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1.

Aims

The mechanisms underlying magnesium (Mg) uptake by plant roots remain to be fully elucidated. In particular, there is little information about the effects of Mg deficiency on Mg uptake activity. A Mg uptake kinetic study is essential for better understanding the Mg uptake system.

Methods

We performed a Mg uptake tracer experiment in rice plants using 28?Mg.

Results

Mg uptake was mediated by high- and low-affinity transport systems. The K m value of the high-affinity transport system was approximately 70 μM under Mg-deficient conditions. The Mg uptake activity was promoted by Mg deficiency, which in turn fell to the basal level after 5- min of Mg resupply. The induced uptake rate was inhibited by ionophore treatment, suggesting that an energy-dependent uptake system is enhanced by Mg deficiency.

Conclusions

The Mg uptake changes rapidly with Mg conditions in rice, as revealed by a 28?Mg tracer experiment. This technique is expected to be applicable for Mg uptake analyses, particularly in mutants or other lines.
  相似文献   
2.
3.
Unequal absorption of photons between photosystems I and II, and between bundle-sheath and mesophyll cells, are likely to affect the efficiency of the CO2-concentrating mechanism in C4 plants. Under steady-state conditions, it is expected that the biochemical distribution of energy (ATP and NADPH) and photosynthetic metabolite concentrations will adjust to maintain the efficiency of C4 photosynthesis through the coordination of the C3 (Calvin-Benson-Bassham) and C4 (CO2 pump) cycles. However, under transient conditions, changes in light quality will likely alter the coordination of the C3 and C4 cycles, influencing rates of CO2 assimilation and decreasing the efficiency of the CO2-concentrating mechanism. To test these hypotheses, we measured leaf gas exchange, leaf discrimination, chlorophyll fluorescence, electrochromatic shift, photosynthetic metabolite pools, and chloroplast movement in maize (Zea mays) and Miscanthus × giganteus following transitional changes in light quality. In both species, the rate of net CO2 assimilation responded quickly to changes in light treatments, with lower rates of net CO2 assimilation under blue light compared with red, green, and blue light, red light, and green light. Under steady state, the efficiency of CO2-concentrating mechanisms was similar; however, transient changes affected the coordination of C3 and C4 cycles in M. giganteus but to a lesser extent in maize. The species differences in the ability to coordinate the activities of C3 and C4 cycles appear to be related to differences in the response of cyclic electron flux around photosystem I and potentially chloroplast rearrangement in response to changes in light quality.The CO2-concentrating mechanism in C4 plants reduces the carbon lost through the photorespiratory pathway by limiting the oxygenation of ribulose-1,5-bisphosphate (RuBP) by the enzyme Rubisco (Brown and Smith, 1972; Sage, 1999). Through the compartmentalization of the C4 cycle in the mesophyll cells and the C3 cycle in the bundle-sheath cells (Hatch and Slack, 1966), C4 plants suppress RuBP oxygenation by generating a high CO2 partial pressure around Rubisco (Furbank and Hatch, 1987). To maintain high photosynthetic rates and efficient light energy utilization, the metabolic flux through the C3 and C4 cycles must be coordinated. However, coordination of the C3 and C4 cycles is likely disrupted due to rapid changes in environmental conditions, particularly changes in light availability (Evans et al., 2007; Tazoe et al., 2008).Spatial and temporal variations in light environments, including both light quantity and quality, are expected to alter the coordination of the C3 and C4 cycles. For example, it has been suggested that the coordination of C3 and C4 cycles is altered by changes in light intensity (Henderson et al., 1992; Cousins et al., 2006; Tazoe et al., 2006, 2008; Kromdijk et al., 2008, 2010; Pengelly et al., 