Nuclear replication activities during imbibition of abscisic acid- and gibberellin-deficient tomato (Lycopersicon esculentum Mill.) seeds

The role of cis-abscisic acid (ABA) and gibberellins (GAs) in the induction of cell-cycle activities has been studied during imbibition and subsequent germination of tomato seeds. Using flow cytometry, nuclear replication activity was investigated in embryo root tips isolated from seeds of the ABA-deficient mutant sit ^w , the GA-deficient mutant gib-1, and the wild-type (MM) tomato (Lycopersicon esculentum Mill. cv. Moneymaker) upon imbibition in water, 10 μM GA₄₊₇, 5 μM ABA or 5 μM ABA+10 μM GA₄₊₇. The nuclei of fully matured dry MM, sit ^w and gib-1 seeds predominantly showed 2C DNA signals, indicating that the cell-cycle activity of most root-tip cells had been arrested at the G₁ phase of nuclear division. However, ABA-deficient sit ^w seeds contained a significantly higher amount of G₂ cells (4C DNA) compared with the other genotypes, suggesting that, during maturation, cell-cycle activity in sit ^w seeds is less efficiently arrested in G₁. Upon imbibition in water, an induction of the 4C signal, indicating nuclear replication, was observed in the root tip cells of both MM and sit ^w embroys. The augmentation in the 4C signal occurred before visible germination. Gib-1 seeds did not show cell-cycle activity and did not germinate in water. Upon imbibition in GA₄₊₇, both cell-cycle activity and subsequent germination were enhanced in MM and sit ^w seeds, and were induced in gib-1. In ABA, the germination of MM and sit ^w seeds was inhibited while nuclear replication of these seeds was not affected. It is concluded that GA influences germination by acting upon processes that precede cell-cycle activation, while ABA affects growth by acting upon processes that follow cell-cycle activation.     

Effects of osmotic preconditioning on nuclear replication activity in seeds of pepper (Capsicum annuum)

Routing of cytosolically synthesized precursor proteins into chloroplasts is a specific process which involves a multitude of soluble and membrane components. In this review we wil1 focus on early events of the translocation pathway of nuclear coded plastidic precursor proteins and compare import routes for polypeptide of the outer chloroplast envelope to that of internal chloroplast compartments. A number of proteins housed in the chloroplast envelopes have been implied to be involved in the translocation process, but so far a certain function has not been assigned to any of these proteins. The only exception could be an envelope localized hsc 70 homologue which could retain the import competence of a precursor protein in transit into the organelle.     

Emergence of artemisinin-resistant Plasmodium falciparum with kelch13 C580Y mutations on the island of New Guinea

The rapid and aggressive spread of artemisinin-resistant Plasmodium falciparum carrying the C580Y mutation in the kelch13 gene is a growing threat to malaria elimination in Southeast Asia, but there is no evidence of their spread to other regions. We conducted cross-sectional surveys in 2016 and 2017 at two clinics in Wewak, Papua New Guinea (PNG) where we identified three infections caused by C580Y mutants among 239 genotyped clinical samples. One of these mutants exhibited the highest survival rate (6.8%) among all parasites surveyed in ring-stage survival assays (RSA) for artemisinin. Analyses of kelch13 flanking regions, and comparisons of deep sequencing data from 389 clinical samples from PNG, Indonesian Papua and Western Cambodia, suggested an independent origin of the Wewak C580Y mutation, showing that the mutants possess several distinctive genetic features. Identity by descent (IBD) showed that multiple portions of the mutants’ genomes share a common origin with parasites found in Indonesian Papua, comprising several mutations within genes previously associated with drug resistance, such as mdr1, ferredoxin, atg18 and pnp. These findings suggest that a P. falciparum lineage circulating on the island of New Guinea has gradually acquired a complex ensemble of variants, including kelch13 C580Y, which have affected the parasites’ drug sensitivity. This worrying development reinforces the need for increased surveillance of the evolving parasite populations on the island, to contain the spread of resistance.     

Identification and QTL mapping of whitefly resistance components in Solanum galapagense

Solanum galapagense is closely related to the cultivated tomato and can show a very good resistance towards whitefly. A segregating population resulting from a cross between the cultivated tomato and a whitefly resistant S. galapagense was created and used for mapping whitefly resistance and related traits, which made it possible to study the genetic basis of the resistance. Quantitative trait loci (QTL) for adult survival co-localized with type IV trichome characteristics (presence, density, gland longevity and gland size). A major QTL (Wf-1) was found for adult survival and trichome characters on Chromosome 2. This QTL explained 54.1 % of the variation in adult survival and 81.5 % of the occurrence of type IV trichomes. A minor QTL (Wf-2) for adult survival and trichome characters was identified on Chromosome 9. The major QTL was confirmed in F3 populations. Comprehensive metabolomics, based on GCMS profiling, revealed that 16 metabolites segregating in the F2 mapping population were associated with Wf-1 and/or Wf-2. Analysis of the 10 most resistant and susceptible F2 genotypes by LCMS showed that several acyl sugars were present in significantly higher concentration in the whitefly resistant genotypes, suggesting a role for these components in the resistance as well. Our results show that whitefly resistance in S. galapagense seems to inherit relatively simple compared to whitefly resistance from other sources and this offers great prospects for resistance breeding as well as elucidating the underlying molecular mechanism(s) of the resistance.     

