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

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

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>Bacillus subtilis </it>and <it>Arabidopsis thaliana </it>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.</p> <p>Conclusion</p> <p>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.</p

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

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