The Natural Variance of the Arabidopsis Floral Secondary Metabolome

Application of mass spectrometry-based metabolomics enables the detection of genotype-related natural variance in metabolism. Differences in secondary metabolite composition of flowers of 64 Arabidopsis thaliana (Arabidopsis) natural accessions, representing a considerable portion of the natural variation in this species are presented. The raw metabolomic data of the accessions and reference extracts derived from flavonoid knockout mutants have been deposited in the MetaboLights database. Additionally, summary tables of floral secondary metabolite data are presented in this article to enable efficient re-use of the dataset either in metabolomics cross-study comparisons or correlation-based integrative analysis of other metabolomic and phenotypic features such as transcripts, proteins and growth and flowering related phenotypes

Natural variation in flavonol accumulation in Arabidopsis is determined by the flavonol glucosyltransferase BGLU6.

Ishihara H, Tohge T, Viehöver P, Fernie AR, Weisshaar B, Stracke R. Natural variation in flavonol accumulation in Arabidopsis is determined by the flavonol glucosyltransferase BGLU6. Journal of Experimental Botany. 2016;67(5):1505-1517

From Models to Crop Species: Caveats and Solutions for Translational Metabolomics

Although plant metabolomics is largely carried out on Arabidopsis it is essentially genome-independent, and thus potentially applicable to a wide range of species. However, transfer between species, or even between different tissues of the same species, is not facile. This is because the reliability of protocols for harvesting, handling and analysis depends on the biological features and chemical composition of the plant tissue. In parallel with the diversification of model species it is important to establish good handling and analytic practice, in order to augment computational comparisons between tissues and species. Liquid chromatography–mass spectrometry (LC–MS)-based metabolomics is one of the powerful approaches for metabolite profiling. By using a combination of different extraction methods, separation columns, and ion detection, a very wide range of metabolites can be analyzed. However, its application requires careful attention to exclude potential pitfalls, including artifactual changes in metabolite levels during sample preparation under variations of light or temperature and analytic errors due to ion suppression. Here we provide case studies with two different LC–MS-based metabolomics platforms and four species (Arabidopsis thaliana, Chlamydomonas reinhardtii, Solanum lycopersicum, and Oryza sativa) that illustrate how such dangers can be detected and circumvented

Conserved changes in dynamics of metabolic processes during fruit development and ripening across species

Computational analyses of molecular phenotypes traditionally aim at identifying biochemical components that exhibit differential expression under various scenarios (e.g. environmental and internal perturbations) in a single species. High-throughput metabolomics technologies allow the quantification of (relative) metabolite levels across developmental stages in different tissues, organs, and species. Novel methods for analyzing the resulting multiple data tables could reveal preserved dynamics of metabolic processes across species. The problem we address in this study is 2-fold. (1) We derive a single data table, referred to as a compromise, which captures information common to the investigated set of multiple tables containing data on different fruit development and ripening stages in three climacteric (i.e. peach [Prunus persica] and two tomato [Solanum lycopersicum] cultivars, Ailsa Craig and M82) and two nonclimacteric (i.e. strawberry [Fragaria × ananassa] and pepper [Capsicum chilense]) fruits; in addition, we demonstrate the power of the method to discern similarities and differences between multiple tables by analyzing publicly available metabolomics data from three tomato ripening mutants together with two tomato cultivars. (2) We identify the conserved dynamics of metabolic processes, reflected in the data profiles of the corresponding metabolites that contribute most to the determined compromise. Our analysis is based on an extension to principal component analysis, called STATIS, in combination with pathway overenrichment analysis. Based on publicly available metabolic profiles for the investigated species, we demonstrate that STATIS can be used to identify the metabolic processes whose behavior is similarly affected during fruit development and ripening. These findings ultimately provide insights into the pathways that are essential during fruit development and ripening across species.Fil: Klie, Sebastian. Max Planck Institute of Molecular Plant Physiology; AlemaniaFil: Osorio, Sonia. Consejo Superior de Investigaciones Cientificas. Instituto de Hortofruticultura Subtropical y Mediterránea; EspañaFil: Tohge, Takayuki. Max Planck Institute of Molecular Plant Physiology; AlemaniaFil: Drincovich, Maria Fabiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (i); ArgentinaFil: Fait, Aaron. Ben-Gurion University of the Negrev; IsraelFil: Giovannoni, Federico. Cornell University; Estados UnidosFil: Fernie, Alisdair R.. Max Planck Institute of Molecular Plant Physiology; AlemaniaFil: Nikoloski, Zoran. Max Planck Institute of Molecular Plant Physiology; Alemani