2010). However, more recent publications indicate that some of the proposed light sensitivity of the CO2-concentrating mechanisms in C4 plants can be attributed to oversimplifications of leaf models of carbon isotope discrimination (Δ13C), in particular, errors in estimates of bundle-sheath CO2 partial pressure and omissions of respiratory fractionation (Ubierna et al., 2011, 2013). Alternatively, there is little information on the effects of light quality on the coordination of C3 and C4 cycle activities and the subsequent impact on net rate of CO2 assimilation (Anet).In C3 plants, Anet is reduced under blue light compared with red or green light (Evans and Vogelmann, 2003; Loreto et al., 2009). This was attributed to differences in absorbance and wavelength-dependent differences in light penetration into leaves, where red and green light penetrate farther into leaves compared with blue light (Vogelmann and Evans, 2002; Evans and Vogelmann, 2003). Differences in light quality penetration into a leaf are likely to have profound impacts on C4 photosynthesis, because the C4 photosynthetic pathway requires the metabolic coordination of the mesophyll C4 cycle and the bundle-sheath C3 cycle. Indeed, Evans et al. (2007) observed a 50% reduction in the rate of CO2 assimilation in Flaveria bidentis under blue light relative to white light at a light intensity of 350 µmol quanta m−2 s−1. This was attributed to poor penetration of blue light into the bundle-sheath cells and subsequent insufficient production of ATP in the bundle-sheath cells to match the rates of mesophyll cell CO2 pumping (Evans et al., 2007). Recently, Sun et al. (2012) observed similar low rates of steady-state CO2 assimilation under blue light relative to red, green, and blue light (RGB), red light, and green light at a constant light intensity of 900 µmol quanta m−2 s−1.Because the light penetration into a leaf depends on light quality, with blue light penetrating the least, this potentially results in changes in the energy available for carboxylation reactions in the bundle-sheath (C3 cycle) and mesophyll (C4 cycle) cells. Changes in the balance of energy driving the C3 and C4 cycles can alter the efficiency of the CO2-concentrating mechanisms, often represented by leakiness (ϕ), the fraction of CO2 that is pumped into the bundle-sheath cells that subsequently leaks back out (Evans et al., 2007). Unfortunately, ϕ cannot be measured directly, but it can be estimated through the combined measured and modeled values of Δ13C (Farquhar, 1983). Using measurements of Δ13C, it has been demonstrated that under steady-state conditions, changes in light quality do not affect ϕ (Sun et al., 2012); however, it remains unknown if ϕ is also constant during the transitions between different light qualities. In fact, sudden changes of light quality could temporally alter the coordination of the C3 and C4 cycles.To understand the effects of light quality on C4 photosynthesis and the coordination of the activities of C3 and C4 cycles, we measured transitional changes in leaf gas exchange and Δ13C under RGB and broad-spectrum red, green, and blue light in the NADP-malic enzyme C4 plants maize (Zea mays) and Miscanthus × giganteus. Leaf gas exchange and Δ13C measurements were used to estimate ϕ using the complete model of C4 leaf Δ13C (Farquhar, 1983; Farquhar and Cernusak, 2012). Additionally, we measured photosynthetic metabolite pools, Rubisco activation state, chloroplast movement, and rates of linear versus cyclic electron flow during rapid transitions from red to blue light and blue to red light. We hypothesized that the limited penetration of blue light into the leaf would result in insufficient production of ATP in the bundle-sheath cells to match the rate of mesophyll cell CO2 pumping. We predicted that rapid changes in light quality would affect the coordination of the C3 and C4 cycles and cause an increase in ϕ, but this would equilibrate as leaf metabolism reached a new steady-state condition.  相似文献   
4.
5.
