l-2-Aminopimelic acid acts as an auxin mimic to induce lateral root formation across diverse plant species.

The identification of chemicals that modulate plant development and adaptive responses to stresses has attracted increasing attention for agricultural applications. Recent basic studies have identified functional amino acids that are essential for plant organogenesis, indicating that amino acids can regulate plant growth. In this study, we newly identified 2-aminopimelic acid (2APA), a nonproteinogenic amino acid, as a novel bioactive compound involved in root morphogenesis. This biological effect was confirmed in several plant species. Our phenotypic analysis revealed that the bioactive 2APA is an l-form stereoisomer. Overall, our study identified a promising root growth regulator and provided insight into the intricate metabolism related to root morphology.

Roles of type II H+-PPases and PPsPase1/PECP2 in early developmental stages and PPi homeostasis of Arabidopsis thaliana.

The regulation of intracellular pyrophosphate (PPi) level is crucial for proper morphogenesis across all taxonomic kingdoms. PPi is released as a byproduct from ~200 metabolic reactions, then hydrolyzed by either membrane-bound (H+-PPase) or soluble pyrophosphatases (PPases). In Arabidopsis, the loss of the vacuolar H+-PPase/FUGU5, a key enzyme in PPi homeostasis, results in delayed growth and a number of developmental defects, pointing to the importance of PPi homeostasis in plant morphogenesis. The Arabidopsis genome encodes several PPases in addition to FUGU5, such as PPsPase1/PECP2, VHP2;1 and VHP2;2, although their significance regarding PPi homeostasis remains elusive. Here, to assess their contribution, phenotypic analyses of cotyledon aspect ratio, palisade tissue cellular phenotypes, adaxial side pavement cell complexity, stomatal distribution, and etiolated seedling length were performed, provided that they were altered due to excess PPi in a fugu5 mutant background. Overall, our analyses revealed that the above five traits were unaffected in ppspase1/pecp2, vhp2;1 and vhp2;2 loss-of-function mutants, as well as in fugu5 mutant lines constitutively overexpressing PPsPase1/PECP2. Furthermore, metabolomics revealed that ppspase1/pecp2, vhp2;1 and vhp2;2 etiolated seedlings exhibited metabolic profiles comparable to the wild type. Together, these results indicate that the contribution of PPsPase1/PECP2, VHP2;1 and VHP2;2 to PPi levels is negligible in comparison to FUGU5 in the early stages of seedling development.

Skotomorphogenesis exploits threonine to promote hypocotyl elongation.

Mobilisation of seed storage reserves is important for seedling establishment in Arabidopsis. In this process, sucrose is synthesised from triacylglycerol via core metabolic processes. Mutants with defects in triacylglycerol-to-sucrose conversion display short etiolated seedlings. We found that whereas sucrose content in the indole-3-butyric acid response 10 (ibr10) mutant was significantly reduced, hypocotyl elongation in the dark was unaffected, questioning the role of IBR10 in this process. To dissect the metabolic complexity behind cell elongation, a quantitative-based phenotypic analysis combined with a multi-platform metabolomics approach was applied. We revealed that triacylglycerol and diacylglycerol breakdown were disrupted in ibr10, resulting in low sugar content and poor photosynthetic ability. Importantly, batch-learning self-organised map clustering revealed that threonine level was correlated with hypocotyl length. Consistently, exogenous threonine supply stimulated hypocotyl elongation, indicating that sucrose levels are not always correlated with etiolated seedling length, suggesting the contribution of amino acids in this process.

Atypical Myrosinase as a Mediator of Glucosinolate Functions in Plants.

