Rice (Oryza sativa L.), a staple crop plant that is a major source of calories for approximately 50% of the human population, exhibits various physiological responses against temperature stress. These responses are known mechanisms of flexible adaptation through crosstalk with the intrinsic circadian clock. However, the molecular regulatory network underlining this crosstalk remains poorly understood. Therefore, we performed systematic transcriptome data analyses to identify the genes involved in both cold stress responses and diurnal rhythmic patterns. Here, we first identified cold-regulated genes and then identified diurnal rhythmic genes from those (119 cold-upregulated and 346 cold-downregulated genes). We defined cold-responsive diurnal rhythmic genes as CD genes. We further analyzed the functional features of these CD genes through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses and performed a literature search to identify functionally characterized CD genes. Subsequently, we found that light-harvesting complex proteins involved in photosynthesis strongly associate with the crosstalk. Furthermore, we constructed a protein–protein interaction network encompassing four hub genes and analyzed the roles of the Stay-Green (SGR) gene in regulating crosstalk with sgr mutants. We predict that these findings will provide new insights in understanding the environmental stress response of crop plants against climate change.

The plant U-box (PUB) protein is the E3 ligase that plays roles in the degradation or post-translational modification of target proteins. In rice, 77 U-box proteins were identified and divided into eight classes according to the domain configuration. We performed a phylogenomic analysis by integrating microarray expression data under abiotic stress to the phylogenetic tree context. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) expression analyses identified that eight, twelve, and eight PUB family genes are associated with responses to drought, salinity, and cold stress, respectively. In total, 16 genes showed increased expression in response to three abiotic stresses. Among them, the expression of OsPUB2 in class II and OsPUB33, OsPUB39, and OsPUB41 in class III increased in all three abiotic stresses, indicating their involvement in multiple abiotic stress regulation. In addition, we identified the circadian rhythmic expression for three out of 16 genes responding to abiotic stress through meta-microarray expression data analysis. Among them, OsPUB4 is predicted to be involved in the rice GIGANTEA (OsGI)-mediating diurnal rhythm regulating mechanism. In the last, we constructed predicted protein-protein interaction networks associated with OsPUB4 and OsGI. Our analysis provides essential information to improve environmental stress tolerance mediated by the PUB family members in rice.

Carbohydrate metabolism is an important biochemical process related to developmental growth and yield-related traits. Due to global climate change and rapid population growth, increasing rice yield has become vital. To understand whole carbohydrate metabolism pathways and find related clues for enhancing yield, genes in whole carbohydrate metabolism pathways were systemically dissected using meta-transcriptome data. This study identified 866 carbohydrate genes from the MapMan toolkit and the Kyoto Encyclopedia of Genes and Genomes database split into 11 clusters of different anatomical expression profiles. Analysis of functionally characterized carbohydrate genes revealed that source activity and eating quality are the most well-known functions, and they each have a strong correlation with tissue-preferred clusters. To verify the transcriptomic dissection, three pollen-preferred cluster genes were used and found downregulated in the gori mutant. Finally, we summarized carbohydrate metabolism as a conceptual model in gene clusters associated with morphological traits. This systemic analysis not only provided new insights to improve rice yield but also proposed novel tissue-preferred carbohydrate genes for future research.

The collar region plays a crucial role in leaf angle formation and plant architecture, which is important for improving crop yield given the challenges of diminishing arable land and changing environmental conditions. To determine collar region-preferential genes (CRPGs) affecting plant architecture and crop yield, we conducted genome-wide transcriptomic analysis. By integrating our RNA sequencing data with public rice anatomical expression data, we identified 657 CRPGs. Verification involved testing six randomly selected CRPGs, all of which exhibited collar-preferential expression. The functional significance of CRPGs was assessed via Gene Ontology enrichment analysis, utilizing MapMan and KEGG, and literature analysis provided additional information for characterized CRPGs. Our findings revealed links between manipulating leaf angle and phytohormone-related pathways and stress responses. Moreover, based on the CRPGs, five transcription factors downstream of the liguleless 1 (LG1) gene were identified. Overall, the identified CRPGs provide potential targets for further research and breeding applications aimed at improving crop productivity by manipulating leaf architecture.

Promoters are key components for the application of biotechnological techniques in crop plants. Reporter genes such as GUS or GFP have been used to test the activity of promoters for diverse applications. A huge number of T-DNAs carrying promoterless GUS near their right borders have been inserted into the rice genome, and 105,739 flanking sequence tags from rice lines with this T-DNA insertion have been identified, establishing potential promoter trap lines for 20,899 out of 55,986 genes in the rice genome. Anatomical meta-expression data and information on abiotic stress related to these promoter trap lines enable us to quickly identify new promoters associated with various expression patterns. In the present report, we introduce a strategy to identify new promoters in a very short period of time using a combination of meta-expression analysis and promoter trap lines.