Akira Oikawa

Pause-and-Stop: The Effects of Osmotic Stress on Cell Proliferation during Early Leaf Development in Arabidopsis and a Role for Ethylene Signaling in Cell Cycle Arrest

This research assesses how plant leaf growth is regulated under water-limiting conditions at the cellular and molecular level. It demonstrates that growth and, more specifically, cell division responds to stress in a highly dynamic manner. Growth inhibition is mediated by ethylene signaling followed by adaptation and recovery. Despite its relevance for agricultural production, environmental stress-induced growth inhibition, which is responsible for significant yield reductions, is only poorly understood. Here, we investigated the molecular mechanisms underlying cell cycle inhibition in young proliferating leaves of the model plant Arabidopsis thaliana when subjected to mild osmotic stress. A detailed cellular analysis demonstrated that as soon as osmotic stress is sensed, cell cycle progression rapidly arrests, but cells are kept in a latent ambivalent state allowing a quick recovery (pause). Remarkably, cell cycle arrest coincides with an increase in 1-aminocyclopropane-1-carboxylate levels and the activation of ethylene signaling. Our work showed that ethylene acts on cell cycle progression via inhibition of cyclin-dependent kinase A activity independently of EIN3 transcriptional control. When the stress persists, cells exit the mitotic cell cycle and initiate the differentiation process (stop). This stop is reflected by early endoreduplication onset, in a process independent of ethylene. Nonetheless, the potential to partially recover the decreased cell numbers remains due to the activity of meristemoids. Together, these data present a conceptual framework to understand how environmental stress reduces plant growth.

Physiological Roles of the β-Substituted Alanine Synthase Gene Family in Arabidopsis

The β-substituted alanine (Ala) synthase (Bsas) family in the large superfamily of pyridoxal 5′-phosphate-dependent enzymes comprises cysteine (Cys) synthase (CSase) [O-acetyl-serine (thiol) lyase] and β-cyano-Ala synthase (CASase) in plants. Nine genomic sequences encode putative Bsas proteins in Arabidopsis thaliana. The physiological roles of these Bsas isoforms in vivo were investigated by the characterization of T-DNA insertion mutants. Analyses of gene expression, activities of CSase and CASase, and levels of Cys and glutathione in the bsas mutants indicated that cytosolic Bsas1;1, plastidic Bsas2;1, and mitochondrial Bsas2;2 play major roles in Cys biosynthesis. Cytosolic Bsas1;1 has the most dominant contribution both in leaf and root, and mitochondrial Bsas2;2 plays a significant role in root. Mitochondrial Bsas3;1 is a genuine CASase. Nontargeted metabolome analyses of knockout mutants were carried out by a combination of gas chromatography time-of-flight mass spectrometry and capillary electrophoresis time-of-flight mass spectrometry. The level of γ-glutamyl-β-cyano-Ala decreased in the mutant bsas3;1, indicating the crucial role of Bsas3;1 in β-cyano-Ala metabolism in vivo.

Dissection of genotype–phenotype associations in rice grains using metabolome quantitative trait loci analysis

A comprehensive and large-scale metabolome quantitative trait loci (mQTL) analysis was performed to investigate the genetic backgrounds associated with metabolic phenotypes in rice grains. The metabolome dataset consisted of 759 metabolite signals obtained from the grains of 85 lines of rice (Oryza sativa, Sasanishiki × Habataki back-crossed inbred lines). Metabolome analysis was performed using four mass spectrometry pipelines to enhance detection of different classes of metabolites. This mQTL analysis of a wide range of metabolites highlighted an uneven distribution of 802 mQTLs on the rice genome, as well as different modes of metabolic trait (m-trait) control among various types of metabolites. The levels of most metabolites within rice grains were highly sensitive to environmental factors, but only weakly associated with mQTLs. Coordinated control was observed for several groups of metabolites, such as amino acids linked to the mQTL hotspot on chromosome 3. For flavonoids, m-trait variation among the experimental lines was tightly governed by genetic factors that alter the glycosylation of flavones. Many loci affecting levels of metabolites were detected by QTL analysis, and plausible gene candidates were evaluated by in silico analysis. Several mQTLs profoundly influenced metabolite levels, providing insight into the control of rice metabolism. The genomic region and genes potentially responsible for the biosynthesis of apigenin-6,8-di-C-α-l-arabinoside are presented as an example of a critical mQTL identified by the analysis.

