was observed during infestation. These results are consistent with the miRNA sequencing data (Fig. 5 C). Among the eight BPH resistance-related miRNAs (Fig. 4 B), we observed negative correlations with seven target mRNAs that were only differentially expressed in B1415 during the early or late BPH infestation stages (Fig. 5 D). These miRNA-mRNA pairs were identified as candidate BPH resistance genes, with some target genes known to be linked to plant resistance mechanisms [ 28 , 46 – 49 ]. Most of these miRNA-mRNA pairs, except miR528-5p/Os03g0129400, showed a pattern of miRNA down-regulation in the early infestation stage in B1415 plants, paired with up-regulated mRNAs during the early and late infestation stages (Fig. 5 D). Fig. 5. Open in a new tab The miRNA-mRNA joint analysis related to plant resistance A Gene ontology (GO) analysis. Biological processes, cellular components, and molecular functions of the miRNA-targeted genes. The x- and y-axes indicate the names of the clusters and the P -value in the -log 10 scale of genes in a category, respectively B Kyoto encyclopedia of genes and genomes (KEGG) analysis. KEGG pathway enrichment analysis of the miRNA-targeted genes. The x- and y-axes indicate the rich factor of each pathway and the pathway name, respectively. The bubble size indicates the number of genes. The color bar indicates the P -value C Expression validation of BPH14 and BPH15 in the B1415 plant by qRT-PCR. The TBP were used as internal reference. Data represent the mean ± SE of three independent biological repeats. Data were subjected to Student’s t-test, and asterisks indicate significant differences between B1415_0 and the other indicated group (* P < 0.05) D Hierarchical clustering analysis of potential BPH resistance related miRNA-mRNA interactions in the B1415 plant. The color bar represents fold-change values shown in the log 2 scale and asterisks indicate significant differences between the indicated comparisons (* P < 0.05; ** P < 0.01) Verification of miRNAs’ repressive effects on their target genes The miR172d-3p and miR396 family members have been widely reported to be related to pathogen and herbivore resistance [ 28 , 34 , 41 , 42 , 44 , 45 ]. To confirm miRNAs’ negative regulation on their target genes, we focused on miR172d-3p, miR396e-5p, and miR396f-5p, which were predicted to target genes encoding a nodulin-like protein (Os06g0708700) and an AP2 domain transcription factor (Os11g0129700), both of which are involved in resistance regulation [ 46 , 47 ]. The combined analysis uncovered that these miRNA-mRNA pairs could be instrumental in how rice reacts to BPH infestation (Fig. 5 D). qRT-PCR validation of these miRNAs and their target genes unveiled that miR172d-3p, miR396e-5p, and miR396f-5p negatively modulated Os06g0708700 and Os11g0129700 expression in B1415_early/B1415_0 and B1415_late/B1415_0 groups (Fig. 6 A). We then tested the negative regulations using an agroinfiltration-based transient leaf transformation assay in Nicotiana benthamiana . Precursor miR172d-3p was co-expressed with GFP or GFP-fused Os06g0708700 (Os06g0708700-GFP) in tobacco leaves (Fig. 6 B). MiR396e-5p was selected as a negative control since it does not engage with the Os06g0708700 gene (Supplementary Table 6). The findings revealed that co-expressing miR172d-3p with Os06g0708700-GFP significantly decreased GFP fluorescence signals, whereas no suppression was noted when miR172d-3p was co-expressed with GFP alone (Fig. 6 B). Similarly, the expression of Os11g0129700 was downregulated when co-expressed with miR396e-5p or miR396f-5p (Fig. 6 C and D). Western blotting confirmed that the protein expression levels corresponded to the observed GFP fluorescence signals (Fig. 6 B and D, Supplementary Fig. 1). These results demonstrate that miR172d-3p specifically reduces Os06g0708700 expression, while miR396e-5p and miR396f-5p decrease Os11g0129700 expression. Fig. 6. Open in a new tab Assessment of negative regulation of miRNAs on their predicted target genes A Contrasting expression validation of miRNAs (miR172d-3p, miR396e-5p and miR396f-5p) and their targets (Os06g0708700 and Os11g0129700) in the B1415 plant by qRT-PCR. The U6 and TBP were used as internal