Enantioselective Phytotoxicity of the Herbicide Imazethapyr on the Response of the Antioxidant System and Starch Metabolism in Arabidopsis thaliana

Background: The enantiomers of a chiral compound possess different biological activities, and one of the enantiomers usually shows a higher level of toxicity. Therefore, the exploration of the causative mechanism of enantioselective toxicity is regarded as one of primary goals of biological chemistry. Imazethapyr (IM) is an acetolactate synthase (ALS)-inhibiting chiral herbicide that has been widely used in recent years with racemate. We investigated the enantioselectivity between R- and S-IM to form reactive oxygen species (ROS) and to regulate antioxidant gene transcription and enzyme activity. Results: Dramatic differences between the enantiomers were observed: the enantiomer of R-IM powerfully induced ROS formation, yet drastically reduced antioxidant gene transcription and enzyme activity, which led to an oxidative stress. The mechanism by which IM affects carbohydrate metabolism in chloroplasts has long remained a mystery. Here we report evidence that enantioselectivity also exists in starch metabolism. The enantiomer of R-IM resulted in the accumulation of glucose, maltose and sucrose in the cytoplasm or the chloroplast and disturbed carbohydrates utilization. Conclusion: The study suggests that R-IM more strongly retarded plant growth than S-IM not only by acting on ALS, but also by causing an imbalance in the antioxidant system and the disturbance of carbohydrate metabolism wit

Systematic Analysis of Stability Patterns in Plant Primary Metabolism

Metabolic networks are characterized by complex interactions and regulatory mechanisms between many individual components. These interactions determine whether a steady state is stable to perturbations. Structural kinetic modeling (SKM) is a framework to analyze the stability of metabolic steady states that allows the study of the system Jacobian without requiring detailed knowledge about individual rate equations. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. Until now, SKM experiments applied univariate tests to detect the network components with the largest influence on stability. In this work, we present an extended SKM approach relying on supervised machine learning to detect patterns of enzyme-metabolite interactions that act together in an orchestrated manner to ensure stability. We demonstrate its application on a detailed SK-model of the Calvin-Benson cycle and connected pathways. The identified stability patterns are highly complex reflecting that changes in dynamic properties depend on concerted interactions between several network components. In total, we find more patterns that reliably ensure stability than patterns ensuring instability. This shows that the design of this system is strongly targeted towards maintaining stability. We also investigate the effect of allosteric regulators revealing that the tendency to stability is significantly increased by including experimentally determined regulatory mechanisms that have not yet been integrated into existing kinetic models

Plastidial Starch Phosphorylase in Sweet Potato Roots Is Proteolytically Modified by Protein-Protein Interaction with the 20S Proteasome

Post-translational regulation plays an important role in cellular metabolism. Earlier studies showed that the activity of plastidial starch phosphorylase (Pho1) may be regulated by proteolytic modification. During the purification of Pho1 from sweet potato roots, we observed an unknown high molecular weight complex (HX) showing Pho1 activity. The two-dimensional gel electrophoresis, mass spectrometry, and reverse immunoprecipitation analyses showed that HX is composed of Pho1 and the 20S proteasome. Incubating sweet potato roots at 45°C triggers a stepwise degradation of Pho1; however, the degradation process can be partially inhibited by specific proteasome inhibitor MG132. The proteolytically modified Pho1 displays a lower binding affinity toward glucose 1-phosphate and a reduced starch-synthesizing activity. This study suggests that the 20S proteasome interacts with Pho1 and is involved in the regulation of the catalytic activity of Pho1 in sweet potato roots under heat stress conditions

Enzymatic degradation of granular potato starch by Microbacterium aurum strain B8.A

Microbacterium aurum strain B8.A was isolated from the sludge of a potato starch-processing factory on the basis of its ability to use granular starch as carbon- and energy source. Extracellular enzymes hydrolyzing granular starch were detected in the growth medium of M. aurum B8.A, while the type strain M. aurum DSMZ 8600 produced very little amylase activity, and hence was unable to degrade granular starch. The strain B8.A extracellular enzyme fraction degraded wheat, tapioca and potato starch at 37 °C, well below the gelatinization temperature of these starches. Starch granules of potato were hydrolyzed more slowly than of wheat and tapioca, probably due to structural differences and/or surface area effects. Partial hydrolysis of starch granules by extracellular enzymes of strain B8.A resulted in large holes of irregular sizes in case of wheat and tapioca and many smaller pores of relatively homogeneous size in case of potato. The strain B8.A extracellular amylolytic system produced mainly maltotriose and maltose from both granular and soluble starch substrates; also, larger maltooligosaccharides were formed after growth of strain B8.A in rich medium. Zymogram analysis confirmed that a different set of amylolytic enzymes was present depending on the growth conditions of M. aurum B8.A. Some of these enzymes could be partly purified by binding to starch granules

