Taketo Okada

KNApSAcK Family Databases: Integrated Metabolite–Plant Species Databases for Multifaceted Plant Research

A database (DB) describing the relationships between species and their metabolites would be useful for metabolomics research, because it targets systematic analysis of enormous numbers of organic compounds with known or unknown structures in metabolomics. We constructed an extensive species-metabolite DB for plants, the KNApSAcK Core DB, which contains 101,500 species-metabolite relationships encompassing 20,741 species and 50,048 metabolites. We also developed a search engine within the KNApSAcK Core DB for use in metabolomics research, making it possible to search for metabolites based on an accurate mass, molecular formula, metabolite name or mass spectra in several ionization modes. We also have developed databases for retrieving metabolites related to plants used for a range of purposes. In our multifaceted plant usage DB, medicinal/edible plants are related to the geographic zones (GZs) where the plants are used, their biological activities, and formulae of Japanese and Indonesian traditional medicines (Kampo and Jamu, respectively). These data are connected to the species-metabolites relationship DB within the KNApSAcK Core DB, keyed via the species names. All databases can be accessed via the website http://kanaya.naist.jp/KNApSAcK_Family/. KNApSAcK WorldMap DB comprises 41,548 GZ-plant pair entries, including 222 GZs and 15,240 medicinal/edible plants. The KAMPO DB consists of 336 formulae encompassing 278 medicinal plants; the JAMU DB consists of 5,310 formulae encompassing 550 medicinal plants. The Biological Activity DB consists of 2,418 biological activities and 33,706 pairwise relationships between medicinal plants and their biological activities. Current statistics of the binary relationships between individual databases were characterized by the degree distribution analysis, leading to a prediction of at least 1,060,000 metabolites within all plants. In the future, the study of metabolomics will need to take this huge number of metabolites into consideration.

De novo sequencing and transcriptome analysis of the central nervous system of mollusc Lymnaea stagnalis by deep RNA sequencing.

The pond snail Lymnaea stagnalis is among several mollusc species that have been well investigated due to the simplicity of their nervous systems and large identifiable neurons. Nonetheless, despite the continued attention given to the physiological characteristics of its nervous system, the genetic information of the Lymnaea central nervous system (CNS) has not yet been fully explored. The absence of genetic information is a large disadvantage for transcriptome sequencing because it makes transcriptome assembly difficult. We here performed transcriptome sequencing for Lymnaea CNS using an Illumina Genome Analyzer IIx platform and obtained 81.9 M of 100 base pair (bp) single end reads. For de novo assembly, five programs were used: ABySS, Velvet, OASES, Trinity and Rnnotator. Based on a comparison of the assemblies, we chose the Rnnotator dataset for the following blast searches and gene ontology analyses. The present dataset, 116,355 contigs of Lymnaea transcriptome shotgun assembly (TSA), contained longer sequences and was much larger compared to the previously reported Lymnaea expression sequence tag (EST) established by classical Sanger sequencing. The TSA sequences were subjected to blast analyses against several protein databases and Aplysia EST data. The results demonstrated that about 20,000 sequences had significant similarity to the reported sequences using a cutoff value of 1e-6, and showed the lack of molluscan sequences in the public databases. The richness of the present TSA data allowed us to identify a large number of new transcripts in Lymnaea and molluscan species.

Metabolomics of Medicinal Plants: The Importance of Multivariate Analysis of Analytical Chemistry Data

Metabolomics, the comprehensive and global analysis of diverse metabolites produced in cells and organisms, has greatly expanded metabolite fingerprinting and profiling as well as the selection and identification of marker metabolites. The methodology typically employs multivariate analysis to statistically process the massive amount of analytical chemistry data resulting from high-throughput and simultaneous metabolite analysis. Although the technology of plant metabolomics has mainly developed with other post-genomics in systems biology and functional genomics, it is independently applied to the evaluation of the qualities of medicinal plants, based on the diversity of metabolite fingerprints resulting from multivariate analysis of non-targeted or widely targeted metabolite analysis. One advantage of applying metabolomics is that medicinal plants are evaluated based not only on the limited number of metabolites that are pharmacologically important chemicals, but also on the fingerprints of minor metabolites and bioactive chemicals. In particular, score plot and loading plot analyses e.g. principal component analysis (PCA), partial-least-squares discriminant analysis (PLS-DA), and discrimination map analysis such as batch-learning self-organizing map (BL-SOM) analysis, are often employed for the reduction of a metabolite fingerprint and the classification of analyzed samples. Based on recent studies, we now understand that metabolomics can be an effective approach for comprehensive evaluation of the qualities of medicinal plants. In this review, we describe practical cases in which metabolomic study was performed on medicinal plants, and discuss the utility of metabolomics for this research field, with focus on multivariate analysis.

