Summary: Nitrogen (N) is critical for crop growth, and enhancing nitrogen use efficiency (NUE) is essential for achieving high grain yields and quality in crop production. In this study, we identified two transcription factors OsRF2b and OsbZIP61, which localize in the nucleus and function as negative regulators of NUE and grain yield in rice. OsRF2b responds to nitrogen supplies, and overexpression of OsRF2b resulted in growth retardation, reduced nitrogen content, and lower NUE in transgenic lines, leading to decreased grain yield. In contrast, the rf2b mutant exhibited increased plant height, grain size, and NUE, which contribute to improved grain yield. Furthermore, RT‐qPCR results showed that OsRF2b overexpression significantly downregulated OsNRT1.1B and OsNRT2.1/2.2 expression, while the OsRF2b mutation significantly upregulated these genes. Additionally, yeast one‐hybrid, EMSA, and transient expression assays demonstrated that OsRF2b directly binds to the promoter region of OsNRT1.1B and suppresses its expression. Moreover, bimolecular fluorescence complementation (BiFC) and luciferase complementation imaging (LCI) assays indicated that OsRF2b interacts with OsbZIP61, and OsbZIP61 overexpression also decreased rice NUE and grain yield. In conclusion, we identified the bZIP transcription factor OsRF2b, which interacts with OsbZIP61 and acts as negative regulators of NUE and grain yield by forming heterodimers and directly binding to the promoter of OsNRT1.1B and suppressing its expression. These findings provide new insights into the mechanisms underlying two bZIPs involved in NUE and grain yield, which offer a genetic basis for rice breeding in the future.

Improving plant nitrogen-use efficiency (NUE) has great significance for various crops, particularly in hybrid breeding. Reducing nitrogen inputs is key to achieving sustainable rice production and mitigating environmental problems. In this study, we analyzed the transcriptomic and physiological changes in two indica restorer lines (Nanhui511 [NH511] and Minghui23 [MH23]) under high nitrogen (HN) and low nitrogen (LN) conditions. Compared to MH23, NH511 was more sensitive to different nitrogen supplies and exhibited higher nitrogen uptake and NUE under HN conditions by increasing lateral root and tiller numbers in the seedling and maturation stages, respectively. NH511 also exhibited a lower survival rate than MH23 when planted in a chlorate-containing hydroponic solution, indicating its HN uptake ability under different nitrogen-supply conditions. Transcriptomic analysis showed that NH511 has 2456 differentially expressed genes, whereas MH23 had only 266. Furthermore, these genes related to nitrogen utilization showed differential expression in NH511 under HN conditions, while the opposite was observed in MH23. Our findings revealed that NH511 could be regarded as elite rice and used for breeding high-NUE restorer lines by regulating and integrating nitrogen-utilization genes, which provides novel insights for the cultivation of high-NUE hybrid rice.

Plant microRNAs (miRNAs) are endogenous non-coding RNAs (~20–24 nucleotides) that regulate gene expression post-transcriptionally, playing critical roles in plant growth, development, and stress responses. This review systematically examines AI applications in plant miRNA research, tracing evolution from traditional machine learning to deep learning architectures. Plant miRNAs exhibit distinctive features necessitating plant-specific computational approaches: nuclear-localized biogenesis, high target complementarity (>80%), and coding region targeting. These characteristics enable more accurate computational prediction and experimental validation than animal systems. Methodological advances have improved prediction accuracy from ~90% (early SVMs) to >99% (recent deep learning), though metrics reflect different evaluation contexts. We analyze applications across miRNA identification, target prediction with degradome validation, miRNA–lncRNA interactions, and ceRNA networks. Critical assessment reveals that degradome data capture mixed RNA fragments from multiple sources beyond miRNA cleavage, requiring stringent multi-evidence validation. Similarly, fundamental ambiguities in lncRNA definition compound prediction uncertainties. Major challenges include severe data imbalance (positive to negative ratios of 1:100 to 1:10,000), limited cross-species generalization, insufficient model interpretability, and experimental validation bottlenecks. Approximately 75% of plant miRNA families in miRBase v20 lack convincing evidence, underscoring the need for rigorous annotation standards. Future directions encompass multimodal deep learning, explainable AI, spatiotemporal graph neural networks, and ultimately AI-driven de novo miRNA design, though the latter requires substantial advances in both computation and high-throughput validation. This synthesis demonstrates that AI has become indispensable for plant miRNA research, providing essential support for crop improvement while acknowledging persistent challenges demanding continued innovation.

The expansin-like subfamilies (EXLA and EXLB) are vital for plant cell wall dynamics, but it remains uncharacterized in wild tetraploid and hexaploid Ipomoea batatas, and its role in the storage root (SR) development is poorly understood. In this work, we identified 4, 3, 3, and 3 EXLAs, alongside 11, 9, 13, and 8 EXLBs, in diploid I. trifida strain Y22, wild tetraploid I. batatas strain Y428B, and hexaploid I. batatas strain Y601, and cultivated sweet potato ‘Nancy Hall’, respectively. A comprehensive bioinformatic analysis of the expansin-like genes and proteins was performed to reveal their potential roles in SR development. Gene expression profiling showed that EXLA members were expressed during SR development, while approximately half of the EXLB members were expressed in Y22, Y428B (pencil root), Y601, and NH, respectively. Proteomic analysis (4D-DIA) detected 2, 1, 1, and 1 EXLAs, and 3, 3, 3, and 3 EXLBs in the mature SRs of the respective species. Integrated transcriptomic and proteomic analyses suggested that downregulating Iba6xEXLB2 and Iba6xEXLB1 may be associated with SR swelling in sweet potato. Furthermore, subcellular localization assays confirmed that Iba6xEXLB2 and Iba6xEXLB8 are localized to the cell wall/membrane. This study enhances the understanding of the expansin-like gene subfamily in sweet potato and its wild relatives and lays the groundwork for future functional studies on the role of expansin-like genes in SR development.

Improving plant nitrogen use efficiency (NUE) is important for many crops, especially in hybrid breeding. Reducing nitrogen inputs is key for achieving sustainable rice production and decreasing environmental problems. Here, we analyzed the transcriptomic and physiological changes in two indica restorer lines (Nanhui511 and Minghui23) under high (HN) and low nitrogen (LN) conditions. Nanhui511 (NH511) was more sensitive to different nitrogen supplies and exhibited higher nitrogen uptake and NUE under HN conditions by increasing lateral root and tiller numbers in the seedling and maturation stages, respectively. It also exhibited a lower survival rate than that of MH23 when planted in chlorate-containing hydroponic solution, indicating its high nitrogen uptake ability under different nitrogen supply conditions. When transferred from LN to HN conditions, NH511 had 2456 differentially expressed genes, whereas MH23 had only 266. Furthermore, genes related to nitrogen utilization showed differential expression in NH511 under HN conditions, while the opposite was observed in MH23. These results will help decrease certain limitations of hybrid rice breeding and provide novel data for high-NUE hybrid rice cultivation.