Assessing the generality of global leaf trait relationships

Global-scale quantification of relationships between plant traits gives insight into the evolution of the world's vegetation, and is crucial for parameterizing vegetation-climate models. A database was compiled, comprising data for hundreds to thousands of species for the core 'leaf economics' traits leaf lifespan, leaf mass per area, photosynthetic capacity, dark respiration, and leaf nitrogen and phosphorus concentrations, as well as leaf potassium, photosynthetic N-use efficiency (PNUE), and leaf N : P ratio. While mean trait values differed between plant functional types, the range found within groups was often larger than differences among them. Future vegetation-climate models could incorporate this knowledge. The core leaf traits were intercorrelated, both globally and within plant functional types, forming a 'leaf economics spectrum'. While these relationships are very general, they are not universal, as significant heterogeneity exists between relationships fitted to individual sites. Much, but not all, heterogeneity can be explained by variation in sample size alone. PNUE can also be considered as part of this trait spectrum, whereas leaf K and N : P ratios are only loosely related.     

Recombinant hTRBP and hPACT Modulate hAgo2-Catalyzed siRNA-Mediated Target RNA Cleavage In Vitro

The human TAR RNA-binding protein (hTRBP) and protein activator of protein kinase R (hPACT) are important players in RNA interference (RNAi). Together with hArgonaute2 (hAgo2) and hDicer they have been reported to form the RISC-loading complex (RLC). Among other functions, hTRBP was suggested to assist the loading of hAgo2 with small interfering RNAs (siRNAs) within the RLC. Although several studies have been conducted to evaluate the specific functions of hTRBP and hPACT in RNAi, exact mechanisms and modes of action are still unknown. Here, we present a biochemical study further evaluating the role of hTRBP and hPACT in hAgo2-loading. We found that both proteins enhance hAgo2-mediated RNA cleavage significantly; even a hAgo2 mutant impaired in siRNA binding shows full cleavage activity in the presence of hTRBP or hPACT. Pre-steady state binding studies reveal that the assembly of wildtype-hAgo2 (wt-hAgo2) and siRNAs remains largely unaffected, whereas the binding of mutant hAgo2-PAZ9 to siRNA is restored by adding either hTRBP or hPACT. We conclude that both proteins assist in positioning the siRNA within hAgo2 to ensure optimal binding and cleavage. Overall, our data indicate that hTRBP and hPACT are part of a regulative system of RNAi that is important for efficient target RNA cleavage.     

Metagenomic Analysis from the Interior of a Speleothem in Tjuv-Ante's Cave,Northern Sweden

Speleothems are secondary mineral deposits normally formed by water supersaturated with calcium carbonate percolating into underground caves, and are often associated with low-nutrient and mostly non-phototrophic conditions. Tjuv-Ante’s cave is a shallow-depth cave formed by the action of waves, with granite and dolerite as major components, and opal-A and calcite as part of the speleothems, making it a rare kind of cave. We generated two DNA shotgun sequencing metagenomic datasets from the interior of a speleothem from Tjuv-Ante’s cave representing areas of old and relatively recent speleothem formation. We used these datasets to perform i) an evaluation of the use of these speleothems as past biodiversity archives, ii) functional and taxonomic profiling of the speleothem’s different formation periods, and iii) taxonomic comparison of the metagenomic results to previous microscopic analyses from a nearby speleothem of the same cave. Our analyses confirm the abundance of Actinobacteria and fungi as previously reported by microscopic analyses on this cave, however we also discovered a larger biodiversity. Interestingly, we identified photosynthetic genes, as well as genes related to iron and sulphur metabolism, suggesting the presence of chemoautotrophs. Furthermore, we identified taxa and functions related to biomineralization. However, we could not confidently establish the use of this type of speleothems as biological paleoarchives due to the potential leaching from the outside of the cave and the DNA damage that we propose has been caused by the fungal chemical etching.     

