Free energy calculations show that acidic P1 variants undergo large pKa shifts upon binding to trypsin

Serine proteinases and their protein inhibitors belong to one of the most comprehensively studied models of protein-protein interactions. It is well established that the narrow trypsin specificity is caused by the presence of a negatively charged aspartate at the specificity pocket. X-ray crystallography as well as association measurements revealed, surprisingly, that BPTI with glutamatic acid as the primary binding (P1) residue was able to bind to trypsin. Previous free energy calculations showed that there was a substantially unfavorable binding free energy associated with accommodation of ionized P1 Glu at the S1-site of trypsin. In this study, the binding of P1 Glu to trypsin has been systematically investigated in terms of the protonation states of P1 Glu and Asp189, the orientation of Gln192, as well as the possible presence of counterions using the linear interaction energy (LIE) approach and the free energy perturbation (FEP) method. Twenty-four conceivable binding arrangements were evaluated and quantitative agreement with experiments is obtained when the P1 Glu binds in its protonated from. The results suggest that P1 Glu is one of the variants of BPTI that inhibit trypsin strongest at low pH, contrary to the specificity profile of trypsin, suggesting a new regulation mechanism of trypsin-like enzymes.     

Spatial separation of litter decomposition and mycorrhizal nitrogen uptake in a boreal forest

Our understanding of how saprotrophic and mycorrhizal fungi interact to re-circulate carbon and nutrients from plant litter and soil organic matter is limited by poor understanding of their spatiotemporal dynamics. In order to investigate how different functional groups of fungi contribute to carbon and nitrogen cycling at different stages of decomposition, we studied changes in fungal community composition along vertical profiles through a Pinus sylvestris forest soil. We combined molecular identification methods with 14C dating of the organic matter, analyses of carbon:nitrogen (C:N) ratios and 15N natural abundance measurements. Saprotrophic fungi were primarily confined to relatively recently (< 4 yr) shed litter components on the surface of the forest floor, where organic carbon was mineralized while nitrogen was retained. Mycorrhizal fungi dominated in the underlying, more decomposed litter and humus, where they apparently mobilized N and made it available to their host plants. Our observations show that the degrading and nutrient-mobilizing components of the fungal community are spatially separated. This has important implications for biogeochemical studies of boreal forest ecosystems.     

End Stage Renal Disease Predicts Increased Risk of Death in First Degree Relatives in the Norwegian Population

BackgroundIncreased risk of end stage renal disease (ESRD) and death in Norwegian living kidney donors has been reported, most of the donors were related to the recipient. The present study investigates risk of death in first degree relatives of ESRD patients.MethodsThe Norwegian Population Registry, The Norwegian Cause of Death Registry and the Norwegian Renal Registry were linked. All citizens born in Norway, alive in 1960 and with at least one registered first degree relative were included; individuals who died during the first year of life were excluded. A cohort-design was used, ESRD in a first degree relative was the main exposure variable and death and causes of death were the main outcome variables. Cox regression statistics were used to investigate mortality risks.Results5 130 600 individuals were included, 27 508 had at least one first degree relative with ESRD. 828 022 died during follow-up, of whom 4105 had a first degree relative with ESRD. Adjusted hazard ratio (aHR) for death was 1.13 (1.09–1.16) in individuals with a relative with ESRD compared to those without a relative with ESRD. Excluding known hereditary renal disease, aHR decreased to 1.12 (1.09–1.15). Cardiovascular death aHR was 1.15 (1.10–1.21), of which cerebrovascular death 1.34 (1.22–1.50). aHR for death due to non-hereditary renal/ureteric disease was 2.29 (1.81–2.91) with renal failure 1.80 (1.26–2.56) and glomerular disease 5.69 (3.88–8.34) as main contributors. Diabetes mellitus death aHR was 1.68 (1.35–2.10). Absolute mortality risks increased most for the oldest cohorts with excess mortality of 148 per 100.000 person years for the cohort born 1920–39 and 218 for the cohort born 1900–1919.ConclusionsESRD in first degree relatives was associated with increased hazard ratio for death. Death due to cardiovascular disease, renal disease and diabetes mellitus increased the most.     

