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Pollen protects male sperm and allows flowering plants to adapt to diverse terrestrial environments, thereby leading to the rapid expansion of plants into new regions. The process of anther/pollen development is coordinately regulated by internal and external factors including hormones. Currently, the molecular mechanisms underlying gibberellin (GA)-mediated male reproductive development in plants remain unknown. We show here that rice DELLA/SLR1, which encodes the central negative regulator of GA signaling, is essential for rice anther development. The slr1-5 mutant exhibits premature programmed cell death of the tapetum, lacks Ubisch bodies, and has no exine and no mature pollen. SLR1 is mainly expressed in tapetal cells and tetrads, and is required for the appropriate expression of genes encoding key factors of pollen development, which are suggested to be OsMS188-targeted genes. OsMS188 is the main component in the essential genetic program of tapetum and pollen development. Further, we demonstrate that SLR1 interacts with OsMS188 to cooperatively activate the expression of the sporopollenin biosynthesis and transport-related genes CYP703A3, DPW, ABCG15 and PKS1 for rapid formation of pollen walls. Overall, the results of this study suggest that the GA hormonal signal is integrated into the anther genetic program and regulates rice anther development through the GA-DELLA-OsMS188 module.

Chloroplast development involves the coordinated expression of both plastids- and nuclear-encoded genes in higher plants. However, the underlying mechanism still remains largely unknown. In this study, we isolated and characterized an Arabidopsis mutant with an albino lethality phenotype named RNA processing 8 (rp8). Genetic complementation analysis demonstrated that the gene AT4G37920 (RP8) was responsible for the mutated phenotype. The RP8 gene was strongly expressed in photosynthetic tissues at both transcription and translation protein levels. The RP8 protein is localized in the chloroplast and associated with the thylakoid. Disruption of the RP8 gene led to a defect in the accumulation of the rpoA mature transcript, which reduced the level of the RpoA protein, and affected the transcription of PEP-dependent genes. The abundance of the chloroplast rRNA, including 23S, 16S, 4.5S, and 5S rRNA, were reduced in the rp8 mutant, respectively, and the amounts of chloroplast ribosome proteins, such as, PRPS1(uS1c), PRPS5(uS5c), PRPL2 (uL2c), and PRPL4 (uL4c), were substantially decreased in the rp8 mutant, which indicated that knockout of RP8 seriously affected chloroplast translational machinery. Accordingly, the accumulation of photosynthetic proteins was seriously reduced. Taken together, these results indicate that the RP8 protein plays an important regulatory role in the rpoA transcript processing, which is required for the expression of chloroplast genes and chloroplast development in Arabidopsis.

Identification of cis-regulatory elements in Arabidopsis is a key step to understanding its transcriptional regulation scheme. In this study, the Arabidopsis gene coexpression network was constructed using the ATTED-II data, and thereafter a subgraph-induced approach and clique-finding algorithm were used to extract gene coexpression groups from the gene coexpression network. A total of 23 large coexpression gene groups were obtained, with each consisting of more than 100 highly correlated genes. Four classical tools were used to predict motifs in the promoter regions of coexpressed genes. Consequently, we detected a large number of candidate biologically relevant regulatory elements, and many of them are consistent with known cis-regulatory elements from AGRIS and AthaMap. Experiments on coexpressed groups, including E2Fa target genes, showed that our method had a high probability of returning the real binding motif. Our study provides the basis for future cis-regulatory module analysis and creates a starting point to unravel regulatory networks of Arabidopsis thaliana.

Identification of regulatory relationships between transcription factors (TFs) and their targets is a central problem in post-genomic biology. In this paper, we apply an approach based on the support vector machine (SVM) and gene-expression data to predict the regulatory interactions in Arabidopsis. A set of 125 experimentally validated TF-target interactions and 750 negative regulatory gene pairs are collected as the training data. Their expression profiles data at 79 experimental conditions are fed to the SVM to perform the prediction. Through the jackknife cross-validation test, we find that the overall prediction accuracy of our approach achieves 88.68%. Our approach could help to widen the understanding of Arabidopsis gene regulatory scheme and may offer a cost-effective alternative to construct the gene regulatory network.