Using dynamic relative climate impact curves to quantify the climate impact of bioenergy production systems over time

The climate impact of bioenergy is commonly quantified in terms of CO₂ equivalents, using a fixed 100‐year global warming potential as an equivalency metric. This method has been criticized for the inability to appropriately address emissions timing and the focus on a single impact metric, which may lead to inaccurate or incomplete quantification of the climate impact of bioenergy production. In this study, we introduce Dynamic Relative Climate Impact (DRCI) curves, a novel approach to visualize and quantify the climate impact of bioenergy systems over time. The DRCI approach offers the flexibility to analyze system performance for different value judgments regarding the impact category (e.g., emissions, radiative forcing, and temperature change), equivalency metric, and analytical time horizon. The DRCI curves constructed for fourteen bioenergy systems illustrate how value judgments affect the merit order of bioenergy systems, because they alter the importance of one‐time (associated with land use change emissions) versus sustained (associated with carbon debt or foregone sequestration) emission fluxes and short‐ versus long‐lived climate forcers. Best practices for bioenergy production (irrespective of value judgments) include high feedstock yields, high conversion efficiencies, and the application of carbon capture and storage. Furthermore, this study provides examples of production contexts in which the risk of land use change emissions, carbon debt, or foregone sequestration can be mitigated. For example, the risk of indirect land use change emissions can be mitigated by accompanying bioenergy production with increasing agricultural yields. Moreover, production contexts in which the counterfactual scenario yields immediate or additional climate impacts can provide significant climate benefits. This paper is accompanied by an Excel‐based calculation tool to reproduce the calculation steps outlined in this paper and construct DRCI curves for bioenergy systems of choice.     

Global solid biomass trade for energy by 2020: an assessment of potential import streams and supply costs to North-West Europe under different sustainability constraints

The expected use of solid biomass for large-scale heat and power production across North–West Europe (NW EU) has led to discussions about its sustainability, especially due to the increasing import dependence of the sector. While individual Member States and companies have put forward sustainability criteria, it remains unclear how different requirements will influence the availability and cost of solid biomass and thus how specific regions will satisfy their demand in a competitive global market. We combined a geospatially explicit least-cost biomass supply model with a linear optimization solver to assess global solid biomass trade streams by 2020 with a particular focus on NW EU. We apply different demand and supply scenarios representing varying policy developments and sustainability requirements. We find that the projected EU solid biomass demand by 2020 can be met across all scenarios, almost exclusively via domestic biomass. The exploitation of domestic agricultural residue and energy crop potentials, however, will need to increase sharply. Given sustainability requirements for solid biomass as for liquid biofuels, extra-EU imports may reach 236 PJ by 2020, i.e., 400% of their 2010 levels. Intra-EU trade is expected to grow with stricter sustainability requirements up to 548 PJ, i.e., 280% of its 2010 levels by 2020. Increasing sustainability requirements can have different effects on trade portfolios across NW EU. Excluding pulpwood pellets may drive the supply costs of import dependent countries, foremost the Netherlands and the UK, whereas excluding additional forest biomass may entail higher costs for Germany and Denmark which rely on regional biomass. Excluding solid biomass fractions may create short-term price hikes. Our modeling results are strongly influenced by parameterization choices, foremost assumed EU biomass supply volumes and costs and assumed relations between criteria and supply. The model framework is suited for the inclusion of dynamic supply–demand interactions and other world regions.     

BIOLOGICAL AVAILABILITY OF IRON TO THE FRESHWATER CYANOBACTERIUM ANABAENA FLOS‐AQUAE1

Iron acquisition from various ferric chelates and colloids was studied using iron‐limited cells of Anabaena flos‐aquae (Lyng.) Brèb UTEX 1444, a cyanobacterial strain that produces high levels of siderophores under iron limitation. Various chelators of greatly varying affinity for Fe³⁺ (HEDTA, EDDHA, desferrioxamine mesylate, HBED, 8‐hydroxyquinoline) were assayed for the degree of iron acquisition by iron‐limited cyanobacterial cells. Iron uptake rates (measured by graphite furnace atomic absorption spectrometry) varied approximately inversely with calculated [Fe³⁺] (calculated as pFe) and decreased with increasing chelator‐to‐iron ratio. No iron uptake was observed when Fe³⁺ was chelated with HBED, the strongest of the tested chelators. Iron‐limited Anabaena cells were able to take up iron from 8‐hydroxyquinoline (oxine or 8HQ), a compound sometimes used to quantify aquatic iron bioavailability. Iron bound to purified humic acid was poorly available but did support some growth at high humic acid concentrations. These results suggest that for cyanobacteria, even tightly bound iron is biologically available, including to a limited extent iron bound to humic acids. However, iron bound to some extremely strong chelators (e.g. HBED) is likely to be biologically unavailable.     