Metabolome analysis of Ephedra plants with different contents of ephedrine alkaloids by using UPLC-Q-TOF-MS.

Metabolome analysis of four varieties of Ephedra plants, which contain different amounts of ephedrine alkaloids, was demonstrated in this study. The metabolites were comprehensively analyzed by using ultra performance liquid chromatography (UPLC) coupled with quadrupole time-of-flight mass spectrometry (Q-TOF-MS) and the ephedrine alkaloids were also profiled. Subsequently, multivariate analyses of principal component analysis (PCA) and batch-learning self-organizing mapping (BL-SOM) analysis were applied to the raw data of the total ion chromatogram (TIC). PCA was performed to visualize the fingerprints characteristic for each Ephedra variant and the independent metabolome clusters were formed. The metabolite fingerprints were also visualized by BL-SOM analysis and were displayed as a lattice of colored individual cells which was characteristic for each Ephedra variant. BL-SOM analysis was also used for identification of chemical marker peaks because the information assigned to a cell represented either increases or decreases in peak intensities. Using this analysis, ephedrine alkaloids were successfully selected from the TICs as chemical markers for each Ephedra variant and this result suggested that BL-SOM analysis was an effective method for the selection of marker metabolites. We report our study here as a practical case of metabolomic study on medicinal resources.

Comparative analysis of transcriptomes in aerial stems and roots of Ephedra sinica based on high-throughput mRNA sequencing

Ephedra plants are taxonomically classified as gymnosperms, and are medicinally important as the botanical origin of crude drugs and as bioresources that contain pharmacologically active chemicals. Here we show a comparative analysis of the transcriptomes of aerial stems and roots of Ephedra sinica based on high-throughput mRNA sequencing by RNA-Seq. De novo assembly of short cDNA sequence reads generated 23,358, 13,373, and 28,579 contigs longer than 200 bases from aerial stems, roots, or both aerial stems and roots, respectively. The presumed functions encoded by these contig sequences were annotated by BLAST (blastx). Subsequently, these contigs were classified based on gene ontology slims, Enzyme Commission numbers, and the InterPro database. Furthermore, comparative gene expression analysis was performed between aerial stems and roots. These transcriptome analyses revealed differences and similarities between the transcriptomes of aerial stems and roots in E. sinica. Deep transcriptome sequencing of Ephedra should open the door to molecular biological studies based on the entire transcriptome, tissue- or organ-specific transcriptomes, or targeted genes of interest.

Informatics framework of traditional Sino-Japanese medicine (Kampo) unveiled by factor analysis

Kampo, an empirically validated system of traditional Sino-Japanese medicine, aims to treat patients holistically. This is in contrast to modern medicine, which focuses in principle on treating the affected parts of the body of the patient. Kampo medicines formulated as combinations of crude drugs are prescribed based on a Kampo-specific diagnosis called Sho (in Japanese), defined as the holistic condition of each patient. Therefore, the medication system is very complex and is not well understood from a modern scientific perspective. Here, we show the informatics framework of Kampo medication by multivariate factor analysis of the elements constituting Kampo medication. First, the variation of Kampo formulas projected by principal component analysis (PCA) indicated that the combination patterns of crude drugs were highly correlated with Sho diagnoses of Deficiency and Excess. In an opposite way, partial least squares projection to latent structures (PLS) regression analysis could also predict Deficiency/Excess only from the composed crude drugs. Secondly, to chemically verify the correlation between Deficiency/Excess and crude drugs, we performed mass spectrometry (MS)-based metabolome analysis of Kampo prescriptions. PCA and PLS regression analysis of the metabolome data also suggested that Deficiency/Excess could be theoretically explained based on the variation in chemical fingerprints of Kampo medicines. Our results show that factor analysis of Kampo concepts and of the metabolomes of Kampo medicines enables interpretation of the complex system of Kampo. This study will theoretically form the basis for establishing traditionally and empirically based medications worldwide, leading to systematically personalized medicine.

Multivariate Analysis of Analytical Chemistry Data and Utility of the KNApSAcK Family Database to Understand Metabolic Diversity in Medicinal Plants

Due to the advances in computational sciences and analytical chemistry, metabolome analysis, which aims to elucidate metabolic diversity in organisms, has been demonstrated. The metabolomic approach has been frequently employed in medicinal plant studies because it can comprehensively and simultaneously analyze numerous metabolites with medicinal properties. To demonstrate metabolome analysis of medicinal plants, this chapter introduces: (1) comprehensive metabolite analysis based on analytical chemistry, (2) multivariate analysis of analytical chemistry data, and (3) a metabolite database search to identify signal processing in chemical analysis. In particular, the utility and role of the KNApSAcK Family database, which is a medicinal plant database connected with metabolites constructed by our group, are explained in detail. Additionally, this chapter describes the effectiveness and potential of computational systems biology in medicinal plant research.