Gene expression throughout the reproductive process in rice (Oryza sativa) beginning with primordia development through pollination/fertilization to zygote formation was analyzed. We analyzed 25 stages/organs of rice reproductive development including early microsporogenesis stages with 57,381 probe sets, and identified around 26,000 expressed probe sets in each stage. Fine dissection of 25 reproductive stages/organs combined with detailed microarray profiling revealed dramatic, coordinated and finely tuned changes in gene expression. A decrease in expressed genes in the pollen maturation process was observed in a similar way with Arabidopsis and maize. An almost equal number of ab initio predicted genes and cloned genes which appeared or disappeared coordinated with developmental stage progression. A large number of organ-/stage-specific genes were identified; notably 2,593 probe sets for developing anther, including 932 probe sets corresponding to ab initio predicted genes. Analysis of cell cycle-related genes revealed that several cyclin-dependent kinases (CDKs), cyclins and components of SCF E3 ubiquitin ligase complexes were expressed specifically in reproductive organs. Cell wall biosynthesis or degradation protein genes and transcription factor genes expressed specifically in reproductive stages were also newly identified. Rice genes homologous to reproduction-related genes in other plants showed expression profiles both consistent and inconsistent with their predicted functions. The rice reproductive expression atlas is likely to be the most extensive and most comprehensive data set available, indispensable for unraveling functions of many specific genes in plant reproductive processes that have not yet been thoroughly analyzed.
Similarity of gene expression profiles provides important clues for understanding the biological functions of genes, biological processes and metabolic pathways related to genes. A gene expression network (GEN) is an ideal choice to grasp such expression profile similarities among genes simultaneously. For GEN construction, the Pearson correlation coefficient (PCC) has been widely used as an index to evaluate the similarities of expression profiles for gene pairs. However, calculation of PCCs for all gene pairs requires large amounts of both time and computer resources. Based on correspondence analysis, we developed a new method for GEN construction, which takes minimal time even for large-scale expression data with general computational circumstances. Moreover, our method requires no prior parameters to remove sample redundancies in the data set. Using the new method, we constructed rice GENs from large-scale microarray data stored in a public database. We then collected and integrated various principal rice omics annotations in public and distinct databases. The integrated information contains annotations of genome, transcriptome and metabolic pathways. We thus developed the integrated database OryzaExpress for browsing GENs with an interactive and graphical viewer and principal omics annotations (http://riceball.lab.nig.ac.jp/oryzaexpress/). With integration of Arabidopsis GEN data from ATTED-II, OryzaExpress also allows us to compare GENs between rice and Arabidopsis. Thus, OryzaExpress is a comprehensive rice database that exploits powerful omics approaches from all perspectives in plant science and leads to systems biology.
Background: High-density short oligonucleotide microarrays are useful tools for studying biodiversity, because they can be used to investigate both nucleotide and expression polymorphisms. However, when different strains (or species) produce different signal intensities after mRNA hybridization, it is not easy to determine whether the signal intensities were affected by nucleotide or expression polymorphisms. To overcome this difficulty, nucleotide and expression polymorphisms are currently examined separately.
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