Unbiased characterization of genotype-dependent metabolic regulations by metabolomic approach in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Metabolites are not only the catalytic products of enzymatic reactions but also the active regulators or the ultimate phenotype of metabolic homeostasis in highly complex cellular processes. The modes of regulation at the metabolome level can be revealed by metabolic networks. We investigated the metabolic network between wild-type and 2 mutant (<it>methionine-over accumulation 1 </it>[<it>mto1</it>] and <it>transparent testa4 </it>[<it>tt4</it>]) plants regarding the alteration of metabolite accumulation in <it>Arabidopsis thaliana</it>.</p> <p>Results</p> <p>In the GC-TOF/MS analysis, we acquired quantitative information regarding over 170 metabolites, which has been analyzed by a novel score (ZMC, z-score of metabolite correlation) describing a characteristic metabolite in terms of correlation. Although the 2 mutants revealed no apparent morphological abnormalities, the overall correlation values in <it>mto1 </it>were much lower than those of the wild-type and <it>tt4 </it>plants, indicating the loss of overall network stability due to the uncontrolled accumulation of methionine. In the <it>tt4 </it>mutant, a new correlation between malate and sinapate was observed although the levels of malate, sinapate, and sinapoylmalate remain unchanged, suggesting an adaptive reconfiguration of the network. Gene-expression correlations presumably responsible for these metabolic networks were determined using the metabolite correlations as clues.</p> <p>Conclusion</p> <p>Two Arabidopsis mutants, <it>mto1 </it>and <it>tt4</it>, exhibited the following changes in entire metabolome networks: the overall loss of metabolic stability (<it>mto1</it>) or the generation of a metabolic network of a backup pathway for the lost physiological functions (<it>tt4</it>). The expansion of metabolite correlation to gene-expression correlation provides detailed insights into the systemic understanding of the plant cellular process regarding metabolome and transcriptome.</p

Metabolome and Lipidome Profiles of Populus × canescens Twig Tissues During Annual Growth Show Phospholipid-Linked Storage and Mobilization of C, N, and S

The temperate climax tree species Fagus sylvatica and the floodplain tree species Populus × canescens possess contrasting phosphorus (P) nutrition strategies. While F. sylvatica has been documented to display P storage and mobilization (Netzer et al., 2017), this was not observed for Populus × canescens (Netzer et al., 2018b). Nevertheless, changes in the abundance of organic bound P in gray poplar trees indicated adaptation of the P nutrition to different needs during annual growth. The present study aimed at characterizing seasonal changes in metabolite and lipid abundances in gray poplar and uncovering differences in metabolite requirement due to specific needs depending on the season. Seasonal variations in the abundance of (i) sugar-Ps and phospholipids, (ii) amino acids, (iii) sulfur compounds, and (iv) carbon metabolites were expected. It was hypothesized that seasonal changes in metabolite levels relate to N, S, and C storage and mobilization. Changes in organic metabolites binding Pi (Porg) are supposed to support these processes. Variation in triacylglycerols, in sugar-phosphates, in metabolites of the TCA cycle and in the amino acid abundance of poplar twig buds, leaves, bark, and wood were found to be linked to changes in metabolite abundances as well as to C, N, and S storage and mobilization processes. The observed changes support the view of a lack of any P storage in poplar. Yet, during dormancy, contents of phospholipids in twig bark and wood were highest probably due to frost-hardening and to its function in extra-plastidic membranes such as amyloplasts, oleosomes, and protein bodies. Consistent with this assumption, in spring sugar-Ps increased when phospholipids declined and poplar plants entering the vegetative growth period and, hence, metabolic activity increases. These results indicate that poplar trees adopt a policy of P nutrition without P storage and mobilization that is different from their N- and S-nutrition strategies

