Sheath blight disease (ShB) is considered to be the second most important disease affecting rice, and the genetic mechanism of ShB resistance in rice is great complicated. Uncovering genetic mechanism of ShB resistance and strong resistant varieties in rice are the premise for ShB resistance improvement. A rice ShB genome-wide association study (GWAS) was performed using approximately five million SNPs within Ting’s core collection. “Early pradifice”, one typical japonica, was determined to be the most resistant variety in both 2016 and 2017. A total of 34 and four significant (p ≤ 1.93 × 10⁻⁸) SNPs were observed in 2016 and 2017, respectively. Moreover, 23 of 34 and two of four gene-based SNPs not reported in previous studies in 2016 and 2017, respectively, were identified as significantly associated with rice ShB resistance. Furthermore, we performed GO (gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses for the genes located at a region within 10 kb of the significant SNPs. Both in 2016 and 2017, we observed that genes were uniquely enriched in the regulation of transcription and RNA processing in the category of “biological process”, plasma membrane, nucleus, integral component of membrane and cell wall in the category of “cellular component”, and ATP binding in the category of “molecular function”. The results of the present study may establish a foundation for further research investigating these elite genes and utilizing the resistant varieties in Ting’s core collection to improve rice ShB resistance.

Protein–protein interactions (PPIs) are significant in understanding the complex molecular processes of plant growth, disease resistance, and stress responses. Machine learning (ML) has recently emerged as a powerful tool that can predict and analyze PPIs, offering complementary insights into traditional experimental approaches. It also accounts for proteoforms, distinct molecular variants of proteins arising from alternative splicing, or genetic variations and modifications, which can significantly influence PPI dynamics and specificity in rice. This review presents a comprehensive summary of ML-based methods for PPI predictions in rice (Oryza sativa) based on recent developments in algorithmic innovation, feature extraction processes, and computational resources. We present applications of these models in the discovery of candidate genes, unknown protein annotations, identification of plant–pathogen interactions, and precision breeding. Case studies demonstrate the utility of ML-based methods in improving rice resistance to abiotic and biotic stresses. Additionally, this review highlights key challenges like data limits, model generalizability, and future directions like multi-omics, deep learning and artificial intelligence (AI). This review provides a roadmap for researchers aiming to use ML to generate predictive and mechanistic insights on rice PPI networks, hence helping to achieve enhanced crop improvement programs.

Heterosis is the phenomenon in which some hybrid traits are superior to those of their parents. Most studies have analyzed the heterosis of agronomic traits of crops; however, heterosis of the panicles can improve yield and is important for crop breeding. Therefore, a systematic study of panicle heterosis is needed, especially during the reproductive stage. RNA sequencing (RNA Seq) and transcriptome analysis are suitable for further study of heterosis. Using the Illumina Nova Seq platform, the transcriptome of ZhongZheYou 10 (ZZY10), an elite rice hybrid, the maintainer line ZhongZhe B (ZZB), and the restorer line Z7-10 were analyzed at the heading date in Hangzhou, 2022. 581 million high-quality short reads were obtained by sequencing and were aligned against the Nipponbare reference genome. A total of 9000 differential expression genes were found between the hybrids and their parents (DG_HP). Of the DG_HP, 60.71% were up-regulated and 39.29% were down-regulated in the hybrid. Comparative transcriptome analysis revealed that 5235 and 3765 DG_HP were between ZZY10 and ZhongZhe B and between ZZY10 and Z7-10, respectively. This result is consistent with the transcriptome profile of ZZY10 and was similar to Z7-10. The expression patterns of DG_HP mainly exhibited over-dominance, under-dominance, and additivity. Among the DG_HP-involved GO terms, pathways such as photosynthesis, DNA integration, cell wall modification, thylakoid, and photosystem were significant. 21 DG_HP, which were involved in photosynthesis, and 17 random DG_HP were selected for qRT-PCR validation. The up-regulated PsbQ and down-regulated subunits of PSI and PSII and photosynthetic electron transport in the photosynthesis pathway were observed in our study. Extensive transcriptome data were obtained by RNA-Seq, providing a comprehensive overview of panicle transcriptomes at the heading stage in a heterotic hybrid.

As an important global food crop, rice is damaged by a variety of piercing–sucking pests. Identifying a broad-spectrum promoter induced by the physical signal of sucking pests and applying it to transgenic breeding to mitigate the damage caused by different sucking pests will significantly improve the efficiency of our breeding. This study compared the transcriptome changes in two rice varieties under needle-wounding stress to investigate their differential responses to mechanical damage. The results showed that the insect-susceptible variety TN1 exhibited more differentially expressed genes (DEGs) and greater changes in expression levels after needle treatment, indicating a more active internal gene regulatory network. GO and KEGG enrichment analysis further revealed that TN1 not only exhibited changes in genes related to the extracellular environment, but also mobilized more genes associated with stress response and defense. By screening the differentially expressed genes, we identified two promoters (P1 and P2) with inducible expression characteristics in both the resistant and susceptible rice varieties. These promoters were able to effectively drive the expression of the insect resistance gene OsLecRK1* and enhance the resistance of transgenic plants against the brown planthopper. This study provides promoter resources for the development of insect-resistant transgenic crops.

Alkane is a predominant wax component, whose production requires the aids of CER1 and CER3. In rice, OsCER1 and OsCER3 are present in multiple copies. Until now, the roles of these genes have been studied individually; however, a systematic comparison of their relative contributions to cuticular wax biosynthesis has not yet been carried out. Phylogenetic tree analysis revealed that CER1s and CER3s from different plants are classified into two subgroups. RT-qPCR analysis showed that these genes display distinct expression patterns, revealing their specific roles in wax production. Promoter prediction analysis showed that cis-elements responding to light, phytohormones and stress are enriched in the promoter region of OsCER1s and OsCER3s. These proteins are all localized in the endoplasmic reticulum. Further study showed that OsCER1s and OsCER3s are inclined to form a complex during the wax synthesis. Finally, the wax analysis of single mutants showed that among the examined genes, OsCER3a mutation greatly reduced the total wax amounts to 19.6% of wild-type plant with a decrease in most of wax components, whereas mutation of other genes including OsCER3b, OsCER3c, OsCER1a and OsCER1c slightly or barely affect wax production, suggesting that OsCER3a plays major roles in rice wax production whereas other proteins redundantly participate in the wax synthesis. Additionally, the wax increasing rates of Arabidopsis expressing OSCER1 are lower than those of overexpressing AtCER1. Taken together, our study identified the predominant genes involved in wax production, which will be useful for genetically engineering rice with enhanced stress tolerance.