Cytochrome P450 CYP710A Encodes the Sterol C-22 Desaturase in Arabidopsis and Tomato

Δ22-Unsaturated sterols, containing a double bond at the C-22 position in the side chain, occur specifically in fungi and plants. Here, we describe the identification and characterization of cytochrome P450s belonging to the CYP710A family as the plant C-22 desaturase. Recombinant proteins of CYP710A1 and CYP710A2 from Arabidopsis thaliana and CYP710A11 from tomato (Lycopersicon esculentum) were expressed using a baculovirus/insect system. The Arabidopsis CYP710A1 and tomato CYP710A11 proteins exhibited C-22 desaturase activity with β-sitosterol to produce stigmasterol (CYP710A1, Km = 1.0 μM and kinetic constant [kcat] = 0.53 min−1; CYP710A11, Km = 3.7 μM and kcat = 10 min−1). In Arabidopsis transgenic lines with CYP710A1 and CYP710A11 overexpression, stigmasterol levels increased by 6- to 32-fold. Arabidopsis CYP710A2 was able to produce brassicasterol and stigmasterol from 24-epi-campesterol and β-sitosterol, respectively. Sterol profiling analyses for CYP710A2 overexpression and a T-DNA insertion event into CYP710A2 clearly demonstrated in planta that CYP710A2 was responsible for both brassicasterol and stigmasterol production. Semiquantitative PCR analyses and promoter:β-glucuronidase transgenic approaches indicated strict tissue/organ-specific regulation for each CYP710A gene, implicating differential tissue distributions of the Δ22-unsaturated sterols in Arabidopsis. Our results support the possibility that the CYP710 family may encode P450s of sterol C-22 desaturases in different organisms.

A γ -Glutamyl Transpeptidase-Independent Pathway of Glutathione Catabolism to Glutamate via 5-Oxoproline in Arabidopsis

The degradation pathway of glutathione (GSH) in plants is not well understood. In mammals, GSH is predominantly metabolized through the γ-glutamyl cycle, where GSH is degraded by the sequential reaction of γ-glutamyl transpeptidase (GGT), γ-glutamyl cyclotransferase, and 5-oxoprolinase to yield glutamate (Glu) and dipeptides that are subject to peptidase action. In this study, we examined if GSH is degraded through the same pathway in Arabidopsis (Arabidopsis thaliana) as occurs in mammals. In Arabidopsis, the oxoprolinase knockout mutants (oxp1-1 and oxp1-2) accumulate more 5-oxoproline (5OP) and less Glu than wild-type plants, suggesting substantial metabolite flux though 5OP and that 5OP is a major contributor to Glu steady-state levels. In the ggt1-1/ggt4-1/oxp1-1 triple mutant with no GGT activity in any organs except young siliques, the 5OP concentration in leaves was not different from that in oxp1-1, suggesting that GGTs are not major contributors to 5OP production in Arabidopsis. 5OP formation strongly tracked the level of GSH in Arabidopsis plants, suggesting that GSH is the precursor of 5OP in a GGT-independent reaction. Kinetics analysis suggests that γ-glutamyl cyclotransferase is the major source of GSH degradation and 5OP formation in Arabidopsis. This discovery led us to propose a new pathway for GSH turnover in plants where GSH is converted to 5OP and then to Glu by the combined action of γ-glutamyl cyclotransferase and 5-oxoprolinase in the cytoplasm.

Clarification of Pathway-Specific Inhibition by Fourier Transform Ion Cyclotron Resonance/Mass Spectrometry-Based Metabolic Phenotyping Studies

We have developed a metabolic profiling scheme based on direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS). The scheme consists of: (1) reproducible data collection under optimized FT-ICR/MS analytical conditions; (2) automatic mass-error correction and multivariate analyses for metabolome characterization using a newly developed metabolomics tool (DMASS software); (3) identification of marker metabolite candidates by searching a species-metabolite relationship database, KNApSAcK; and (4) structural analyses by an MS/MS method. The scheme was applied to metabolic phenotyping of Arabidopsis (Arabidopsis thaliana) seedlings treated with different herbicidal chemical classes for pathway-specific inhibitions. Arabidopsis extracts were directly infused into an electrospray ionization source on an FT-ICR/MS system. Acquired metabolomics data were comprised of mass-to-charge ratio values with ion intensity information subjected to principal component analysis, and metabolic phenotypes from the herbicide treatments were clearly differentiated from those of the herbicide-free treatment. From each herbicide treatment, candidate metabolites representing such metabolic phenotypes were found through the KNApSAcK database search. The database search and MS/MS analyses suggested dose-dependent accumulation patterns of specific metabolites including several flavonoid glycosides. The metabolic phenotyping scheme on the basis of FT-ICR/MS coupled with the DMASS program is discussed as a general tool for high throughput metabolic phenotyping studies.

Covering chemical diversity of genetically-modified tomatoes using metabolomics for objective substantial equivalence assessment.