references, respectively. Data represent the mean ± SE of three independent biological repeats. Data were subjected to Student’s t-test, and asterisks indicate significant differences between the indicated comparisons (* P < 0.05; ** P < 0.01) B-D Fluorescence photograph of N. benthamiana leaves co-expressing miRNAs with GFP or their targets. The miR396e-5p in B and miR172d-3p in C and D were used as a negative control, respectively. Protein immunoblotting analysis of GFP and the target genes expressed in N. benthamiana leaves were shown below using anti-GFP antibody. Ponceau staining of the rubisco small subunit was used to demonstrate equal loading and the molecular masses (in kilodaltons) are indicated in B-D Discussion Increasing evidence suggests that miRNAs are crucial for plant responses to insect stress [ 27 – 31 ]. However, limited research has focused on the defense mechanisms utilized by B1415, which exhibits durable and effective resistance against BPH [ 12 ]. This study explored the miRNA dynamics and comprehensively analyzed miRNA and mRNA sequencing data to understand how B1415 responds to BPH infestation. This analysis sheds light on the miRNA response to herbivore insects and helps clarify the regulatory mechanisms governing miRNA-mRNA interactions in B1415 during BPH attacks. The reliability of the small RNA sequencing data is supported by principal component analysis (PCA), which revealed clear differentiation among the experimental groups (Fig. 1 B). The distinct separation in the PCA plot validates the robustness and quality of the data, ensuring that subsequent analyses and conclusions are based on reliable, well-characterized miRNA profiles. By comparing miRNA expression in B1415 and RP prior to and after BPH infestation, we identified 550 known miRNAs (Supplementary Table 2). Of them, 136 exhibited differential expression, indicating that BPH infestation altered miRNA expression profiles in rice (Fig. 2 A, Supplementary Table 3). Notably, significantly more DEMs were found in B1415 compared to RP during the early BPH infestation stage (Fig. 2 A). Previous studies show that during the early infestation stage, the insect prepares for sustained infestation by transiently puncturing the epidermis, penetrating cell walls, salivating into the cells, and ingesting phloem sap [ 50 ]. At this stage, plants have not yet suffered severe damage. The increased miRNA activity in B1415 implies that certain miRNAs rapidly respond to BPH herbivory, acting as central signal messengers that activate target genes to defend against potential damage as BPH infestation progresses. Interestingly, we have observed a similar but weaker miRNA expression trend in P15 plants (carrying the BPH15 gene) compared to their susceptible recipient line during the early infestation [ 32 ], suggesting that BPH14 gene contributes to the stronger resistance of the B1415 plants, which carry both BPH14 and BPH15 genes. On the other hand, fewer DEMs were identified in resistant BPH6G plants (carrying BPH6 gene) compared to their susceptible wild type counterparts during the early BPH infestation, indicating that different BPH resistance genes may mediate distinct resistance mechanisms [ 33 ]. 93 DEMs were unveiled in B1415 vs. RP prior to and after BPH infestation (Fig. 3 A). Before infestation, 6 DEMs were detected in the B1415_0/RP_0 comparison, all of which were down-regulated in B1415. This implies that BPH resistance-related target genes of these DEMs are predominantly up-regulated in B1415 prior to BPH attack, aligning with previous findings that more genes are up-regulated in B1415 plants [ 36 ]. Among these miRNAs, miR1882e-3p, previously linked to basal resistance to rice blast in OsDCL1 RNAi lines, was also down-regulated in B1415_0/RP_0, indicating a role in innate immune responses to both pathogens and herbivores [ 37 ]. Additionally, miR1882h, known for regulating BPH fecundity, was differentially expressed in resistant rice as opposed to susceptible varieties, suggesting that it may influence BPH population dynamics and thereby limit damage to rice plants [ 38 ]. These findings imply that B1415 plants are in a primed state, regulated by DEMs even before BPH attacks. After BPH infestation, 85 DEMs were