The carbon dioxide (CO2)-concentrating mechanism of cyanobacteria is characterized by the occurrence of Rubisco-containing microcompartments called carboxysomes within cells. The encapsulation of Rubisco allows for high-CO2 concentrations at the site of fixation, providing an advantage in low-CO2 environments. Cyanobacteria with Form-IA Rubisco contain α-carboxysomes, and cyanobacteria with Form-IB Rubisco contain β-carboxysomes. The two carboxysome types have arisen through convergent evolution, and α-cyanobacteria and β-cyanobacteria occupy different ecological niches. Here, we present, to our knowledge, the first direct comparison of the carboxysome function from α-cyanobacteria (Cyanobium spp. PCC7001) and β-cyanobacteria (Synechococcus spp. PCC7942) with similar inorganic carbon (Ci; as CO2 and HCO3) transporter systems. Despite evolutionary and structural differences between α-carboxysomes and β-carboxysomes, we found that the two strains are remarkably similar in many physiological parameters, particularly the response of photosynthesis to light and external Ci and their modulation of internal ribulose-1,5-bisphosphate, phosphoglycerate, and Ci pools when grown under comparable conditions. In addition, the different Rubisco forms present in each carboxysome had almost identical kinetic parameters. The conclusions indicate that the possession of different carboxysome types does not significantly influence the physiological function of these species and that similar carboxysome function may be possessed by each carboxysome type. Interestingly, both carboxysome types showed a response to cytosolic Ci, which is of higher affinity than predicted by current models, being saturated by 5 to 15 mm Ci. This finding has bearing on the viability of transplanting functional carboxysomes into the C3 chloroplast.Cyanobacteria inhabit a diverse range of ecological habitats, including both freshwater and marine ecosystems. The flexibility to occupy these different habitats is thought to come in part from the carbon-concentrating mechanism (CCM) present in all species (Badger et al., 2006). The CCM comprises inorganic carbon (Ci; as carbon dioxide [CO2] and HCO3) transporters for Ci uptake and protein microbodies called carboxysomes for CO2 concentration and fixation by Rubisco (Badger and Price, 2003). The CCM is believed to have evolved in response to changes in the absolute and relative levels of CO2 and oxygen (O2) in the atmosphere during the evolution of oxygenic photosynthesis in cyanobacteria (Price et al., 2008).There are two main phylogenetic groups within the cyanobacteria based on Rubisco and carboxysome phylogenies; α-cyanobacteria have α-carboxysomes with Form-IA Rubisco, whereas β-cyanobacteria have β-carboxysomes with Form-IB Rubisco (Tabita, 1999; Badger et al., 2002). Rubisco large subunit protein sequences from these two groups are closely related but nevertheless, distinguishable (Supplemental Fig. S1). In general, α-cyanobacteria and β-cyanobacteria occupy a quite different range of ecological habitats. The α-cyanobacteria are mostly marine organisms, with the majority of species living in the open ocean (Badger et al., 2006). Marine α-cyanobacteria live in very stable environments with high pH (pH 8.2) and dissolved carbon levels but low nutrients. They are characterized by small cells, very small genomes (1.6–2.8 Mb), and a few constitutively expressed carbon uptake transporters (Rae et al., 2011; Beck et al., 2012). They have been described as low flux, low energy cyanobacteria with a minimal CCM (Badger et al., 2006). Although these species are slow growing, oceanic cyanobacteria contribute as much as one-half of oceanic primary productivity (Liu et al., 1997, 1999; Field et al., 1998), suggesting that they may contribute up to 25% to net global productivity every year.In comparison, β-cyanobacteria occupy a much more diverse range of habitats, including freshwater, estuarine, and hot springs and never reach the same levels of global abundance (Badger et al., 2006). They are characterized by larger cells, larger genomes (2.2–3.6 Mb), and an array of carbon uptake transporters, including those transporters induced under low Ci (Rae et al., 2011, 2013). In addition to these broadly defined α-groups and β-groups, there are small numbers of α-cyanobacteria that have been termed transitional strains (Price, 2011; Rae et al., 2011). These species (e.g. Cyanobium spp. PCC7001, Synechococcus spp. WH5701, and Cyanobium spp. PCC6307; Supplemental Fig. S1) live in marginal marine and freshwater environments and have a number of characteristics similar to β-cyanobacteria. For example, they have a more diverse range of Ci uptake systems and a significantly larger genome than closely related α-cyanobacteria, and it has been suggested that the additional genes encoding transport systems were acquired by horizontal gene transfer (HGT) from β-cyanobacteria (Rae et al., 2011).Although the carboxysomes from α-cyanobacteria and β-cyanobacteria are very similar in overall structure, in that they share an outer protein shell of common phylogenetic origin (Kerfeld et al., 2005), they are distinguished from each other largely by differences in the proteins, which seem to make up or interact with the interior of the carboxysome compartment (Supplemental Table S1). This finding suggests that their different structures today have arisen through periods of common and convergent