Unravelling enzymatic discoloration in potato through a combined approach of candidate genes,QTL, and expression analysis

Enzymatic discoloration (ED) of potato tubers was investigated in an attempt to unravel the underlying genetic factors. Both enzyme and substrate concentration have been reported to influence the degree of discoloration and as such this trait can be regarded as polygenic. The diploid mapping population C × E, consisting of 249 individuals, was assayed for the degree of ED and levels of chlorogenic acid and tyrosine. Using this data, Quantitative Trait Locus (QTL) analysis was performed. Three QTLs for ED have been found on parental chromosomes C3, C8, E1, and E8. For chlorogenic acid a QTL has been identified on C2 and for tyrosine levels, a QTL has been detected on C8. None of the QTLs overlap, indicating the absence of genetic correlations between these components underlying ED, in contrast to earlier reports in literature. An obvious candidate gene for the QTL for ED on Chromosome 8 is polyphenol oxidase (PPO), which was previously mapped on chromosome 8. With gene-specific primers for PPO gene POT32 a CAPS marker was developed. Three different alleles (POT32-1, -2, and -3) could be discriminated. The segregating POT32 alleles were used to map the POT32 CAPS marker and QTL analysis was redone, showing that POT32 coincides with the QTL peak. A clear correlation between allele combinations and degree of discoloration was observed. In addition, analysis of POT32 gene expression in a subset of genotypes indicated a correlation between the level of gene expression and allele composition. On average, genotypes having two copies of allele 1 had both the highest degree of discoloration as well as the highest level of POT32 gene expression.     

Focus on Metabolism: Spatially Resolved Plant Metabolomics: Some Potentials and Limitations of Laser-Ablation Electrospray Ionization Mass Spectrometry Metabolite Imaging