detected in B1415_early/RP_early group (Fig. 3 A), significantly more than that in either B1415_0/RP_0 or B1415_late/RP_late, indicating a rapid activation of many DEMs in response to BPH infestation (Fig. 3 A). Most of these DEMs have been previously associated with BPH-responsive miRNAs, suggesting both conservation and diversification in miRNA-mediated BPH resistance (Fig. 3 B) [ 32 – 35 ]. MiR160f-3p level was reduced in both resistant and susceptible rice plants, consistent with our findings [ 32 ]. MiR160 targets auxin response factors, key elements in plant development and stress adaptation. This down-regulation likely leads to the up-regulation of its target genes that enhance defense mechanisms [ 39 ]. MiR1859, up-regulated in this study, has been found to respond variably to BPH infestation, suggesting it may modulate different target genes in diverse metabolic processes, enhancing BPH resistance [ 33 , 34 ]. MiR5072, an abundant miRNA regulated by various stressors such as drought, salinity and herbivores like BPH [ 35 , 51 ], also showed up-regulation in this study, indicating its potential role in priming plants for enhanced defense responses. MiR530 family (miR530-3p and miR530-5p) were differentially expressed in interaction with BPH infestation [ 32 , 34 ]. MiR530-5p inhibits Os03g0766900, an AOS gene key to the jasmonic acid signaling, a critical pathway for rice defense against BPH infestation [ 34 ]. Furthermore, miR827 displayed differential expression responding to BPH, pathogens, and abiotic stress, such as cold [ 18 , 32 , 35 ], reflecting the complex regulatory network between biotic and abiotic stress mediated by miR827 [ 52 ]. In B1415, 99 DEMs were identified at the early or late BPH infestation stages, with a significant majority (90 of 99) expressed specifically during the early stage (Fig. 4 A, Supplementary Table 5). These findings suggest that the plant rapidly activates a large number of resistance-related DEMs to initiate a robust defense response early in the interaction with BPH. The fewer DEMs present at the late infestation stage (5 of 99 DEMs) may indicate that the initial defense responses are compelling enough to manage the BPH threat, or that the plant’s defense strategy shifts towards other resistance-related genes as the infestation progresses. This shift aligns with previous studies showing fewer DEGs in B1415 plants early in infestation and more at later stages [ 36 ]. Eight DEMs were identified as potential BPH resistance candidates (Fig. 4 B). miR172, a known regulator of plant innate immunity, modulates the FLS2 gene, which is crucial for pathogen recognition [ 42 ]. Its role in resistance to Phytophthora infestans in tomato and Magnaporthe oryzae in rice has been documented [ 44 , 45 ]. In this study, miR172d was significantly down-regulated, supporting its involvement in rice defense against BPH [ 34 ]. The miR396 family members, including miR396e and miR396f, have been linked to rice resistance to fungi like M. oryzae [ 41 ]. These miRNAs were also down-regulated in resistant B1415 plants upon BPH infestation, indicating a role in both pathogen and herbivore resistance. MiR435, previously implicated in negatively modulating BPH defense in the IR56 variety, was down-regulated in this study, indicating activation of rice resistance to BPH. Its predicted target, α/β-hydrolase, a core component of gibberellin (GA), suggests that miR435 might regulate insect resistance via the GA signaling pathway [ 34 , 53 , 54 ]. The miR444 and miR528 families, typically involved in viral infection responses, were up-regulated and down-regulated, respectively, responding to rice stripe virus (RSV) infection and in B1415 plants during BPH infestation, suggesting that they may regulate rice immunity against BPH and viral pathogens through distinct mechanisms [ 26 , 40 ]. MiR5788 was highlighted as a key player in rice’s defense against BPH, consistent with its regulation by certain circular RNAs (circRNAs) in other studies [ 40 ]. A comprehensive analysis of miRNA and mRNA expression profiles offers a robust approach for identifying functional miRNA-mRNA pairs that play crucial roles in host-insect interactions. In this study, 587 miRNA-mRNA pairs