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Over the past 20 years, Arabidopsis has been the most important model in plant biology worldwide. Its genome sequence was determined with high reliability in 2000 by the Arabidopsis Genome Initiative. This sequence information has become the fundamental basis for all plant scientists when analyzing genomic information and relating it to plant functions. Indeed, most plant scientists would agree that the year 2000 was the key turning point that determined the direction of plant research for the subsequent decade. Following Arabidopsis, the next plant genome to be fully sequenced with high reliability was rice, accomplished by the International Rice Genome Sequencing Project in 2004 (International Rice Genome Sequencing Project 2005) (http://rgp.dna.affrc.go.jp/E/IRGSP/index.html). Rice is a model crop of particular importance for analyzing gene functions in breeding. Japanese plant scientists made a significant contributions to both these genome sequencing projects. The Kazusa DNA Research Institute was heavily involved in the Arabidopsis genome sequence project, and the National Institute for Agrobiological Sciences (NIAS) played a key role in the rice genome sequence project.
After the Arabidopsis genome sequence was determined in 2000, the Arabidopsis 2010 project was initiated. Its aim was to analyze all the coding genes, based on genomics and mutant resources through international cooperation. In the USA, the National Science Foundation has supported this major project for 10 years, and has been successful in promoting not only Arabidopsis research but also plant science and crop science in general (see http://www.arabidopsis.org/). Similar functional genomics projects were also set up in Europe, Japan and other countries after 2000. In Japan, RIKEN started an Arabidopsis functional genomics project at the Yokohama Institute with the goal of collecting full-length cDNAs and tagged mutants (http://rarge.psc.riken.jp/). The Kazusa DNA Research Institute has continued its genome sequencing work on the model legume Lotus japonicus and a number of other plants (http://www.kazusa.or.jp/e/resources/database.html). NIAS also contributed by preparing genomic and mutant resources and quantitative trait locus (QTL) technology (http://rgp.dna.affrc.go.jp/E/). To analyze gene functions, two major resources have been developed for reverse genetics: full-length cDNAs and T-DNA or Ds transposon-tagged mutant lines. Through the 2010 project, the various genomic and mutant resources have been deposited in and distributed from dedicated resource centers. The Arabidopsis Biological Resource Center (ABRC) (http://www.biosci.ohio-state.edu/~plantbio/Facilities/abrc/abrchome.htm) and NASC European Arabidopsis Stock Center (http://arabidopsis.info/) are the major Arabidopsis resource centers in the USA and Europe, respectively. In 2001, RIKEN opened the BioResource Center at the Tsukuba Institute, to collect and distribute the genomic and mutant resources developed in RIKEN and other Japanese institutes (http://www.brc.riken.go.jp/lab/epd/Eng/). The rice functional genomics project in NIAS also developed rice full-length cDNAs and mutant resources, which are available from its own resource center (http://www.rgrc.dna.affrc.go.jp/). In Japan, the National BioResource Project (NBRP) started to collect, reserve and distribute biological resources including plants (http://www.nbrp.jp/index.jsp). These biological resources have become increasingly important to understand the various functions and biodiversity of living organisms.
Arabidopsis transcriptome analyses using microarrays revealed the importance of transcriptional regulation in plant growth and environmental responses. Many transcriptome projects have collected thousands of microarray data sets to integrate gene expression profiles and transcriptional regulatory networks (http://www.arabidopsis.org/portals/expression/index.jsp). Together, these databases provide an information platform for analyzing gene expression profiles and the co-expression of genes and their interactions. Understanding transcriptional regulatory networks is fundamental to investigating the regulatory systems that control plant functions. For this purpose, transcription factors (TFs) have been collected for functional analysis through suppression in transgenic plants (Hiratsu et al. 2003, Iida et al. 2005).
Saturation mutagenesis involves the collection and systematic phenotyping of T-DNA and transposon-tagged gene knockout mutants to analyze gene function based on mutant phenotypes. These mutant collections are important for research into almost every plant function in Arabidopsis and are freely distributed by the resource centers. Now, systematic analyses of phenotypes, so-called phenome analyses, have begun using these mutants, based on a reverse genetics approach. Gene ontology is important for describing gene functions based on mutant phenotypes.
Since its inception, the technology of genome sequencing has developed, while its costs have fallen dramatically. As a result, many genome sequences of various plants and algae have been determined and used in the comparison of protein-coding genes, non-coding RNA genes and overall genome organizations. Next generation DNA sequencers are now available for the high throughput analysis of transcriptome analysis (Lister et al. 2009). Single nucleotide polymorphisms (SNPs) in Arabidopsis ecotypes can now be analyzed using these new DNA sequencers.
In tandem with the advance of genomics, other omics technologies such as metabolomics and proteomics have developed extensively, built upon new mass spectrometry methods. Integrated analyses of metabolomics and transcriptomics have succeeded in discovering novel metabolic networks and their transcriptional regulation systems (http://prime.psc.riken.jp/). Integrated analysis is important to describe complex biological systems underlying various plant functions. For these integrated analyses, databases of metabolomic, proteomic and transcriptomic information comprise an essential toolbox. For example, systematic analysis of plant hormones is an emergent field analyzing the hormone networks in various plant processes. Such systematic analyses of data from different omics are important for integrative biology. Together, bioinformatics and omics databases are key to the integration of genomics data for systems analyses of plant growth and environmental responses. Bioinformatics becomes more and more critical as the size of the databases expands, and will ultimately allow the evolution of biology from a descriptive science to a predictive science based on omics data mining.