Removal of Prymnesium parvum (Haptophyceae) and its toxins using clay minerals

Laboratory experiments were conducted to examine the ability of several clay minerals from Sweden to remove the fish-killing microalga, Prymnesium parvum Carter, from suspension. In their commercial form (i.e. after incineration at 400 °C), seawater slurries (salinity = 26) of the three minerals tested were generally ineffective at removing P. parvum from culture within a range of 0.01 to 0.50 g/L, and after 2.5 h of flocculation and settling. Dry bentonite (SWE1) displayed the highest removal efficiency (RE) at 17.5%, with 0.50 g/L. Illite (SWE3) averaged only 7.5% RE between 0.10 to 0.50 g/L, while kaolinite (SWE2) kept the cells suspended instead of removing them. Brief mixing of the clay-cell suspension after SWE1 addition improved RE by a factor of 2.5 (i.e. 49% at 0.50 g/L), relative to no mixing. The addition of polyaluminum chloride (PAC, at 5 ppm) to 0.50 g/L SWE1 also improved RE to 50% relative to SWE1 alone, but only minor improvements in RE were seen with SWE2 and SWE2 combined with PAC. In further experiments, P. parvum grown in NP-replete conditions were removed in greater numbers than cells in N- or P-limited cultures, at 0.10–0.25 g/L of SWE1 and 5 ppm PAC. With 0.50 g/L, RE converged at 40% for all three culture conditions. The toxin concentration of NP-replete cultures decreased from 24.2 to 9.2 μg/mL (60% toxin RE) with 0.10–0.50 g/L SWE1 treatment and 5 ppm PAC. A strong correlation was found between cell and toxin RE (r²=0.995). For N-limited cultures, toxin RE ranged between 21 and 87% with the same clay/PAC concentrations, although the correlation between cell and toxin removal was more moderate (r²=0.746) than for NP-replete conditions. Interestingly, the toxin concentration within the clay-cell pellet increased dramatically after treatment, suggesting that clay addition may stimulate toxin production in N-stressed cells. For P-limited cultures, toxin concentration also decreased following clay/PAC treatment (i.e. 36% toxin RE), but toxin removal was poorly correlated to cell removal (r²=0.462). To determine whether incineration affected SWE1’s removal ability, a sample of its wet, unprocessed form was tested. The RE of wet bentonite (SWE4) was slightly better than that of SWE1 (31% versus 17%, respectively, at 0.50 g/L), but when 5 ppm PAC was added, RE increased from 10 to 64% with 0.05 g/L of SWE4, and increased further to 77% with 0.50 g/L. There were no significant differences in RE among NP-replete, N-limited and P-limited cultures using PAC-treated SWE4. Finally, RE varied with P. parvum concentration, reaching a maximum level at the lowest cell concentration (1×10³ cells/mL): 100% RE with 0.10 and 0.50 g/L SWE4 + 5 ppm PAC. RE dropped as cell concentration increased to 1×10⁴ and 5×10⁴ cells/mL, but rose again when concentration increased to 1×10⁵ cells/mL, the concentration used routinely for the removal experiments above. Based on these results, SWE4 with PAC was the most effective mineral sample against P. parvum. Overall, these studies demonstrated that clay flocculation can be effective at removing P. parvum and its toxins only under certain treatment conditions with respect to cell concentration, clay type and concentration, and physiological status.     