Environmental stability affects phenotypic evolution in a globally distributed marine picoplankton

Marine phytoplankton can evolve rapidly when confronted with aspects of climate change because of their large population sizes and fast generation times. Despite this, the importance of environment fluctuations, a key feature of climate change, has received little attention—selection experiments with marine phytoplankton are usually carried out in stable environments and use single or few representatives of a species, genus or functional group. Here we investigate whether and by how much environmental fluctuations contribute to changes in ecologically important phytoplankton traits such as C:N ratios and cell size, and test the variability of changes in these traits within the globally distributed species Ostreococcus. We have evolved 16 physiologically distinct lineages of Ostreococcus at stable high CO₂ (1031±87 μatm CO₂, SH) and fluctuating high CO₂ (1012±244 μatm CO₂, FH) for 400 generations. We find that although both fluctuation and high CO₂ drive evolution, FH-evolved lineages are smaller, have reduced C:N ratios and respond more strongly to further increases in CO₂ than do SH-evolved lineages. This indicates that environmental fluctuations are an important factor to consider when predicting how the characteristics of future phytoplankton populations will have an impact on biogeochemical cycles and higher trophic levels in marine food webs.     

Light-induced spectral absorbance changes in relation to photosynthesis and the epoxidation state of xanthophyll cycle components in cotton leaves

When cotton (Gossypium hirsutum L., cv Acaia SJC-1) leaves kept in weak light were suddenly exposed to strong red actinic light a spectral absorbance change took place having the following prominent characteristics. (a) It was irreversible within the first four minute period after darkening. (b) The difference in leaf absorbance between illuminated and predarkened leaves had a major peak at 505 nanometers, a minor peak at 465 nanometers, a shoulder around 515 nanometers, and minor troughs at 455 and 480 nanometers. (c) On the basis of its spectral and kinetic characteristics this absorbance change can be readily distinguished from the much faster electrochromic shift which has a peak at 515 nanometers, from the slow, so-called light-scattering change which has a broad peak centered around 535 nanometers and is reversed upon darkening, and from absorbance changes associated with light-induced chloroplast rearrangements. (d) The extent and time course of this absorbance change closely matched that of the deepoxidation of violaxanthin to zeaxanthin in the same leaves. (e) Both the absorbance change and the ability to form zeaxanthin were completely blocked in leaves to which dithiothreitol (DTT) had been provided through the cut petlole. DTT treatment also caused strong inhibition of that component of the 535-nanometer absorbance change which is reversed in less than 4 minutes upon darkening and considered to be caused by increased light scattering. Moreover, DTT inhibited a large part of nonphotochemical quenching of chlorophyll fluorescence in the presence of excessive light. However, DTT had no detectable effect on the photon yield of photosynthesis measured under strictly rate-limiting photon flux densities or on the light-saturated photosynthetic capacity, at least in the short term. We conclude that it is possible to monitor light-induced violaxanthin de-epoxidation in green intact leaves by measurement of the absorbance change at 505 nanometers. Determination of absorbance changes in conjunction with measurements of photosynthesis in the presence and absence of DTT provide a system well suited for future studies of meachanisms of dissipation of excessive excitation energy in intact leaves.     

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.     

Superiority of radiotelemetry over the tail-cuff method in evaluating control and drug-induced data in cardiovascular functions

The aim of the study was to compare an often-used method to measure blood pressure (BP) using a tail-cuff (TC) device, with radiotelemetry (RT) which allows to sample data on heart rate (HR) and BP in freely moving rodents without any restraint in behaviour. Data were collected in male normotensive Wistar-Kyoto rats and in spontaneously hypertensive rats. Experiments were performed under 12:12 h light–dark conditions (lights on at 07:00 h) with simulated dawn and dusk for 45 min. Experiments were performed at 08:00–10:00 h (rest phase) and at 20:00–22:00 h (activity phase) under control conditions and after treatment with the beta-adrenoceptor blocker metoprolol (8 mg/kg). For TC, the Harvard BP Monitoring System (Edenbrigde, England) and for RT radio transmitters (Dataquest IV system, TA11PA-C40, DSI, St. Paul, Minnesota, USA) were used. Rats bearing the TC device were also monitored under RT. The experiments show that TC significantly increased HR and both systolic and diastolic BP in both strains and both at L and D. Metoprolol reduced TC-induced HR but left BP increase uneffected. The study shows that RT is the method of choice to monitor BP and HR in rodents, TC is not suitable.