Glucosinolates (GLSs) are a well-known class of specialized plant metabolites, distributed mostly in the order Brassicales. A vast research field in basic and applied sciences has grown up around GLSs owing to their presence in important agricultural crops and the model plant Arabidopsis thaliana, and their broad range of bioactivities beneficial to human health. The major purpose of GLSs in plants has been considered their function as a chemical defense against predators. GLSs are physically separated from a specialized class of beta-thioglucosidases called myrosinases, at the tissue level or at the single-cell level. They are brought together as a consequence of tissue damage, primarily triggered by herbivores, and their interaction results in the release of toxic volatile chemicals including isothiocyanates. In addition, recent studies have suggested that plants may adopt other strategies independent of tissue disruption for initiating GLS breakdown to cope with certain biotic/abiotic stresses. This hypothesis has been further supported by the discovery of an atypical class of GLS-hydrolyzing enzymes possessing features that are distinct from those of the classical myrosinases. Nevertheless, there is only little information on the physiological importance of atypical myrosinases. In this review, we focus on the broad diversity of the beta-glucosidase subclasses containing known atypical myrosinases in A. thaliana to discuss the hypothesis that numerous members of these subclasses can hydrolyze GLSs to regulate their diverse functions in plants. Also, the increasingly broadening functional repertoires of known atypical/classical myrosinases are described with reference to recent findings. Assessment of independent insights gained from A. thaliana with respect to (1) the phenotype of mutants lacking genes in the GLS metabolic/breakdown pathways, (2) fluctuation in GLS contents/metabolism under specific conditions, and (3) the response of plants to exogenous GLSs or their hydrolytic products, will enable us to reconsider the physiological importance of GLS breakdown in particular situations, which is likely to be regulated by specific beta-glucosidases.

Genetic Variation for Seed Metabolite Levels in Brachypodium distachyon.

Metabolite composition and concentrations in seed grains are important traits of cereals. To identify the variation in the seed metabolotypes of a model grass, namely Brachypodium distachyon, we applied a widely targeted metabolome analysis to forty inbred lines of B. distachyon and examined the accumulation patterns of 183 compounds in the seeds. By comparing the metabolotypes with the population structure of these lines, we found signature metabolites that represent different accumulation patterns for each of the three B. distachyon subpopulations. Moreover, we found that thirty-seven metabolites exhibited significant differences in their accumulation between the lines Bd21 and Bd3-1. Using a recombinant inbred line (RIL) population from a cross between Bd3-1 and Bd21, we identified the quantitative trait loci (QTLs) linked with this variation in the accumulation of thirteen metabolites. Our metabolite QTL analysis illustrated that different genetic factors may presumably regulate the accumulation of 4-pyridoxate and pyridoxamine in vitamin B6 metabolism. Moreover, we found two QTLs on chromosomes 1 and 4 that affect the accumulation of an anthocyanin, chrysanthemin. These QTLs genetically interacted to regulate the accumulation of this compound. This study demonstrates the potential for metabolite QTL mapping in B. distachyon and provides new insights into the genetic dissection of metabolomic traits in temperate grasses.

Identification and Biochemical Characterization of the Serine Biosynthetic Enzyme 3-Phosphoglycerate Dehydrogenase in Marchantia polymorpha.

L-serine is an important molecule in all living organisms, and thus its biosynthesis is considered to be regulated according to demand. 3-Phosphoglycerate dehydrogenase (PGDH), the first committed enzyme of the phosphorylated pathway of L-serine biosynthesis, is regulated by negative feedback from L-serine in bacteria. In the case of the vascular plant Arabidopsis thaliana, two PGDH isozymes out of three are inhibited by L-serine and activated by L-alanine, L-valine, L-methionine, L-homoserine, and L-homocysteine, suggesting a more complicated regulatory mechanism of L-serine biosynthesis in A. thaliana than in bacteria. However, it remains to be clarified whether the activation mechanism of PGDH by amino acids is conserved in land plants. In this study, we identified the sole isozyme of PGDH in the liverwort Marchantia polymorpha (MpPGDH) and elucidated its biochemical characteristics. MpPGDH cDNA encodes a 65.6 kDa protein that contains a putative transit peptide for chloroplast localization. MpPGDH shares 75-80% identity with A. thaliana isozymes and forms a homotetramer in vitro. Recombinant MpPGDH exhibited an optimal pH of 9.0, apparent Michaelis constants of 0.49 ± 0.04 and 0.096 ± 0.010 mM for 3-PGA and NAD+, respectively, and apparent maximum velocity of 5.65 ± 0.10 µmol⋅min-1⋅mg-1, similar to those of A. thaliana isozymes. Phosphate ions were found to stabilize MpPGDH, suggesting that phosphate ions are also a crucial factor in the regulation of serine biosynthesis via the phosphorylated pathway in Marchantia polymorpha. MpPGDH was inhibited by L-serine in a cooperative manner and was activated by L-alanine, L-valine, L-methionine, L-homoserine, and L-homocysteine to a lesser extent than it is in A. thaliana. The results suggest that an ancestral PGDH of land plants was inhibited byL-serine and slightly activated by five other amino acids.