were identified, involving 95 DEMs and 537 targeted genes (Supplementary Table 6). These miRNA-mRNA interactions help elucidate the regulatory networks governing rice defense mechanisms against BPH infestation. GO analysis unveiled that DEMs-targeted genes were enriched in the phenylpropanoid metabolic process and lignin metabolic process (Fig. 5 A). These pathways are critical for plant defense as phenylpropanoids and lignin help reinforce cell walls, enhancing resistance to pathogens and herbivores [ 55 , 56 ]. KEGG analysis uncovered that the targeted genes were primarily enriched in circadian rhythm pathways and glycine, serine, and threonine metabolism (Fig. 5 B). This suggests that rice defense responses against BPH may be modulated jointly by internal circadian clocks and amino acid metabolism, potentially optimizing plants’ resilience to biotic stresses [ 57 ]. BPH15 expression was significantly up-regulated in rice exposed to pathogens or BPH infestation [ 6 ], indicating that stress conditions trigger its expression. Our study further supports this, showing a significant increase in BPH15 expression during both the early and late stages of BPH infestation (Fig. 5 C). These findings suggest that BPH15 plays a key role in the plant’s defense mechanisms and may be a crucial target for miRNAs regulating this response. In contrast, while BPH14 expression was also enhanced by BPH feeding, it did not reach a significant threshold (Fig. 5 C). Similarly, previous studies have shown increased BPH14 expression after BPH feeding, but without significant differences between resistant (RI35) and susceptible (Kasalath) plants [ 5 ]. These results suggest that functional insect resistance is likely driven more by sequence variations in the coding region than by differences in transcription levels. Furthermore, miRNAs may regulate BPH14 -mediated resistance through indirect pathways, such as salicylic acid signaling, which activates defense responses during BPH infestation [ 8 ]. Several key miRNA-mRNA pairs linked to BPH resistance were identified (Fig. 5 D). For instance, miR172d-3p was found to target Os06g0708700 (encoding a nodulin-like protein), negatively regulating its expression (Fig. 6 A and B). Nodulin-like protein is known to mediate plant defense responses to pathogens by regulating defense-related gene expression [ 46 ]. Previous studies have also confirmed the involvement of miR172 family members in BPH resistance in rice [ 34 ]. Therefore, miR172d-3p may be vital for BPH resistance in rice by targeting Os06g0708700. Additionally, the miR396 family, including miR396e-5p and miR396f-5p, was shown to negatively regulate Os11g0129700 (Fig. 6 A, C and D), which encodes an AP2 domain transcription factor, a critical regulator of plant defense responses [ 47 ]. This implies that miR396 may be essential in modulating rice defense against BPH through its impact on target gene expression. Additionally, miR396e-5p and miR396f-5p were found to negatively regulate Os06g0204800 (encoding growth-regulating factor 2), aligning with previous findings that miR396 targets and negatively regulates OsGRFs, attenuating both BPH and blast disease resistance [ 28 , 41 ]. Therefore, miR396-OsGRFs module represents a promising candidate for rice resistance breeding. Two other important miRNA-mRNA pairs identified were miR435 and miR5788, which target Os02g0153100 (encoding a leucine-rich repeat receptor-like kinase) and Os06g0164900 (encoding S-domain receptor-like kinase-66), respectively. These kinases are vital components of rice defense mechanisms, involved in recognizing pathogens, signal transduction, and activating defense responses [ 48 , 49 ]. Hence, miR435 and miR5788 likely aid in conferring resistance to BPH by modulating these key genes. Our study focuses on the small RNA response of B1415 plant to BPH infestation. However, BPH may also produce small RNAs that influence B1415 plant defense, as suggested by previous research [ 17 ]. While this study provides insights into B1415 plant miRNA regulation, a more comprehensive approach that includes BPH-derived small RNAs could offer a more holistic view of the plant-insect interaction. Future research should investigate the role of