ANGUSTIFOLIA3 Signaling Coordinates Proliferation between Clonally Distinct Cells in Leaves

SummaryCoordinated proliferation between clonally distinct cells via inter-cell-layer signaling largely determines the size and shape of plant organs [1–4]. Nonetheless, the signaling mechanism underlying this coordination in leaves remains elusive because of a lack of understanding of the signaling molecule (or molecules) involved. ANGUSTIFOLIA3 (AN3, also called GRF-INTERACTING FACTOR1) encodes a putative transcriptional coactivator with homology to human synovial sarcoma translocation protein [5–7]. AN3 transcripts accumulate in mesophyll cells but are not detectable in leaf epidermal cells [8]. However, we found here that in addition to mesophyll cells [5, 6], epidermal cells of an3 leaves show defective proliferation. This spatial difference between the accumulation pattern of AN3 transcripts and an3 leaf phenotype is explained by AN3 protein movement across cell layers. AN3 moves into epidermal cells after being synthesized within mesophyll cells and helps control epidermal cell proliferation. Interference with AN3 movement results in abnormal leaf size and shape, indicating that AN3 signaling is indispensable for normal leaf development. AN3 movement does not require type II chaperonin activity, which is needed for movement of some mobile proteins [9]. Taking these findings together, we present a novel model emphasizing the role of mesophyll cells as a signaling source coordinating proliferation between clonally independent leaf cells

A metabolome genome-wide association study implicates histidine N-pi-methyltransferase as a key enzyme in N-methylhistidine biosynthesis in Arabidopsis thaliana

A genome-wide association study (GWAS), which uses information on single nucleotide polymorphisms (SNPs) from many accessions, has become a powerful approach to gene identification. A metabolome GWAS (mGWAS), which relies on phenotypic information based on metabolite accumulation, can identify genes that contribute to primary and secondary metabolite contents. In this study, we carried out a mGWAS using seed metabolomic data from Arabidopsis thaliana accessions obtained by liquid chromatography–mass spectrometry to identify SNPs highly associated with the contents of metabolites such as glucosinolates. These SNPs were present in genes known to be involved in glucosinolate biosynthesis, thus confirming the effectiveness of our analysis. We subsequently focused on SNPs detected in an unknown methyltransferase gene associated with N-methylhistidine content. Knockout and overexpression of A. thaliana lines of this gene had significantly decreased and increased N-methylhistidine contents, respectively. We confirmed that the overexpressing line exclusively accumulated histidine methylated at the pi position, not at the tau position. Our findings suggest that the identified methyltransferase gene encodes a key enzyme for N-methylhistidine biosynthesis in A. thaliana

rre37

The tricarboxylic acid (TCA) cycle and pyruvate metabolism of cyanobacteria are unique and important from the perspectives of biology and biotechnology research. Rre37, a response regulator induced by nitrogen depletion, activates gene expression related to sugar catabolism. Our previous microarray analysis has suggested that Rre37 controls the transcription of genes involved in sugar catabolism, pyruvate metabolism, and the TCA cycle. In this study, quantitative real-time PCR was used to measure the transcript levels of 12 TCA cycle genes and 13 pyruvate metabolism genes. The transcripts of 6 genes (acnB, icd, ppc, pyk1, me, and pta) increased after 4 h of nitrogen depletion in the wild-type GT strain but the induction was abolished by rre37 overexpression. The repression of gene expression of fumC, ddh, and ackA caused by nitrogen depletion was abolished by rre37 overexpression. The expression of me was differently affected by rre37 overexpression, compared to the other 24 genes. These results indicate that Rre37 differently controls the genes of the TCA cycle and pyruvate metabolism, implying the key reaction of the primary in this unicellular cyanobacterium

Rre37 stimulates accumulation of 2-oxoglutarate and glycogen under nitrogen starvation in Synechocystis sp. PCC 6803

AbstractRre37 (sll1330) in a cyanobacterium Synechocystis sp. PCC 6803 acts as a regulatory protein for sugar catabolic genes during nitrogen starvation. Low glycogen accumulation in Δrre37 was due to low expression of glycogen anabolic genes. In addition to low 2-oxoglutarate accumulation, normal upregulated expression of genes encoding glutamate synthases (gltD and gltB) as well as accumulation of metabolites in glycolysis (fructose-6-phosphate, fructose-1,6-bisphosphate, and glyceraldehyde-3-phosphate) and tricarboxylic acid (TCA) cycle (oxaloacetate, fumarate, succinate, and aconitate) were abolished by rre37 knockout. Rre37 regulates 2-oxoglutarate accumulation, glycogen accumulation through expression of glycogen anabolic genes, and TCA cycle metabolites accumulation