Autopolyploidization, geographic origin, and metabolome evolution in Arabidopsis thaliana.

PREMISE OF THE STUDY: Autopolyploidy, or whole-genome duplication, is a recurrent phenomenon in plant evolution. Its existence can be inferred from the presence of massive levels of genetic redundancy revealed by comparative plant phylogenomics. Whole-genome duplication is theoretically associated with evolutionary novelties such as the development of new metabolic reactions and therefore contributes to the evolution of new plant metabolic profiles. However, very little is yet known about the impact of autopolyploidy on the metabolism of recently formed autopolyploids. This study provides a better understanding of the relevance of this evolutionary process. METHODS: In this study, we compared the metabolic profiles of wild diploids, wild autotetraploids, and artificial autotetraploids of Arabidopsis thaliana using targeted ultra-high performance liquid chromatography-triple quadrupole- mass spectrometry (UPLC-QqQ-MS) metabolomics. KEY RESULTS: We found that wild and artificial A. thaliana autotetraploids display different metabolic profiles. Furthermore, wild autotetraploids display unique metabolic profiles associated with their geographic origin. CONCLUSIONS: Autopolyploidy might help plants adapt to challenging environmental conditions by allowing the evolution of novel metabolic profiles not present in the parental diploids. We elaborate on the causes and consequences leading to these distinct profiles.

Investigation of kinetic-order sensitivities in metabolic reaction networks.

Kinetic-order sensitivity (the ratio of relative change in a dependent variable to the relative change in a kinetic order in a power-law-type differential equation) has recently become an important indicator in metabolic pathway analysis using mathematical models with parameter values determined from time-series data on cellular metabolite concentrations. Here, we discuss a potential problem in calculating kinetic-order sensitivities. When the steady-state metabolite concentration is less than unity, a slight increase in the kinetic order changes the metabolite concentration in the incorrect direction, yielding a kinetic-order sensitivity value with an incorrect sign. This is caused by a property of the power-law function (y=Xn): when X is less than unity, y decreases for a larger positive n or for a smaller absolute value of negative n. We propose two solutions. The first is to directly calculate the kinetic-order sensitivities and then reverse the sign of the relevant value if a steady-state metabolite concentration less than unity is involved. The second involves calculation of the kinetic-order sensitivities after setting all metabolite concentrations to values greater than unity (e.g., by changing the units from mM to µM). The latter method changes the absolute values of the kinetic-order sensitivities according to the magnitude of a multiplication factor, because kinetic-order sensitivities do not have unique values. Nevertheless, since the normalized absolute values exhibit an almost identical distribution, it should not be difficult to identify which kinetic order has greater effect, although kinetic order rankings may change slightly under different calculation conditions.

Genetic manipulation of a metabolic enzyme and a transcriptional regulator increasing succinate excretion from unicellular cyanobacterium.

Succinate is a building block compound that the U.S. Department of Energy (DOE) has declared as important in biorefineries, and it is widely used as a commodity chemical. Here, we identified the two genes increasing succinate production of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Succinate was excreted under dark, anaerobic conditions, and its production level increased by knocking out ackA, which encodes an acetate kinase, and by overexpressing sigE, which encodes an RNA polymerase sigma factor. Glycogen catabolism and organic acid biosynthesis were enhanced in the mutant lacking ackA and overexpressing sigE, leading to an increase in succinate production reaching five times of the wild-type levels. Our genetic and metabolomic analyses thus demonstrated the effect of genetic manipulation of a metabolic enzyme and a transcriptional regulator on succinate excretion from this cyanobacterium with the data based on metabolomic technique.