evolution. Certain carboxysome shell proteins from α-carboxysomes and β-carboxysomes show regions of significant sequence homology. These proteins are denoted as CsoS1 to CsoS4 (in α-cyanobacteria) and CcmKLO (in β-cyanobacteria), and the homologous regions have been termed bacterial microcompartment domains (Kerfeld et al., 2010; Rae et al., 2013). Proteins with these domains are also found in bacterial microcompartments in proteobacteria. However, other identified carboxysome proteins do not show any sequence homology between α-carboxysomes and β-carboxysomes but may perform similar functional roles. For example, carbonic anhydrase activity is essential for carboxysome function, but its activity seems to be provided by a range of different proteins (β-CcaA, β-CcmM, and α-CsoSCA; Kupriyanova et al., 2013). Similarly, β-CcmM and α-CsoS2 could play similar roles in organizing the interface between the shell and Rubisco within the carboxysomes (Gonzales et al., 2005; Long et al., 2007).The functioning of a carboxysome relies on a number of biochemical properties associated with the protein microbody structure. These properties include the biochemical/kinetic properties of Rubisco contained within carboxysomes, the conductance of the carboxysome shell to the influx of substrate ribulose-1,5-bisphosphate (RuBP) and the efflux of the carboxylation product phosphoglycerate (PGA), the conductance of the shell to the influx of bicarbonate and the efflux of CO2, and lastly, the manner in which bicarbonate is converted to CO2 within the carboxysomes. α-Carboxysomes and β-carboxysomes have the potential to differ in each of these properties. The flux of phosphorylated sugars across the shell has been postulated to be mediated by the pores in the hexameric shell proteins (Yeates et al., 2010; Kinney et al., 2011), which although similar, do differ between the two carboxysomes types. Bicarbonate and CO2 uptake processes are less well-defined but probably involve aspects of the way in which unique shell interface proteins interact with Rubisco, which also differs in that CsoS2 and CsoSCA are probably the interacting proteins involved in α-carboxysomes (Espie and Kimber, 2011), whereas CcmM and β-carboxysomal CA are variably involved in β-carboxysomes (Long et al., 2010). Finally, the Form-IA and Form-IB Rubisco proteins at the heart of carboxylation, although similar, have the potential to show different kinetic properties. Although Form-IB Rubiscos from β-cyanobacteria are well-characterized, the Form-IA counterparts have received very little attention. In addition, the CCM of very few strains of cyanobacteria have been studied at the level of biochemistry and physiology, and they have been almost exclusively β-cyanobacteria. As a result, there are significant gaps in our knowledge about the similarities and differences in functional traits between α-cyanobacterial and β-cyanobacterial strains. One important question that remains to be answered is whether α-carboxysomes and β-carboxysomes have intrinsic differences in their biochemical properties that influence the nature of the CCM, which is established within each broad cell type.Because of the difficulties in isolating and assaying intact carboxysomes in vitro, the characterization of biochemical properties of carboxysomes is not easily addressed. One way forward is to study the properties of the CCM in detail in a model representative strain from each group and compare their characteristics to contrast the intracellular function of α-cell types and β-cell types. In the past, it has been restricted because of the difficulties in growing many of the open ocean α-cyanobacteria and their very different natures in relation to inorganic transporter composition. However, the availability of α-cyanobacteria transition strains, which grow well in the laboratory, has provided an opportunity to address this question. The α-cyanobacteria Cyanobium spp. PCC7001 (hereafter Cyanobium spp.), in particular, grows in standard freshwater media (BG11) and has growth and photosynthetic performance properties that closely match the model β-cyanobacteria, Synechococcus spp. PCC7942 (hereafter Synechococcus spp.); for this reason, Cyanobium spp. is ideal for a balanced comparison of the in vivo physiological properties of α-carboxysomes and β-carboxysomes in two species with relatively similar Ci-uptake properties.Genome analysis of both strains indicates that Cyanobium spp. have many of the same carbon uptake systems present in Synechococcus spp. (Rae et al., 2011). In using two strains with such similar transport capacities, we aimed to shed light on aspects of the functional properties of carboxysomes in each strain and how these properties affect the operation of the CCM in α-cyanobacteria and β-cyanobacteria. Using both membrane inlet mass spectrometry (MIMS) and silicon oil centrifugation, we investigated Ci pool sizes and CO2 uptake rates in both species for cells grown at high and low CO2. Comparative Rubisco properties and photosynthetic rates of each species were determined, and intracellular pools of RuBP and PGA were measured. In addition, we characterized a number of cellular properties to determine differences in the biochemical environments in which each carboxysome type exists. Together, the results provide a unique functional comparison of two distinct carboxysome types from phylogenetically disparate cyanobacteria.  相似文献   
6.