Establishment of an in planta magnesium monitoring system using CAX3 promoter-luciferase in Arabidopsis

The direct determination of elemental concentrations in plants is laborious. To overcome this, a novel monitoring system for magnesium (Mg) in plants was established. Mg deficiency-induced genes were identified by microarray analysis and transgenic lines that expressed luciferase (LUC) under the control of the Mg deficiency-inducible CAX3 promoter were established. The transgenic lines showed a clear response under low Mg conditions, and the degree of luminescence reflected the accumulation of endogenous CAX3 mRNA. The CAX3 expression pattern was also examined in a previously characterized low Mg-sensitive mutant, mrs2-7. In mrs2-7 mutant plants, CAX3 expression was more than three times higher than in the wild-type. In addition, CAX3 expression was negatively correlated with the shoot Mg concentration. Together, these results indicate that CAX3 transcription is a quantitative marker of the Mg status in Arabidopsis

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

Metabolism and Regulatory Functions of O-Acetylserine, S-Adenosylmethionine, Homocysteine, and Serine in Plant Development and Environmental Responses

The metabolism of an organism is closely related to both its internal and external environments. Metabolites can act as signal molecules that regulate the functions of genes and proteins, reflecting the status of these environments. This review discusses the metabolism and regulatory functions of O-acetylserine (OAS), S-adenosylmethionine (AdoMet), homocysteine (Hcy), and serine (Ser), which are key metabolites related to sulfur (S)-containing amino acids in plant metabolic networks, in comparison to microbial and animal metabolism. Plants are photosynthetic auxotrophs that have evolved a specific metabolic network different from those in other living organisms. Although amino acids are the building blocks of proteins and common metabolites in all living organisms, their metabolism and regulation in plants have specific features that differ from those in animals and bacteria. In plants, cysteine (Cys), an S-containing amino acid, is synthesized from sulfide and OAS derived from Ser. Methionine (Met), another S-containing amino acid, is also closely related to Ser metabolism because of its thiomethyl moiety. Its S atom is derived from Cys and its methyl group from folates, which are involved in one-carbon metabolism with Ser. One-carbon metabolism is also involved in the biosynthesis of AdoMet, which serves as a methyl donor in the methylation reactions of various biomolecules. Ser is synthesized in three pathways: the phosphorylated pathway found in all organisms and the glycolate and the glycerate pathways, which are specific to plants. Ser metabolism is not only important in Ser supply but also involved in many other functions. Among the metabolites in this network, OAS is known to function as a signal molecule to regulate the expression of OAS gene clusters in response to environmental factors. AdoMet regulates amino acid metabolism at enzymatic and translational levels and regulates gene expression as methyl donor in the DNA and histone methylation or after conversion into bioactive molecules such as polyamine and ethylene. Hcy is involved in Met–AdoMet metabolism and can regulate Ser biosynthesis at an enzymatic level. Ser metabolism is involved in development and stress responses. This review aims to summarize the metabolism and regulatory functions of OAS, AdoMet, Hcy, and Ser and compare the available knowledge for plants with that for animals and bacteria and propose a future perspective on plant research

Identification of a metabolic reaction network from time-series data of metabolite concentrations.

Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the metabolic profiles. However, these analyses cannot simultaneously carry out identification of metabolic reaction networks and prediction of dynamic behaviour of metabolites in the networks. The present study, therefore, proposes a new approach consisting of a combination of statistical technique and mathematical modelling approach to identify and predict a probable metabolic reaction network from time-series data of metabolite concentrations and simultaneously construct its mathematical model. Firstly, regression functions are fitted to experimental data by the locally estimated scatter plot smoothing method. Secondly, the fitted result is analysed by the bivariate Granger causality test to determine which metabolites cause the change in other metabolite concentrations and remove less related metabolites. Thirdly, S-system equations are formed by using the remaining metabolites within the framework of biochemical systems theory. Finally, parameters including rate constants and kinetic orders are estimated by the Levenberg-Marquardt algorithm. The estimation is iterated by setting insignificant kinetic orders at zero, i.e., removing insignificant metabolites. Consequently, a reaction network structure is identified and its mathematical model is obtained. Our approach is validated using a generic inhibition and activation model and its practical application is tested using a simplified model of the glycolysis of Lactococcus lactis MG1363, for which actual time-series data of metabolite concentrations are available. The results indicate the usefulness of our approach and suggest a probable pathway for the production of lactate and acetate. The results also indicate that the approach pinpoints a probable strong inhibition of lactate on the glycolysis pathway