BPH-derived small RNAs in shaping B1415 plant responses. Profiling these small RNAs and examining their interactions with B1415 plant miRNAs and other non-coding RNAs could deepen our understanding of the co-evolutionary dynamics between rice and BPH. Conclusions This investigation is the first to examine miRNA expression profiles in B1415, known for its durable resistance to BPH and extensive use in breeding BPH-resistant rice varieties. Through high-throughput sequencing, we analyzed miRNA transcriptome before and after BPH infestation, revealing key miRNA-mRNA regulatory networks contributing to B1415’s BPH resistance. The pyramiding line showed a rapid and extensive miRNA response during the early infestation stage, suggesting a proactive defense strategy. A total of 587 miRNA-mRNA pairs were uncovered, with targeted genes significantly enriched in phenylpropanoid and lignin metabolism pathways, crucial for reinforcing plant cell walls against pathogens. Additionally, pathways related to circadian rhythms and amino acid metabolism were highlighted, emphasizing their role in optimizing defense strategies against BPH. Key candidates, including miR172d-3p and the miR396 family (miR396e-5p and miR396f-5p), were shown to negatively regulate target genes Os06g0708700 and Os11g0129700, respectively, supporting their roles in modulating rice defense responses. This study deepens our knowledge of miRNA-mediated interactions between BPH resistance gene pyramiding rice lines and insects, offering key insights for augmenting BPH resistance in rice. Methods Plants and insect materials The pyramiding line containing both BPH14 and BPH15 genes (B1415) was created using the indica cultivar ‘Wushansimiao’ as the recurrent parent (RP) [ 12 ]. ‘Wushansimiao’ is an elite rice cultivar developed by the Guangdong Academy of Agricultural Sciences, widely cultivated in central and southern China, but highly susceptible to BPH [ 12 ]. Rice seeds were sown in soil-filled plastic containers (15 cm tall × 9 cm wide), with 15 seedlings per container, and grown under controlled conditions of 14 h light (32 ± 2°C)/10 h dark (26 ± 2°C). The soil was collected from natural fields at the Genetics Institute of Wuhan University (latitude: 30°32’32” N, longitude: 114°22’0” E), thoroughly mixed with a stirring machine to ensure uniformity, but not sterilized. BPH populations were maintained on the susceptible indica cultivar ‘Taichung Native 1’ (TN1, IRRI Acc. No. 00105), which lacks known BPH resistance genes [ 12 , 33 ], under the same environmental conditions. BPH infestation and sample collection Sample collection and BPH treatments were executed following the endpoint method [ 36 ]. Despite varying start times, all treatments were concluded concurrently. Four-leaf stage seedlings of B1415 and RP were infested with eight 3rd instar BPH nymphs per seedling at 0, 3, 6, 12, 24, 48, and 72 h with 15 seedlings each in three biological replicates. The leaf sheath samples were categorized into three stages based on BPH infestation: non-infested (0 h), for seedlings not yet infested; early infestation (3, 6, 12 h), representing the early stage of infestation, with samples collected at 3, 6, and 12 h post-infestation; and late infestation (24, 48, 72 h), representing late stage of infestation, with samples collected at 24, 48, and 72 h post-infestation. These stages followed prior methodology [ 36 ] and were designated as B1415_0, B1415_early, B1415_late, RP_0, RP_early, and RP_late, respectively. All samples were frozen, ground, and preserved in TRIzol (Invitrogen Life Technologies) at -80 °C. Small RNA library preparation and sequencing Total RNA was extracted using TRIzol, and its quantity, quality, and integrity were determined using NanoDrop (Thermo Scientific). Eighteen small RNA libraries were produced using the NEBNext Multiplex Small RNA Library Prep Set for Illumina (New England Biolabs, Inc.), encompassing samples from B1415 and RP at non-infestation (B1415_0, RP_0), early infestation (B1415_early, RP_early), and late infestation (B1415_late, RP_late) stages, each with three biological replicates. The small RNA libraries underwent quantification using a high-sensitivity DNA Kit on a Bioanalyzer 2100 system (Agilent) prior to sequencing on the Illumina NovaSeq 6000 platform. The NCBI accession number for the developed small RNA library is GSE278202 . Bioinformatics analysis of small RNA sequences Data were filtered by abandoning adapter sequences and low-quality reads. The obtained clean reads, ranging from 18 nt to 36 nt in length, underwent deduplication to generate unique reads for further exploration. These reads were matched to the Rfam (ver. 13.0) database using BLAST to identify rRNA, tRNA, snRNA, and snoRNA, adhering to a criterion of no more than two mismatches. Unclassified reads were matched to the miRBase database (ver. 22.0) using BLAST with the same criterion to identify mature miRNA sequences. The identified known miRNAs were classified into families by aligning them with reference miRNAs from the miRBase database. Unmapped reads were analyzed against the genome using the Mireap online program to predict novel miRNAs. Analysis of differentially expressed miRNAs (DEMs) The read count values for miRNA were determined by tallying the sequences aligned to mature miRNAs. To normalize these counts, they were expressed as count per million (CPM). PCA was conducted using the DESeq package in R to assess data variance. DESeq (v1.39.0) was employed to analyze DEMs following the criteria of |log 2 Fold Change (FC)| > 1 and P < 0.05. Target gene prediction The psRobot v1.2 with default parameters was employed to predict miRNA targets on mRNAs [ 58 ]. PsRobot is a computational tool designed for predicting plant small RNA target genes through sequence alignment. It evaluates base pairing between small RNAs and target sequences by assigning scores based on mismatches, bulges, and other alignment features. In this study, the score threshold was set to 2.5, where lower scores indicate stricter matches and higher scores suggest poorer alignment quality. The number of allowed bulge gaps (unpaired nucleotides within the alignment) was set to the default of one bulge gap, and no adjustments were made to the boundaries of the 5’ and 3’ ends of the small RNA sequences. To perform the prediction, the CDS sequences of the rice genome (retrieved from the Rice Annotation Project Database: https://rapdb.dna.affrc.go.jp/index.html ) and the sequences of mature miRNAs were input into psRobot. This process generated predictions of potential miRNA-target gene interactions for further investigation. GO and KEGG pathway analyses GO and KEGG pathway analyses were executed against the Gene Ontology, UniProt, NCBI and KEGG databases. The biological importance of the identified genes in GO and KEGG categories was statistically defined using Fisher’s exact test ( P < 0.05) [ 36 ]. Real-time quantification of miRNAs and mRNAs Reverse transcription of miRNA and mRNA was carried out with a PrimeScript RT Reagent Kit containing gDNA Eraser (RR047A, TaKaRa). The qRT-PCR was used for real-time quantification of miRNA expression, while quantitative RT-PCR was employed for mRNA expression analysis. Supplementary Table 9 lists the primers. The PCR reactions were carried out on a CFX96 real-time system (Bio-Rad) using SYBR Green Real-Time PCR Master Mix (QPK-201, Toyobo). MiRNA and mRNA quantities were normalized to the internal references U6 and TBP , respectively [ 33 ]. Vector construction The precursor miRNAs (pre-miR172d-3p, pre-miR396e-5p, and pre-miR396f-5p) and the coding sequence of their targets (Os06g0708700 and Os11g0129700), excluding stop codons, were amplified from ‘Wushansimiao’ cDNA. The precursor miRNAs were cloned into the pcambia3301-GFP vector by replacing GFP using Xba I- Bgl II restriction sites and ligation. Similarly, target gene’ coding sequence were cloned into plasmid pcambia3301-GFP using Xba I- BamH I restriction sites and ligation, placing them in front of the GFP segment to create Os06g0708700-GFP and Os11g0129700-GFP fusion vectors [ 59 ]. The primers used for constructing the miRNA and gene constructs are detailed in Supplementary Table 9. Transient expression and fluorescence signal observation in Nicotiana benthamiana ( N. benthamiana ) N. benthamiana (wild tobacco) plants were cultivated in a chamber at 25 °C under LED yellow light with an intensity of 10,000 lx and a 14 h light/10 h