Laser-ablation electrospray ionization (LAESI)-mass spectrometry imaging has been applied to contrasting plant organs to assess its potential as a procedure for performing in vivo metabolomics in plants. In a proof-of-concept experiment, purple/white segmented Phalaenopsis spp. petals were first analyzed using standard liquid chromatography-mass spectrometry analyses of separate extracts made specifically from the purple and white regions. Discriminatory compounds were defined and putatively annotated. LAESI analyses were then performed on living tissues, and these metabolites were then relocalized within the LAESI-generated data sets of similar tissues. Maps were made to illustrate their locations across the petals. Results revealed that, as expected, anthocyanins always mapped to the purple regions. Certain other (nonvisible) polyphenols were observed to colocalize with the anthocyanins, whereas others were found specifically within the white tissues. In a contrasting example, control and Cladosporium fulvum-infected tomato (Solanum lycopersicum) leaves were subjected to the same procedures, and it could be observed that the alkaloid tomatine has clear heterogeneous distribution across the tomato leaf lamina. Furthermore, LAESI analyses revealed perturbations in alkaloid content following pathogen infection. These results show the clear potential of LAESI-based imaging approaches as a convenient and rapid way to perform metabolomics analyses on living tissues. However, a range of limitations and factors have also been identified that must be taken into consideration when interpreting LAESI-derived data. Such aspects deserve further evaluation before this approach can be applied in a routine manner.Plants are a tremendously rich source of a myriad of structurally and chemically diverse metabolites (Rao and Ravishankar, 2002; D’Auria and Gershenzon, 2005). Many of these metabolites have a (partly) known function in the plant, although our knowledge of the vast majority of plant secondary metabolites is still sparse, or even nonexistent (Rao and Ravishankar, 2002; D’Auria and Gershenzon, 2005; Fernie, 2007). Plant metabolites are also of considerable importance in a crop context. Indeed, most plant species that have undergone domestication have become crops specifically because they provide us with a source of chemicals. This is not only true for all of our food crops, but also for many other species of genera such as Pyrethrum (insecticides), Jasminium and Santalum (perfumes), Hevea (rubber), Nicotiana and Cannabis (drugs), Linum (oils), Artemisia and Taxus (pharmaceuticals), Cinnamomum (flavors), etc. However, despite the importance of plants as a source of exploitable and essential biochemicals, we often still have remarkably limited knowledge of the relevant biosynthetic pathways, the genetics behind the key enzymes, and indeed when, why, and where these metabolites are produced and stored within the plant in question (Fernie, 2007; Sumner et al., 2011; Kueger et al., 2012).The field of plant metabolomics has grown tremendously since its recent inception earlier this century (Fiehn et al., 2000; Fiehn, 2002). As an untargeted approach to gain a broad overview of the complexity of plant metabolic composition, the technology has, in a short time, made significant inroads into helping expand our knowledge of plant biochemistry (Kueger et al., 2012; Etalo et al., 2013; Hunerdosse and Nomura, 2014; Meret et al., 2014). Typically, rich metabolomics data sets already provide us with a valuable means to generate hypotheses relating to plant metabolism, which then become the focus of further, more direct investigation (Quanbeck et al., 2012). New technologies are being developed, and especially, new data-mining strategies are being designed to allow us to look deep into plant metabolism without having first to rely on preconceptions. However, there are significant limitations to the application of the technology, which still remain the topic of much research effort.Robust sampling approaches for plant biochemical analysis generally entail taking reliably measurable amounts of plant material that will yield detectable levels of the chemical components. Although for metabolomics analyses, samples of just 50 mg can often suffice, obtaining a reliable sample with minimum biological variation generally requires an initial pooling of materials from which a representative sample is then taken. We therefore treat plant tissue as being homogeneous, but this is clearly a gross oversimplification (Fernie, 2007). Plants have been considered to be composed of roughly 40 different cell types, and a plant organ such as a leaf will generally contain up to 15 different cell types (Martin et al., 2001). Different morphologies also parallel different biochemical composition. Even directly neighboring cells within an organ, for example, a leaf epidermis that often comprises pavement, guard, trichome, and glandular hair cells, are formed from cells already known to have distinctly different biochemistries. Making an extract, for any kind of metabolomics or standard biochemical analysis, therefore entails that we immediately lose most intercellular and intertissue resolution. However, our knowledge is growing in that, in addition to known or expected biochemical differences between cell types, metabolite accumulation across organs can be far from uniform; indeed, islands of higher and lower concentrations of particular metabolites have been observed. This is of course immediately visible when the metabolites concerned can be seen by the naked eye; anthocyanins, for example, are often found to be heterogeneously distributed across leaves, fruits, and flower petals, creating clear phenotypic patterns. The same may also be true of other compounds that are invisible to the human eye but that, in contrast, may still be detectable by insects (e.g. through their fluorescence capacity; see http://www.naturfotograf.com/UV_flowers_list.html; Gronquist et al., 2001).In an ideal situation, we would like to be able to look directly into a plant tissue and be able to analyze the biochemical composition at the single cell level. Some so-called metabolite imaging technologies, usually based on mass spectrometric detection (mass spectrometry imaging [MSI]), have recently been introduced as a step toward this optimistic goal. Included here are matrix-assisted laser desorption/ionization (MALDI)-MSI, direct analysis in real time, and desorption electrospray ionization approaches (Cody et al., 2005; Cornett et al., 2007; Ifa et al., 2010). Early examples of MALDI-MSI have shown not only how primary metabolites such as sugars can be strongly localized within plant organs (Rolletschek et al., 2011), but also how the heterogeneous distribution of glucosinolates in Arabidopsis (Arabidopsis thaliana) can potentially determine grazing behavior of caterpillars (Shroff et al., 2008). This technology continues to improve, and recent exciting developments have resulted in cellular and subcellular imaging of metabolites at a resolution of 5 to 9 µm using MALDI (Korte et al., 2015). However, some key practical limitations of MALDI-based approaches are centered around the need to initially have to pretreat/dehydrate the tissue prior to applying the required matrix solution and the requirement of applying a vacuum during the biochemical analysis. Recently, a new technology has been introduced, laser ablation electrospray ionization (LAESI), which can potentially overcome some of these limitations, given that measurements can be made on fresh, living tissue without the need for a vacuum, thus creating the potential for high-resolution in vivo metabolomics.Here, we report on a set of experiments performed to assess both the potential and limitations of using LAESI-based MSI approaches to perform metabolic mapping on living plant tissues. While identifying a number of technological challenges that still need to be tackled, we were able to show that it is possible to use LAESI-MSI to map metabolites directly onto their known location (in this case, by exploiting the visibility of anthocyanins) as well as localize invisible metabolites in the same tissue. Results have revealed that in plants, for both petal and leaf tissue, the distribution of metabolites can be highly heterogeneous, and that this heterogeneity is of potential relevance to our gaining a broader, more detailed understanding of the overall molecular organization and phenotypic features of plant tissues. Furthermore, knowledge of the nature and extent of this heterogeneity has particular relevance and importance when trying to understand how a plant functions as a system, interacting with its environment. We predict that a higher resolution understanding of plant biochemistry will lead to an increasingly discriminatory capacity in our ability to define more accurately the spatial complexity of plant molecular organization.