SS-mPMG and SS-GA: tools for finding pathways and dynamic simulation of metabolic networks.

Metabolomics analysis tools can provide quantitative information on the concentration of metabolites in an organism. In this paper, we propose the minimum pathway model generator tool for simulating the dynamics of metabolite concentrations (SS-mPMG) and a tool for parameter estimation by genetic algorithm (SS-GA). SS-mPMG can extract a subsystem of the metabolic network from the genome-scale pathway maps to reduce the complexity of the simulation model and automatically construct a dynamic simulator to evaluate the experimentally observed behavior of metabolites. Using this tool, we show that stochastic simulation can reproduce experimentally observed dynamics of amino acid biosynthesis in Arabidopsis thaliana. In this simulation, SS-mPMG extracts the metabolic network subsystem from published databases. The parameters needed for the simulation are determined using a genetic algorithm to fit the simulation results to the experimental data. We expect that SS-mPMG and SS-GA will help researchers to create relevant metabolic networks and carry out simulations of metabolic reactions derived from metabolomics data.

Changes in mRNA stability associated with cold stress in Arabidopsis cells.

Control of mRNA half-life is a powerful strategy to adjust individual mRNA levels to various stress conditions, because the mRNA degradation rate controls not only the steady-state mRNA level but also the transition speed of mRNA levels. Here, we analyzed mRNA half-life changes in response to cold stress in Arabidopsis cells using genome-wide analysis, in which mRNA half-life measurements and transcriptome analysis were combined. Half-lives of average transcripts were determined to be elongated under cold conditions. Taking this general shift into account, we identified more than a thousand transcripts that were classified as relatively stabilized or relatively destabilized. The relatively stabilized class was predominantly observed in functional categories that included various regulators involved in transcriptional, post-transcriptional and post-translational processes. On the other hand, the relatively destabilized class was enriched in categories related to stress and hormonal response proteins, supporting the idea that rapid decay of mRNA is advantageous for swift responses to stress. In addition, pentatricopeptide repeat, cyclin-like F-box and Myb transcription factor protein families were significantly over-represented in the relatively destabilized class. The global analysis presented here demonstrates not only the importance of mRNA turnover control in the cold stress response but also several structural characteristics that might be important in the control of mRNA stability.

Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of E<10(-5)) are included in 27 clusters. Five clusters are associated with metabolism, containing P450 genes restricted to the Brassica family and predicted to be involved in secondary metabolism. Operon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system.

Role of camalexin, indole glucosinolates, and side chain modification of glucosinolate-derived isothiocyanates in defense of Arabidopsis against Sclerotinia sclerotiorum.

Plant secondary metabolites are known to facilitate interactions with a variety of beneficial and detrimental organisms, yet the contribution of specific metabolites to interactions with fungal pathogens is poorly understood. Here we show that, with respect to aliphatic glucosinolate-derived isothiocyanates, toxicity against the pathogenic ascomycete Sclerotinia sclerotiorum depends on side chain structure. Genes associated with the formation of the secondary metabolites camalexin and glucosinolate were induced in Arabidopsis thaliana leaves challenged with the necrotrophic pathogen S. sclerotiorum. Unlike S. sclerotiorum, the closely related ascomycete Botrytis cinerea was not identified to induce genes associated with aliphatic glucosinolate biosynthesis in pathogen-challenged leaves. Mutant plant lines deficient in camalexin, indole, or aliphatic glucosinolate biosynthesis were hypersusceptible to S. sclerotiorum, among them the myb28 mutant, which has a regulatory defect resulting in decreased production of long-chained aliphatic glucosinolates. The antimicrobial activity of aliphatic glucosinolate-derived isothiocyanates was dependent on side chain elongation and modification, with 8-methylsulfinyloctyl isothiocyanate being most toxic to S. sclerotiorum. This information is important for microbial associations with cruciferous host plants and for metabolic engineering of pathogen defenses in cruciferous plants that produce short-chained aliphatic glucosinolates.