Diversification of Genes Encoding Granule-Bound Starch Synthase in Monocots and Dicots Is Marked by Multiple Genome-Wide Duplication Events

Starch is one of the major components of cereals, tubers, and fruits. Genes encoding granule-bound starch synthase (GBSS), which is responsible for amylose synthesis, have been extensively studied in cereals but little is known about them in fruits. Due to their low copy gene number, GBSS genes have been used to study plant phylogenetic and evolutionary relationships. In this study, GBSS genes have been isolated and characterized in three fruit trees, including apple, peach, and orange. Moreover, a comprehensive evolutionary study of GBSS genes has also been conducted between both monocots and eudicots. Results have revealed that genomic structures of GBSS genes in plants are conserved, suggesting they all have evolved from a common ancestor. In addition, the GBSS gene in an ancestral angiosperm must have undergone genome duplication ∼251 million years ago (MYA) to generate two families, GBSSI and GBSSII. Both GBSSI and GBSSII are found in monocots; however, GBSSI is absent in eudicots. The ancestral GBSSII must have undergone further divergence when monocots and eudicots split ∼165 MYA. This is consistent with expression profiles of GBSS genes, wherein these profiles are more similar to those of GBSSII in eudicots than to those of GBSSI genes in monocots. In dicots, GBSSII must have undergone further divergence when rosids and asterids split from each other ∼126 MYA. Taken together, these findings suggest that it is GBSSII rather than GBSSI of monocots that have orthologous relationships with GBSS genes of eudicots. Moreover, diversification of GBSS genes is mainly associated with genome-wide duplication events throughout the evolutionary course of history of monocots and eudicots

Carbon Dynamics, Development and Stress Responses in Arabidopsis: Involvement of the APL4 Subunit of ADP-Glucose Pyrophosphorylase (Starch Synthesis)

An Arabidopsis thaliana T-DNA insertional mutant was identified and characterized for enhanced tolerance to the singlet-oxygen-generating herbicide atrazine in comparison to wild-type. This enhanced atrazine tolerance mutant was shown to be affected in the promoter structure and in the regulation of expression of the APL4 isoform of ADP-glucose pyrophosphorylase, a key enzyme of the starch biosynthesis pathway, thus resulting in decrease of APL4 mRNA levels. The impact of this regulatory mutation was confirmed by the analysis of an independent T-DNA insertional mutant also affected in the promoter of the APL4 gene. The resulting tissue-specific modifications of carbon partitioning in plantlets and the effects on plantlet growth and stress tolerance point out to specific and non-redundant roles of APL4 in root carbon dynamics, shoot-root relationships and sink regulations of photosynthesis. Given the effects of exogenous sugar treatments and of endogenous sugar levels on atrazine tolerance in wild-type Arabidopsis plantlets, atrazine tolerance of this apl4 mutant is discussed in terms of perception of carbon status and of investment of sugar allocation in xenobiotic and oxidative stress responses

Editorial overview: physiology and metabolism: plant metabolism: globules to global, modules to models

In the 1980s, the first genes encoding plant enzymes were cloned and plant genetic transformation was born. With the new century, knockout technology was established and the Arabidopsis genome sequence was published. In the last decade, the discipline of plant genomics has expanded rapidly. These developments promised a quantum leap forward in understanding plant biology. In some areas this has undoubtedly been the case, but what about in metabolism and related physiology? This issue of Current Opinion in Plant Biology presents a spectrum of topics from subcellular to global levels. It aims to provide an update on some mainstream topics and some that are less well known. It hopefully provides not only an update, but also an opportunity to reflect on the achievements of the last 35 years and to anticipate the challenges and possibilities in the next 35 years to 2050

Directed partial correlation : inferring large-scale gene regulatory network through induced topology disruptions

Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, largescale data. We propose a directed partial correlation (DPC) method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for the DREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. The R package DPC is available for download (http://code.google.com/p/dpcnet/)