dark cycle. The constructs expressing the specified miRNAs and genes were individually transformed into Agrobacterium tumefaciens GV3101 strain and transiently co-infiltrated into N . benthamiana leaves [ 8 ]. After 3 days of infiltration, the leaves were imaged using a portable GFP observation lamp (LUYOR-3415UR). Protein extraction and immunoblotting analysis After 3 days of infiltration, leaf samples from N . benthamiana were harvested. Total soluble proteins from the infiltrated leaf area were extracted using RIPA buffer (50 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1% Triton X-100, 1% C 24 H 39 NaO 4 , 1 mM EDTA, 0.1% SDS, 10 mM NaF, 1 mM Na 3 VO 4 , and 1 mM PMSF). Following boiling and centrifugation, 10 µL supernatant were separated by SDS-PAGE. GFP-fused proteins were immunoblotted using an anti-GFP antibody (MBL; catalog no. M048-3, 1:1000 in 20 mM Tris-HCl, pH 7.4, 150 mM NaCl, 0.1% Tween 20, and 5% BSA) and a HRP-conjugated secondary antibody (Jackson; catalog no. 115-035-003, 1:10,000 in 20 mM Tris-HCl, pH 7.4, 150 mM NaCl, 0.1% Tween 20, and 5% skimmed milk). Statistical analyses and reproducibility All experiments were executed in triplicates, yielding similar results and statistically appraised using Student’s t-tests at P < 0.05. Supplementary Information 12870_2026_8485_MOESM1_ESM.tif (8.4MB, tif) Supplementary Material 1: Supplementary Figure 1. Original blots showing cropped areas for making Figs. 6B to 6D. (A) The red box in the original blot indicated the cropped areas of the GFP proteins in Figs. 6B to 6D, respectively. Asterisk indicated nonspecific signals. (B) The red box in the original blot indicated the cropped area of the Os06g0708700-GFP proteins in Fig. 6B. (C) The red box in the original blot indicated the cropped area of the Os11g0129700-GFP proteins in Fig. 6C. (D) The red box in the original blot indicated the cropped area of the Os11g0129700-GFP proteins in Fig. 6D. Asterisks indicated nonspecific signals. (E) The red box in the original image indicated the cropped areas of the ponceau staining of the rubisco small subunit in Figs. 6B to 6D, respectively. The numbers below the original blots in (A-D) indicated the exposure time (s) and the samples are labelled with the corresponding labels as shown in Figs. 6B to 6D throughout. 12870_2026_8485_MOESM2_ESM.xlsx (12.1KB, xlsx) Supplementary Material 2: Supplementary Table 1. Small RNA sequences data. 12870_2026_8485_MOESM3_ESM.xlsx (346.4KB, xlsx) Supplementary Material 3: Supplementary Table 2. Detailed information for all 550 known miRNAs. 12870_2026_8485_MOESM4_ESM.xlsx (46.8KB, xlsx) Supplementary Material 4: Supplementary Table 3. Comprehensive details of all 136 DEMs identified in this study. 12870_2026_8485_MOESM5_ESM.xlsx (22.4KB, xlsx) Supplementary Material 5: Supplementary Table 4. Detailed information of DEMs between B1415 and RP prior to and after BPH infestation. 12870_2026_8485_MOESM6_ESM.xlsx (31.7KB, xlsx) Supplementary Material 6: Supplementary Table 5. Detailed information of DEMs at early and late infestation stages in B1415 and RP. 12870_2026_8485_MOESM7_ESM.xlsx (71.4KB, xlsx) Supplementary Material 7: Supplementary Table 6. DEMs and their target genes at early and late infestation stages in B1415 and RP. 12870_2026_8485_MOESM8_ESM.xlsx (17.3KB, xlsx) Supplementary Material 8: Supplementary Table 7. GO enrichment analysis of the miRNA-targeted genes at early and late infestation stages in B1415 and RP. 12870_2026_8485_MOESM9_ESM.xlsx (15.1KB, xlsx) Supplementary Material 9: Supplementary Table 8. KEGG pathway enrichment analysis of the miRNA-targeted genes in early and late infestation stages in B1415 and RP. 12870_2026_8485_MOESM10_ESM.xlsx (167KB, xlsx) Supplementary Material 10: Supplementary Table 9. Primers used in this study. Acknowledgements We are thankful to TopEdit for their assistance in language editing. Abbreviations BPH Brown planthopper B1415 BPH14/BPH15 pyramiding line RP Recurrent parent DEM Differentially expressed microRNA DEG Differentially expressed gene IRRI International Rice Research Institute GO Gene ontology KEGG Kyoto encyclopedia of genes and genomes Authors’ contributions The research was conceptualized and designed by BW, AY, and