Mass spectra-based framework for automated structural elucidation of metabolome data to explore phytochemical diversity.

A novel framework for automated elucidation of metabolite structures in liquid chromatography-mass spectrometer metabolome data was constructed by integrating databases. High-resolution tandem mass spectra data automatically acquired from each metabolite signal were used for database searches. Three distinct databases, KNApSAcK, ReSpect, and the PRIMe standard compound database, were employed for the structural elucidation. The outputs were retrieved using the CAS metabolite identifier for identification and putative annotation. A simple metabolite ontology system was also introduced to attain putative characterization of the metabolite signals. The automated method was applied for the metabolome data sets obtained from the rosette leaves of 20 Arabidopsis accessions. Phenotypic variations in novel Arabidopsis metabolites among these accessions could be investigated using this method.

AtMetExpress development: a phytochemical atlas of Arabidopsis development.

Plants possess many metabolic genes for the production of a wide variety of phytochemicals in a tissue-specific manner. However, the metabolic systems behind the diversity and tissue-dependent regulation still remain unknown due to incomplete characterization of phytochemicals produced in a single plant species. Thus, having a metabolome dataset in addition to the genome and transcriptome information resources would enrich our knowledge of plant secondary metabolism. Here we analyzed phytochemical accumulation during development of the model plant Arabidopsis (Arabidopsis thaliana) using liquid chromatography-mass spectrometry in samples covering many growth stages and organs. We also obtained tandem mass spectrometry spectral tags of many metabolites as a resource for elucidation of metabolite structure. These are part of the AtMetExpress metabolite accumulation atlas. Based on the dataset, we detected 1,589 metabolite signals from which the structures of 167 metabolites were elucidated. The integrated analyses with transcriptome data demonstrated that Arabidopsis produces various phytochemicals in a highly tissue-specific manner, which often accompanies the expression of key biosynthesis-related genes. We also found that a set of biosynthesis-related genes is coordinately expressed among the tissues. These data suggested that the simple mode of regulation, transcript to metabolite, is an origin of the dynamics and diversity of plant secondary metabolism.

Decoding genes with coexpression networks and metabolomics - 'majority report by precogs'.

Following the sequencing of whole genomes of model plants, high-throughput decoding of gene function is a major challenge in modern plant biology. In view of remarkable technical advances in transcriptomics and metabolomics, integrated analysis of these 'omics' by data-mining informatics is an excellent tool for prediction and identification of gene function, particularly for genes involved in complicated metabolic pathways. The availability of Arabidopsis public transcriptome datasets containing data of >1000 microarrays reinforces the potential for prediction of gene function by transcriptome coexpression analysis. Here, we review the strategy of combining transcriptome and metabolome as a powerful technology for studying the functional genomics of model plants and also crop and medicinal plants.

Predicting state transitions in the transcriptome and metabolome using a linear dynamical system model.

BACKGROUND: Modelling of time series data should not be an approximation of input data profiles, but rather be able to detect and evaluate dynamical changes in the time series data. Objective criteria that can be used to evaluate dynamical changes in data are therefore important to filter experimental noise and to enable extraction of unexpected, biologically important information. RESULTS: Here we demonstrate the effectiveness of a Markov model, named the Linear Dynamical System, to simulate the dynamics of a transcript or metabolite time series, and propose a probabilistic index that enables detection of time-sensitive changes. This method was applied to time series datasets from Bacillus subtilis and Arabidopsis thaliana grown under stress conditions; in the former, only gene expression was studied, whereas in the latter, both gene expression and metabolite accumulation. Our method not only identified well-known changes in gene expression and metabolite accumulation, but also detected novel changes that are likely to be responsible for each stress response condition. CONCLUSION: This general approach can be applied to any time-series data profile from which one wishes to identify elements responsible for state transitions, such as rapid environmental adaptation by an organism.