Japanese researchers have contributed significantly to plant genomics and functional genomics, as well as basic research into a variety of plant functions in Arabidopsis, rice and other model plants. In recognition of this effort, PCP Editorial Board members have chosen to publish a special issue on omics and bioinformatics with articles contributed by Japanese researchers. This special issue introduces six cutting-edge topics or reviews of omics and bioinformatics in plant science, predominantly carried out in Japan.
Today, huge sets of transcriptome data are available, and scientists can exploit them in gene discovery. A powerful tool for narrowing down target genes is co-expression analysis (Aoki et al. 2007) using public databases such as ATTED-II (Obayashi et al. 2007). Furthermore, combining transcriptome data with metabolome data provides more robust evidence of linkage between genes and metabolisms (Hirai et al. 2005, Hirai et al. 2007, Yonekura-Sakakibara et al. 2008). In this issue, Sawada et al. (pp. 1181–1190) describe an application of this omics-based approach to identify genes for enzymes involved in the chain elongation machinery of methionine-derived glucosinolates in Arabidopsis. The authors performed a targeted-metabolome analysis with knockout mutants of genes, which were annotated as leucine biosynthesis genes, and found that two genes are involved in the chain elongation machinery of methionine. This is a good example illustrating the efficiency of the omics-based approach in the functional elucidation of genes involved in metabolism.
Proteins are responsible for conducting a wide variety of reactions in living organisms. The proteomic approach, which documents protein accumulation patterns at different developmental stages or in distinct cell populations, is obviously important for a deeper understanding of whole biological processes. Fujiwara et al. (pp. 1191–1200) performed a proteomic analysis of detergent-resistant membranes (DRMs) in rice suspension cells, and identified around 200 proteins. DRMs have recently been recognized as important regions of the plasma membrane involved in various signaling processes in animal, yeast and plant cells. The authors found that OsRac1, a molecular switch in rice innate immunity (Kawasaki et al. 1999, Ono et al. 2001, Lieberherr et al. 2005), and RACK1A, an effector of OsRac1 (Thao et al. 2007, Nakashima et al. 2008), shifted to the DRMs after chitin elicitor treatment. This is evidence suggesting that OsRac1-mediated innate immunity is associated with DRMs.
In the past decade, plant hormone research has progressed remarkably as a result of the identification of hormone biosynthesis pathways and signaling systems. How-ever, the main omics-based approach in plant hormone biology to date has been transcriptome analysis (Nemhauser et al. 2006, Goda et al. 2008), because plant hormones have different chemical properties, and because their concentrations are so low that it has been difficult to perform high-throughput analysis. To solve this problem, Kojima et al. (pp. 1201–1214) report the development of a highly sensitive and high-throughput method for the simultaneous analysis of the four major groups of phytohormones. They applied this technology to plant hormone profiling, drawing organ distribution maps of hormone species in rice. The usefulness of combining hormone profiling data with transcriptome data is illustrated by analyzing relationships between gene expression and hormone metabolism in gibberellin signaling mutants.
Needless to say, phenotype analysis of genetic mutations is an effective way to reveal gene function. To date, large numbers of ‘loss-of-function’ and ‘gain-of-function’ mutant lines have been generated, and some of their phenotypic characteristics have been captured in web-based databases (Weigel et al. 2000, Alonso et al. 2003, Kuromori et al. 2004, Ichikawa et al. 2006, Jeong et al. 2006, Miyao et al. 2007, Kondou et al. 2009). In this issue, Kuromori et al. (pp. 1215–1231) review the available mutant resources and databases of loss-of-function and gain-of-function mutants in Arabidopsis, rice and other crops. They also describe the concepts and principles of several representative mutant resources, including T-DNA and Ds insertion lines, activation-tagging lines and FOX lines. Their review helpfully includes information on types of mutations, numbers of mutant lines and website addresses. It also mentions the strengths and weaknesses of research using ‘loss-of-function’ and ‘gain-of-function’ mutant lines. We recommend this review not only for beginners but also for senior researchers who want to understand the current status of plant phenome analysis.
TFs regulate a wide range of gene expression at the transcriptional level. In Arabidopsis, it is predicted that ≥2,000 genes encode TFs (Iida et al. 2005, Riano-Pachon et al. 2007). In this issue, Mitsuda and Ohme-Takagi (pp. 1232–1248) neatly review Arabidopsis TFs and databases, and also describe strategies for functional analysis of plant TFs. Through the useful tables in this article, readers will easily be able to access information about representative databases holding Arabidopsis TFs and TF genes targeted by microRNA.
In addition to wet experiments, bioinformatics approaches often yield useful information to identify key genes. Makita et al. (pp. 1249–1259) introduce a new integrated database system termed PosMed-plus. This is an expanded version of PosMed, which was initially established to assist candidate selection for positional cloning in mice, humans and rats (Yoshida et al. 2009). In addition to explaining this new system which is powered by an intelligent data-retrieval engine, the authors show an example of in silico positional cloning after QTL analysis in rice. The editors strongly encourage readers to visit the website (http://omicspace.riken.jp/PosMed-plus/) to try the system for themselves.
We hope that this special issue helps PCP readers to understand the effectiveness of omics and bioinformatics approaches in systems analyses of plant functions, and that readers will try to use the technologies and databases in their own research. Moreover, this special issue is useful to understand the recent progress in omics and bioinformatics that has been mainly achieved in Japan.