As metabolomics can provide a biochemical snapshot of an organisms phenotype it is a promising approach for charting the unintended effects of genetic modification. A critical obstacle for this application is the inherently limited metabolomic coverage of any single analytical platform. We propose using multiple analytical platforms for the direct acquisition of an interpretable data set of estimable chemical diversity. As an example, we report an application of our multi-platform approach that assesses the substantial equivalence of tomatoes over-expressing the taste-modifying protein miraculin. In combination, the chosen platforms detected compounds that represent 86% of the estimated chemical diversity of the metabolites listed in the LycoCyc database. Following a proof-of-safety approach, we show that % had an acceptable range of variation while simultaneously indicating a reproducible transformation-related metabolic signature. We conclude that multi-platform metabolomics is an approach that is both sensitive and robust and that it constitutes a good starting point for characterizing genetically modified organisms.

Genetic Engineering of Group 2 σ Factor SigE Widely Activates Expressions of Sugar Catabolic Genes in Synechocystis Species PCC 6803

Metabolic engineering of photosynthetic organisms is required for utilization of light energy and for reducing carbon emissions.Control of transcriptional regulators is a powerful approach for changing cellular dynamics, because a set of genes is concomitantly regulated. Here, we show that overexpression of a group 2 σ factor, SigE, enhances the expressions of sugar catabolic genes in the unicellular cyanobacterium, Synechocystis sp. PCC 6803. Transcriptome analysis revealed that genes for the oxidative pentose phosphate pathway and glycogen catabolism are induced by overproduction of SigE. Immunoblotting showed that protein levels of sugar catabolic enzymes, such as glucose-6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase, glycogen phosphorylase, and isoamylase, are increased. Glycogen levels are reduced in the SigE-overexpressing strain grown under light. Metabolome analysis revealed that metabolite levels of the TCA cycle and acetyl-CoA are significantly altered by SigE overexpression. The SigE-overexpressing strain also exhibited defective growth under mixotrophic or dark conditions. Thus, SigE overexpression changes sugar catabolism at the transcript to phenotype levels, suggesting a σ factor-based engineering method for modifying carbon metabolism in photosynthetic bacteria.

Increased Bioplastic Production with an RNA Polymerase Sigma Factor SigE during Nitrogen Starvation in Synechocystis sp. PCC 6803

Because cyanobacteria directly harvest CO2 and light energy, their carbon metabolism is important for both basic and applied sciences. Here, we show that overexpression of the sigma factor sigE in Synechocystis sp. PCC 6803 widely changes sugar catabolism and increases production of the biodegradable polyester polyhydroxybutyrate (PHB) during nitrogen starvation. sigE overexpression elevates the levels of proteins implicated in glycogen catabolism, the oxidative pentose phosphate pathway, and polyhydroxyalkanoate biosynthesis. PHB accumulation is enhanced by sigE overexpression under nitrogen-limited conditions, yet the molecular weights of PHBs synthesized by the parental glucose-tolerant and sigE overexpression strain are similar. Although gene expression induced by nitrogen starvation is changed and other metabolites (such as GDP-mannose and citrate) accumulate under sigE overexpression, genetic engineering of this sigma factor altered the metabolic pathway from glycogen to PHB during nitrogen starvation.

Assessment of Metabolome Annotation Quality: A Method for Evaluating the False Discovery Rate of Elemental Composition Searches

Background In metabolomics researches using mass spectrometry (MS), systematic searching of high-resolution mass data against compound databases is often the first step of metabolite annotation to determine elemental compositions possessing similar theoretical mass numbers. However, incorrect hits derived from errors in mass analyses will be included in the results of elemental composition searches. To assess the quality of peak annotation information, a novel methodology for false discovery rates (FDR) evaluation is presented in this study. Based on the FDR analyses, several aspects of an elemental composition search, including setting a threshold, estimating FDR, and the types of elemental composition databases most reliable for searching are discussed. Methodology/Principal Findings The FDR can be determined from one measured value (i.e., the hit rate for search queries) and four parameters determined by Monte Carlo simulation. The results indicate that relatively high FDR values (30–50%) were obtained when searching time-of-flight (TOF)/MS data using the KNApSAcK and KEGG databases. In addition, searches against large all-in-one databases (e.g., PubChem) always produced unacceptable results (FDR >70%). The estimated FDRs suggest that the quality of search results can be improved not only by performing more accurate mass analysis but also by modifying the properties of the compound database. A theoretical analysis indicates that FDR could be improved by using compound database with smaller but higher completeness entries. Conclusions/Significance High accuracy mass analysis, such as Fourier transform (FT)-MS, is needed for reliable annotation (FDR <10%). In addition, a small, customized compound database is preferable for high-quality annotation of metabolome data.