Metabolonote: A Wiki-Based Database for Managing Hierarchical Metadata of Metabolome Analyses

Ara, Takeshi; Enomoto, Mitsuo; Arita, Masanori; Ikeda, Chiaki; Kera, Kota; Yamada, Manabu; Nishi­oka, Takaaki; Ikeda, Tasuku; Nihei, Yoshito; Shibata, Daisuke; Kanaya, Shigehiko; Sakurai, Nozomu

doi:10.3389/fbioe.2015.00038

Cited by 36 publications

(31 citation statements)

References 44 publications

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“…The blank sample was analyzed in triplicate. The binary raw data from Xcalibur (.raw) and the experimental metadata for the samples are available on our website (http://webs2.kazusa.or.jp/data/okaramine) and on Metabolonote (http://metabolonote.kazusa.or.jp/SE194:/) (Ara et al, 2015), respectively.…”

Section: Untargeted Metabolome Analysismentioning

confidence: 99%

Metabolome Analysis Identified Okaramines in the Soybean Rhizosphere as a Legacy of Hairy Vetch

et al. 2020

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Inter-organismal communications below ground, such as plant-microbe interactions in the rhizosphere, affect plant growth. Metabolites are shown to play important roles in biological communication, but there still remain a large number of metabolites in soil to be uncovered. Metabolomics, a technique for the comprehensive analysis of metabolites in samples, may uncover the molecules that intermediate these interactions. We conducted a multivariate analysis using liquid chromatography (LC)-mass spectrometry (MS)-based untargeted metabolomics in several soil samples and also targeted metabolome analysis for the identification of the candidate compounds in soil. We identified okaramine A, B, and C in the rhizosphere soil of hairy vetch. Okaramines are indole alkaloids first identified in soybean pulp (okara) inoculated with Penicillium simplicissimum AK-40 and are insecticidal. Okaramine B was detected in the rhizosphere from an open field growing hairy vetch. Okaramine B was also detected in both bulk and rhizosphere soils of soybean grown following hairy vetch, but not detected in soils of soybean without hairy vetch growth. These results suggested that okaramines might be involved in indirect defense of plants against insects. To our knowledge, this is the first report of okaramines in the natural environment. Untargeted and targeted metabolomics would be useful to uncover the chemistry of the rhizosphere.

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Section: Untargeted Metabolome Analysismentioning

confidence: 99%

Metabolome Analysis Identified Okaramines in the Soybean Rhizosphere as a Legacy of Hairy Vetch

et al. 2020

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“…The metabolite candidates for the peaks were searched using the UC2 search function of MF Searcher tool (Sakurai et al, 2018) with a 5 ppm mass tolerance and the following compound databases: KEGG (Kanehisa et al, 2016), KNApSAcK (Afendi et al, 2012), HMDB (Wishart et al, 2013), LIPID MAPS (Fahy et al, 2009), and the flavonoid database in metabolomics.jp. The binary raw data from Xcalibur (.raw) and the experimental metadata for the samples are deposited at the Metabolonote website (http://metabolonote.kazusa.or.jp/SE195:/; http://webs2.kazusa.or.jp/data/nur/) (Ara et al, 2015).…”

Section: Metabolome Analysismentioning

confidence: 99%

Lipid remodeling regulator 1 (LRL1) is differently involved in the phosphorus‐depletion response from PSR1 in Chlamydomonas reinhardtii

et al. 2019

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Summary The elucidation of lipid metabolism in microalgae has attracted broad interest, as their storage lipid, triacylglycerol (TAG), can be readily converted into biofuel via transesterification. TAG accumulates in the form of oil droplets, especially when cells undergo nutrient deprivation, such as for nitrogen (N), phosphorus (P), or sulfur (S). TAG biosynthesis under N‐deprivation has been comprehensively studied in the model microalga Chlamydomonas reinhardtii, during which TAG accumulates dramatically. However, the resulting rapid breakdown of chlorophyll restricts overall oil yield productivity and causes cessation of cell growth. In contrast, P‐deprivation results in oil accumulation without disrupting chloroplast integrity. We used a reverse genetics approach based on co‐expression analysis to identify a transcription factor (TF) that is upregulated under P‐depleted conditions. Transcriptomic analysis revealed that the mutants showed repression of genes typically associated with lipid remodeling under P‐depleted conditions, such as sulfoquinovosyl diacylglycerol 2 (SQD2), diacylglycerol acyltransferase (DGTT1), and major lipid droplet protein (MLDP). As accumulation of sulfoquinovosyl diacylglycerol and TAG were suppressed in P‐depleted mutants, we designated the protein as lipid remodeling regulator 1 (LRL1). LRL1 mutants showed slower growth under P‐depletion. Moreover, cell size in the mutant was significantly reduced, and TAG and starch accumulation per cell were decreased. Transcriptomic analysis also suggested the repression of several genes typically upregulated in adaptation to P‐depletion that are associated with the cell cycle and P and lipid metabolism. Thus, our analysis of LRL1 provides insights into P‐allocation and lipid remodeling under P‐depleted conditions in C. reinhardtii. Open Research Badges This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The sequencing data were made publicly available under the BioProject Accession number PRJDB6733 and an accession number LC488724 at the DNA Data Bank of Japan (DDBJ). The data is available at https://trace.ddbj.nig.ac.jp/BPSearch/bioproject?acc=PRJDB6733; http://getentry.ddbj.nig.ac.jp/getentry/na/LC488724. The metabolome data were made publicly available and can be accessed at http://metabolonote.kazusa.or.jp/SE195:/; http://webs2.kazusa.or.jp/data/nur/.

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“…MetaDB [227] is a web application for metabolomics metadata management with interface to the MetaMS data processing tool, and Metabolonote [228] is a metadata database/management system.…”

Section: Other Programs Of Interestmentioning

confidence: 99%

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

et al. 2017

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The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

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Metabolonote: A Wiki-Based Database for Managing Hierarchical Metadata of Metabolome Analyses

Cited by 36 publications

References 44 publications

Metabolome Analysis Identified Okaramines in the Soybean Rhizosphere as a Legacy of Hairy Vetch

Metabolome Analysis Identified Okaramines in the Soybean Rhizosphere as a Legacy of Hairy Vetch

Lipid remodeling regulator 1 (LRL1) is differently involved in the phosphorus‐depletion response from PSR1 in Chlamydomonas reinhardtii

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

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