LH. Experiments were carried out by LH, DY, HW, XD, LL, JL, MX, HQ, WZ, YW, and TM. Data analysis was performed by LH and DY, while HW contributed to cultivating B1415 and RP plants. LH, AY, and BW wrote the manuscript, and DY and HW assisted with editing. LH, DY, and HW equally contributed to this paper. All authors ratified the final manuscript. Funding This work was funded by the National Natural Science Foundation of China (No. 32301918), the Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement Foundation (No. 2024lzjj04), the Hubei Academy of Agricultural Science Foundation (No. 2023NKYJJ02), the Hubei Provincial Key Research and Development Program (No. 2023BBB031), and the Open Research Fund of Key Laboratory of Integrated Pests Management on Crops in Central China, Ministry of Agriculture and Rural Affairs/Hubei Key Laboratory of Crop Diseases, Insect Pests and Weeds Control (No. 2023ZTSJJ6). Data availability The datasets produced and analyzed in this study are available at the National Center for Biotechnology Information (NCBI) under the GEO series accession No. GSE278202 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE278202 ). Declarations Ethics approval and consent to participate N/A. Consent for publication N/A. Competing interests The authors declare no competing interests. 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[ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials 12870_2026_8485_MOESM1_ESM.tif (8.4MB, tif) Supplementary Material 1: Supplementary Figure 1. Original blots showing cropped areas for making Figs. 6B to 6D. (A) The red box in the original blot indicated the cropped areas of the GFP proteins in Figs. 6B to 6D, respectively. Asterisk indicated nonspecific signals. (B) The red box in the original blot indicated the cropped area of the Os06g0708700-GFP proteins in Fig. 6B. (C) The red box in the original blot indicated the cropped area of the Os11g0129700-GFP proteins in Fig. 6C. (D) The red box in the original blot indicated the cropped area of the Os11g0129700-GFP proteins in Fig. 6D. Asterisks indicated nonspecific signals. (E) The red box in the original image indicated the cropped areas of the ponceau staining of the rubisco small subunit in Figs. 6B to 6D, respectively. The numbers below the original blots in (A-D) indicated the exposure time (s) and the samples are labelled with the corresponding labels as shown in Figs. 6B to 6D throughout. 12870_2026_8485_MOESM2_ESM.xlsx (12.1KB, xlsx) Supplementary Material 2: Supplementary Table 1. Small RNA sequences data. 12870_2026_8485_MOESM3_ESM.xlsx (346.4KB, xlsx) Supplementary Material 3: Supplementary Table 2. Detailed information for all 550 known miRNAs. 12870_2026_8485_MOESM4_ESM.xlsx (46.8KB, xlsx) Supplementary Material 4: Supplementary Table 3. Comprehensive details of all 136 DEMs identified in this study. 12870_2026_8485_MOESM5_ESM.xlsx (22.4KB, xlsx) Supplementary Material 5: Supplementary Table 4. Detailed information of DEMs between B1415 and RP prior to and after BPH infestation. 12870_2026_8485_MOESM6_ESM.xlsx (31.7KB, xlsx) Supplementary Material 6: Supplementary Table 5. Detailed information of DEMs at early and late infestation stages in B1415 and RP. 12870_2026_8485_MOESM7_ESM.xlsx (71.4KB, xlsx) Supplementary Material 7: Supplementary Table 6. DEMs and their target genes at early and late infestation stages in B1415 and RP. 12870_2026_8485_MOESM8_ESM.xlsx (17.3KB, xlsx) Supplementary Material 8: Supplementary Table 7. GO enrichment analysis of the miRNA-targeted genes at early and late infestation stages in B1415 and RP. 12870_2026_8485_MOESM9_ESM.xlsx (15.1KB, xlsx) Supplementary Material 9: Supplementary Table 8. KEGG pathway enrichment analysis of the miRNA-targeted genes in early and late infestation stages in B1415 and RP. 12870_2026_8485_MOESM10_ESM.xlsx (167KB, xlsx) Supplementary Material 10: Supplementary Table 9. Primers used in this study. Data Availability Statement The datasets produced and analyzed in this study are available at the National Center for Biotechnology Information (NCBI) under the GEO series accession No. GSE278202 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE278202 ). 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