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.Plants are of vital significance as a source of food (Grusak and DellaPenna, 1999; Rogalski and Carrer, 2011), feed (Lu et al., 2011), energy (Tilman et al., 2006; Parmar et al., 2011), and feedstocks for the chemical industry (Metzger and Bornscheuer, 2006; Kinghorn et al., 2011). Given the close connection between plant metabolism and the usability of plant products, there is a growing interest in understanding and predicting the behavior and regulation of plant metabolic processes. In order to increase crop quality and yield, there is a need for methods guiding the rational redesign of the plant metabolic network (Schwender, 2009).Mathematical modeling of plant metabolism offers new approaches to understand, predict, and modify complex plant metabolic processes. In plant research, the issue of metabolic modeling is constantly gaining attention, and different modeling approaches applied to plant metabolism exist, ranging from highly detailed quantitative to less complex qualitative approaches (for review, see Giersch, 2000; Morgan and Rhodes, 2002; Poolman et al., 2004; Rios-Estepa and Lange, 2007).A widely used modeling approach is flux balance analysis (FBA), which allows the prediction of metabolic capabilities and steady-state fluxes under different environmental and genetic backgrounds using (non)linear optimization (Orth et al., 2010). Assuming steady-state conditions, FBA has the advantage of not requiring the knowledge of kinetic parameters and, therefore, can be applied to model detailed, large-scale systems. In recent years, the FBA approach has been applied to several different plant species, such as maize (Zea mays; Dal’Molin et al., 2010; Saha et al., 2011), barley (Hordeum vulgare; Grafahrend-Belau et al., 2009b; Melkus et al., 2011; Rolletschek et al., 2011), rice (Oryza sativa; Lakshmanan et al., 2013), Arabidopsis (Arabidopsis thaliana; Poolman et al., 2009; de Oliveira Dal’Molin et al., 2010; Radrich et al., 2010; Williams et al., 2010; Mintz-Oron et al., 2012; Cheung et al., 2013), and rapeseed (Brassica napus; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011), as well as algae (Boyle and Morgan, 2009; Cogne et al., 2011; Dal’Molin et al., 2011) and photoautotrophic bacteria (Knoop et al., 2010; Montagud et al., 2010; Boyle and Morgan, 2011). These models have been used to study different aspects of metabolism, including the prediction of optimal metabolic yields and energy efficiencies (Dal’Molin et al., 2010; Boyle and Morgan, 2011), changes in flux under different environmental and genetic backgrounds (Grafahrend-Belau et al., 2009b; Dal’Molin et al., 2010; Melkus et al., 2011), and nonintuitive metabolic pathways that merit subsequent experimental investigations (Poolman et al., 2009; Knoop et al., 2010; Rolletschek et al., 2011). Although FBA of plant metabolic models was shown to be capable of reproducing experimentally determined flux distributions (Williams et al., 2010; Hay and Schwender, 2011b) and generating new insights into metabolic behavior, capacities, and efficiencies (Sweetlove and Ratcliffe, 2011), challenges remain to advance the utility and predictive power of the models.Given that many plant metabolic functions are based on interactions between different subcellular compartments, cell types, tissues, and organs, the reconstruction of organ-specific models and the integration of these models into interacting multiorgan and/or whole-plant models is a prerequisite to get insight into complex plant metabolic processes organized on a whole-plant scale (e.g. source-sink interactions). Almost all FBA models of plant metabolism are restricted to one cell type (Boyle and Morgan, 2009; Knoop et al., 2010; Montagud et al., 2010; Cogne et al., 2011; Dal’Molin et al., 2011), one tissue or one organ (Grafahrend-Belau et al., 2009b; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011; Mintz-Oron et al., 2012), and only one model exists taking into account the interaction between two cell types by specifying the interaction between mesophyll and bundle sheath cells in C4 photosynthesis (Dal’Molin et al., 2010). So far, no model representing metabolism at the whole-plant scale exists.Considering whole-plant metabolism raises the problem of taking into account temporal and environmental changes in metabolism during plant development and growth. Although classical static FBA is unable to predict the dynamics of metabolic processes, as the network analysis is based on steady-state solutions, time-dependent processes can be taken into account by extending the classical static FBA to a dynamic flux balance analysis (dFBA), as proposed by Mahadevan et al. (2002). The static (SOA) and dynamic optimization approaches introduced in this work provide a framework for analyzing the transience of metabolism by integrating kinetic expressions to dynamically constrain exchange fluxes. Due to the requirement of knowing or estimating a large number of kinetic parameters, so far dFBA has only been applied to a plant metabolic model once, to study the photosynthetic metabolism in the chloroplasts of C3 plants by a simplified model of five biochemical reactions (Luo et al., 2009). Integrating a dynamic model into a static FBA model is an alternative approach to perform dFBA.In this study, a multiscale metabolic modeling (MMM) approach was applied with the aim of achieving a spatiotemporal resolution of cereal crop plant metabolism. To provide a framework for the in silico analysis of the metabolic dynamics of barley on a whole-plant scale, the MMM approach integrates a static multiorgan FBA model and a dynamic whole-plant multiscale functional plant model (FPM) to perform dFBA. The performance of the novel whole-plant MMM approach was tested by studying source-sink interactions during the seed developmental phase of barley plants.     

Evaluating the effectiveness of retention forestry to enhance biodiversity in production forests of Central Europe using an interdisciplinary,multi‐scale approach

Retention forestry, which retains a portion of the original stand at the time of harvesting to maintain continuity of structural and compositional diversity, has been originally developed to mitigate the impacts of clear‐cutting. Retention of habitat trees and deadwood has since become common practice also in continuous‐cover forests of Central Europe. While the use of retention in these forests is plausible, the evidence base for its application is lacking, trade‐offs have not been quantified, it is not clear what support it receives from forest owners and other stakeholders and how it is best integrated into forest management practices. The Research Training Group ConFoBi (Conservation of Forest Biodiversity in Multiple‐use Landscapes of Central Europe) focusses on the effectiveness of retention forestry, combining ecological studies on forest biodiversity with social and economic studies of biodiversity conservation across multiple spatial scales. The aim of ConFoBi is to assess whether and how structural retention measures are appropriate for the conservation of forest biodiversity in uneven‐aged and selectively harvested continuous‐cover forests of temperate Europe. The study design is based on a pool of 135 plots (1 ha) distributed along gradients of forest connectivity and structure. The main objectives are (a) to investigate the effects of structural elements and landscape context on multiple taxa, including different trophic and functional groups, to evaluate the effectiveness of retention practices for biodiversity conservation; (b) to analyze how forest biodiversity conservation is perceived and practiced, and what costs and benefits it creates; and (c) to identify how biodiversity conservation can be effectively integrated in multi‐functional forest management. ConFoBi will quantify retention levels required across the landscape, as well as the socio‐economic prerequisites for their implementation by forest owners and managers. ConFoBi's research results will provide an evidence base for integrating biodiversity conservation into forest management in temperate forests.