Global expression differences and tissue specific expression differences in rice evolution result in two contrasting types of differentially expressed genes.

BACKGROUND: Since the development of transcriptome analysis systems, many expression evolution studies characterized evolutionary forces acting on gene expression, without explicit discrimination between global expression differences and tissue specific expression differences. However, different types of gene expression alteration should have different effects on an organism, the evolutionary forces that act on them might be different, and different types of genes might show different types of differential expression between species. To confirm this, we studied differentially expressed (DE) genes among closely related groups that have extensive gene expression atlases, and clarified characteristics of different types of DE genes including the identification of regulating loci for differential expression using expression quantitative loci (eQTL) analysis data. RESULTS: We detected differentially expressed (DE) genes between rice subspecies in five homologous tissues that were verified using japonica and indica transcriptome atlases in public databases. Using the transcriptome atlases, we classified DE genes into two types, global DE genes and changed-tissues DE genes. Global type DE genes were not expressed in any tissues in the atlas of one subspecies, however changed-tissues type DE genes were expressed in both subspecies with different tissue specificity. For the five tissues in the two japonica-indica combinations, 4.6 ± 0.8 and 5.9 ± 1.5 % of highly expressed genes were global and changed-tissues DE genes, respectively. Changed-tissues DE genes varied in number between tissues, increasing linearly with the abundance of tissue specifically expressed genes in the tissue. Molecular evolution of global DE genes was rapid, unlike that of changed-tissues DE genes. Based on gene ontology, global and changed-tissues DE genes were different, having no common GO terms. Expression differences of most global DE genes were regulated by cis-eQTLs. Expression evolution of changed-tissues DE genes was rapid in tissue specifically expressed genes and those rapidly evolved changed-tissues DE genes were regulated not by cis-eQTLs, but by complicated trans-eQTLs. CONCLUSIONS: Global DE genes and changed-tissues DE genes had contrasting characteristics. The two contrasting types of DE genes provide possible explanations for the previous controversial conclusions about the relationships between molecular evolution and expression evolution of genes in different species, and the relationship between expression breadth and expression conservation in evolution.

OryzaExpress: an integrated database of gene expression networks and omics annotations in rice.

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.

Rice expression atlas in reproductive development.

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.

A simple optimization can improve the performance of single feature polymorphism detection by Affymetrix expression arrays.

BACKGROUND: High-density oligonucleotide arrays are effective tools for genotyping numerous loci simultaneously. In small genome species (genome size: < approximately 300 Mb), whole-genome DNA hybridization to expression arrays has been used for various applications. In large genome species, transcript hybridization to expression arrays has been used for genotyping. Although rice is a fully sequenced model plant of medium genome size (approximately 400 Mb), there are a few examples of the use of rice oligonucleotide array as a genotyping tool. RESULTS: We compared the single feature polymorphism (SFP) detection performance of whole-genome and transcript hybridizations using the Affymetrix GeneChip Rice Genome Array, using the rice cultivars with full genome sequence, japonica cultivar Nipponbare and indica cultivar 93-11. Both genomes were surveyed for all probe target sequences. Only completely matched 25-mer single copy probes of the Nipponbare genome were extracted, and SFPs between them and 93-11 sequences were predicted. We investigated optimum conditions for SFP detection in both whole genome and transcript hybridization using differences between perfect match and mismatch probe intensities of non-polymorphic targets, assuming that these differences are representative of those between mismatch and perfect targets. Several statistical methods of SFP detection by whole-genome hybridization were compared under the optimized conditions. Causes of false positives and negatives in SFP detection in both types of hybridization were investigated. CONCLUSIONS: The optimizations allowed a more than 20% increase in true SFP detection in whole-genome hybridization and a large improvement of SFP detection performance in transcript hybridization. Significance analysis of the microarray for log-transformed raw intensities of PM probes gave the best performance in whole genome hybridization, and 22,936 true SFPs were detected with 23.58% false positives by whole genome hybridization. For transcript hybridization, stable SFP detection was achieved for highly expressed genes, and about 3,500 SFPs were detected at a high sensitivity (> 50%) in both shoot and young panicle transcripts. High SFP detection performances of both genome and transcript hybridizations indicated that microarrays of a complex genome (e.g., of Oryza sativa) can be effectively utilized for whole genome genotyping to conduct mutant mapping and analysis of quantitative traits such as gene expression levels.

SNEP: Simultaneous detection of nucleotide and expression polymorphisms using Affymetrix GeneChip.

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. RESULTS: We have developed SNEP, a new method that allows simultaneous detection of both nucleotide and expression polymorphisms. SNEP involves a robust statistical procedure based on the idea that a nucleotide polymorphism observed at the probe level can be regarded as an outlier, because the nucleotide polymorphism can reduce the hybridization signal intensity. To investigate the performance of SNEP, we used three species: barley, rice and mice. In addition to the publicly available barley data, we obtained new rice and mouse data from the strains with available genome sequences. The sensitivity and false positive rate of nucleotide polymorphism detection were estimated based on the sequence information. The robustness of expression polymorphism detection against nucleotide polymorphisms was also investigated. CONCLUSION: SNEP performed well regardless of the genome size and showed a better performance for nucleotide polymorphism detection, when compared with other previously proposed methods. The R-software 'SNEP' is available at http://www.ism.ac.jp/~fujisawa/SNEP/.

Identifying novel substrates for mouse Cdk5 kinase using the yeast Saccharomyces cerevisiae.

Among the mammalian Cdk family members, Cdk5, activated by the binding of p35, plays an important role in the control of neurogenesis, and its deregulation is thought to be one of the causes of neurodegenerative diseases. Overproduction of Cdk5 and p35 in yeast cells causes growth arrest, probably because of hyperphosphorylation of yeast proteins. We screened mouse brain cDNA that could recover the growth of yeast cells overproducing Cdk5 and p35, hoping that such cDNA encodes a substrate or inhibitor of Cdk5. Mouse brain cDNA library was introduced into a yeast strain in which Cdk5, p35 and mouse cDNA were over-expressed under the control of the GAL promoter, and cDNA plasmids were isolated from the transformants that recovered growth on galactose medium. The analysis of those plasmids revealed that they harbored cDNA that encodes neuronal proteins including SCLIP and CRMP-1, and those with unknown function. We found that Cdk5 could phosphorylate SCLIP and CRMP-1 in vitro and the two proteins in cultured cells showed a mobility shift depending on Cdk5 activity and the presence of specific Ser or Thr residues, indicating that SCLIP and CRMP-1 are likely substrates for Cdk5 in vitro and in cultured cells. Further screening with these systems will enable us to identify more novel substrates and regulators of Cdk5/p35, which will lead to the exploration of Cdk5 function in diverse cellular systems.