High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays ‘Fernandez’) plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable.Plant bioinformatics faces the challenge of integrating information from the related “omics” fields to elucidate the functional relationship between genotype and observed phenotype (Edwards and Batley, 2004), known as the genotype-phenotype map (Houle et al., 2010). One of the main obstacles is our currently limited ability of systemic depiction and quantification of plant phenotypes, representing the so-called phenotyping bottleneck phenomenon (Furbank and Tester, 2011). To get a comprehensive genotype-phenotype map, more accurate and precise phenotyping strategies are required to empower high-resolution linkage mapping and genome-wide association studies in order to uncover underlying genetic variants associated with complex phenotypic traits, which aim to improve the efficiency, effectiveness, and economy of cultivars in plant breeding (Cobb et al., 2013). In the era of phenomics, automatic high-throughput phenotyping in a noninvasive manner is applied to identify and quantify plant phenotypic traits. Plants are bred in fully automated greenhouses under predefined environmental conditions with controlled temperature, watering, and humidity. To meet the demand of data access, exchange, and sharing, several phenomics-related projects in the context of several consortia have been launched, such as the International Plant Phenotyping Network (http://www.plantphenomics.com/), the European Plant Phenotyping Network (http://www.plant-phenotyping-network.eu/), and the German Plant Phenotyping Network (http://www.dppn.de/).Thanks to the development of new imaging and transport systems, various automated or semiautomated high-throughput plant phenotyping systems are being developed and used to examine plant function and performance under controlled conditions. PHENOPSIS (Granier et al., 2006) is one of the pioneering platforms that was developed to dissect genotype-environment effects on plant growth in Arabidopsis (Arabidopsis thaliana). GROWSCREEN (Walter et al., 2007; Biskup et al., 2009; Jansen et al., 2009; Nagel et al., 2012) was designed for rapid optical phenotyping of different plant species with respect to different biological aspects. Other systems in the context of high-throughput phenotyping include Phenodyn/Phenoarch (Sadok et al., 2007), TraitMill (Reuzeau et al., 2005; Reuzeau, 2007), Phenoscope (Tisné et al., 2013), RootReader3D (Clark et al., 2011), GROW Map (http://www.fz-juelich.de/ibg/ibg-2/EN/methods_jppc/methods_node.html), and LemnaTec Scanalyzer 3D. These developments enable the phenotyping of specific organs (e.g. leaf, root, and shoot) or of whole plants. Some of them are even used for three-dimensional plant analysis (Clark et al., 2011). Consequently, several specific software applications (a comprehensive list can be found at http://www.phenomics.cn/links.php), such as HYPOTrace (Wang et al., 2009), HTPheno (Hartmann et al., 2011), LAMINA (Bylesjö et al., 2008), PhenoPhyte (Green et al., 2012), Rosette Tracker (De Vylder et al., 2012), LeafAnalyser (Weight et al., 2008), RootNav (Pound et al., 2013), SmartGrain (Tanabata et al., 2012), and LemnaGrid, were designed to extract a wide range of measurements, such as height/length, width, shape, projected area, digital volume, compactness, relative growth rate, and colorimetric analysis.The huge amount of generated image data from various phenotyping systems requires appropriate data management as well as an appropriate analytical framework for data interpretation (Fiorani and Schurr, 2013). However, most of the developed image-analysis tools are designed for a specific task, for specific plant species, or are not freely available to the research community. They lack flexibility in terms of needed adaptations to meet new analysis requirements. For example, it would be desirable that a system could handle imaging data from different sources (either from fully