Glutaredoxin GRXS17 associates with the cytosolic iron-sulfur cluster assembly pathway

Cytosolic monothiol glutaredoxins (GRXs) are required in iron-sulfur (Fe-S) cluster delivery and iron sensing in yeast and mammals. In plants, it is unclear whether they have similar functions. Arabidopsis (Arabidopsis thaliana) has a sole class II cytosolic monothiol GRX encoded by GRXS17. Here, we used tandem affinity purification to establish that Arabidopsis GRXS17 associates with most known cytosolic Fe-S assembly (CIA) components. Similar to mutant plants with defective CIA components, grxs17 loss-of-function mutants showed some degree of hypersensitivity to DNA damage and elevated expression of DNA damage marker genes. We also found that several putative Fe-S client proteins directly bind to GRXS17, such as XANTHINE DEHYDROGENASE1 (XDH1), involved in the purine salvage pathway, and CYTOSOLIC THIOURIDYLASE SUBUNIT1 and CYTOSOLIC THIOURIDYLASE SUBUNIT2, both essential for the 2-thiolation step of 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U) modification of tRNAs. Correspondingly, profiling of the grxs17-1 mutant pointed to a perturbed flux through the purine degradation pathway and revealed that it phenocopied mutants in the elongator subunit ELO3, essential for the mcm5 tRNA modification step, although we did not find XDH1 activity or tRNA thiolation to be markedly reduced in the grxs17-1 mutant. Taken together, our data suggest that plant cytosolic monothiol GRXs associate with the CIA complex, as in other eukaryotes, and contribute to, but are not essential for, the correct functioning of client Fe-S proteins in unchallenged conditions

Mapping the Arabidopsis Metabolic Landscape by Untargeted Metabolomics at Different Environmental Conditions

Metabolic genome-wide association studies (mGWAS), whereupon metabolite levels are regarded as traits, can help unravel the genetic basis of metabolic networks. A total of 309 Arabidopsis accessions were grown under two independent environmental conditions (control and stress) and subjected to untargeted LC-MS-based metabolomic profiling; levels of the obtained hydrophilic metabolites were used in GWAS. Our two-condition-based GWAS for more than 3000 semi-polar metabolites resulted in the detection of 123 highly resolved metabolite quantitative trait loci (p ≤ 1.0E-08), 24.39% of which were environment-specific. Interestingly, differently from natural variation in Arabidopsis primary metabolites, which tends to be controlled by a large number of small-effect loci, we found several major large-effect loci alongside a vast number of small-effect loci controlling variation of secondary metabolites. The two-condition-based GWAS was followed by integration with network-derived metabolite-transcript correlations using a time-course stress experiment. Through this integrative approach, we selected 70 key candidate associations between structural genes and metabolites, and experimentally validated eight novel associations, two of them showing differential genetic regulation in the two environments studied. We demonstrate the power of combining large-scale untargeted metabolomics-based GWAS with time-course-derived networks both performed under different abiotic environments for identifying metabolite-gene associations, providing novel global insights into the metabolic landscape of Arabidopsis. By combining large-scale untargeted metabolomics-based GWAS and network analysis with environmental stress-driven perturbations of metabolic homeostasis, this system-wide study provides new global insights into the metabolic landscape of Arabidopsis, using a strategy that could readily be extended to other plant species.</p