automated high-throughput phenotyping systems or from setups where images are acquired manually), different imaging modalities (fluorescence, near-infrared, and thermal imaging), and/or different species (wheat [Triticum aestivum], barley [Hordeum vulgare], maize [Zea mays], and Arabidopsis).In this work, we present Integrated Analysis Platform (IAP), a scalable open-source framework, for high-throughput plant phenotyping data processing. IAP handles different image sources and helps to organize phenotypic data by retaining the metadata from the input in the result data set. In order to measure phenotypic traits in new or modified setups, users can easily create new analysis pipelines or modify the predefined ones. IAP provides various user-friendly interfaces at different system levels to meet the demands of users (e.g. software developers, bioinformaticians, and biologists) with different experiences in software programming.  相似文献   
7.
Although oxidative stress has been previously described in plants exposed to uranium (U), some uncertainty remains about the role of glutathione and tocopherol availability in the different responsiveness of plants to photo-oxidative damage. Moreover, in most cases, little consideration is given to the role of water transport in shoot heavy metal accumulation. Here, we investigated the effect of uranyl nitrate exposure (50 μM) on PSII and parameters involved in water transport (leaf transpiration and aquaporin gene expression) of Arabidopsis wild type (WT) and mutant plants that are deficient in tocopherol (vte1: null α/γ-tocopherol and vte4: null α-tocopherol) and glutathione biosynthesis (high content: cad1.3 and low content: cad2.1). We show how U exposure induced photosynthetic inhibition that entailed an electron sink/source imbalance that caused PSII photoinhibition in the mutants. The WT was the only line where U did not damage PSII. The increase in energy thermal dissipation observed in all the plants exposed to U did not avoid photo-oxidative damage of mutants. The maintenance of control of glutathione and malondialdehyde contents probed to be target points for the overcoming of photoinhibition in the WT. The relationship between leaf U content and leaf transpiration confirmed the relevance of water transport in heavy metals partitioning and accumulation in leaves, with the consequent implication of susceptibility to oxidative stress.  相似文献   
8.
9.
New data have been acquired on the biology, morphological features and distribution of Norwegian (Atlantic) pollock Theragra finnmarchica in the Barents Sea. Two individuals of this rare species gadoid (Gadidae) were caught in June and July 2012 in the south-eastern part of the Barents Sea, indicating a wider distribution area of this species than previously thought. It has been confirmed that a number of morphological features of Norwegian pollock is different from T. chalcogramma, and that it feeds on macroplankton (Euphausiidae, Hyperiidae).  相似文献   
10.
Characteristics of morphology and number of melanomacrophage centers (MMCs) in the liver and spleen of the roach Rutilus rutilus and the amount of pigments in MMCs during the Haff disease outbreak and the death of fish in Lake Kotokel in relation to these parameters in the roach from Lake Baikal are described. Pathological changes in the microvasculature and parenchyma in the liver of the roach from Lake Kotokel were found. The area of melanomacrophage centers in the liver of the roach from this lake was significantly smaller, whereas the number and size of these centers in the spleen was significantly larger than in the roaches from Lake Baikal. Among the pigments studied, the strongest response to the content of this toxin in the water body was shown by hemosiderin. An increase in its amount in the spleen MMCs testifies to an enhanced degradation of erythrocytes and iron release, which may be caused by the damage of cells of the erythrocyte lineage by the toxin.  相似文献   
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