Enhancing the structural diversity between forest patches—A concept and real-world experiment to study biodiversity,multifunctionality and forest resilience across spatial scales

Intensification of land use by humans has led to a homogenization of landscapes and decreasing resilience of ecosystems globally due to a loss of biodiversity, including the majority of forests. Biodiversity–ecosystem functioning (BEF) research has provided compelling evidence for a positive effect of biodiversity on ecosystem functions and services at the local (α-diversity) scale, but we largely lack empirical evidence on how the loss of between-patch β-diversity affects biodiversity and multifunctionality at the landscape scale (γ-diversity). Here, we present a novel concept and experimental framework for elucidating BEF patterns at α-, β-, and γ-scales in real landscapes at a forest management-relevant scale. We examine this framework using 22 temperate broadleaf production forests, dominated by Fagus sylvatica. In 11 of these forests, we manipulated the structure between forest patches by increasing variation in canopy cover and deadwood. We hypothesized that an increase in landscape heterogeneity would enhance the β-diversity of different trophic levels, as well as the β-functionality of various ecosystem functions. We will develop a new statistical framework for BEF studies extending across scales and incorporating biodiversity measures from taxonomic to functional to phylogenetic diversity using Hill numbers. We will further expand the Hill number concept to multifunctionality allowing the decomposition of γ-multifunctionality into α- and β-components. Combining this analytic framework with our experimental data will allow us to test how an increase in between patch heterogeneity affects biodiversity and multifunctionality across spatial scales and trophic levels to help inform and improve forest resilience under climate change. Such an integrative concept for biodiversity and functionality, including spatial scales and multiple aspects of diversity and multifunctionality as well as physical and environmental structure in forests, will go far beyond the current widely applied approach in forestry to increase resilience of future forests through the manipulation of tree species composition.     

New determinants of firing rates and patterns of vasopressinergic magnocellular neurons: predictions using a mathematical model of osmodetection

Arginine vasopressin (AVP), one of the most important hormones involved in hydromineral homeostasis, is secreted by hypothalamic magnocellular neurons (MCNs). Here, we implemented two critical parameters for MCN physiology into a Hodgkin-Huxley simulation of the MCN. By incorporating the mechanosensitive channel (MSC) responsible for osmodetection and the synaptic inputs whose frequencies are modulated by changes in ambient osmolality into our model, we were able to develop an improved model with increased physiological relevance and gain new insight into the determinants of the firing patterns of AVP magnocellular neurons. Our results with this MCN model predict that 1) a single MCN is able to display all the firing patterns experimentally observed: silent, irregular, phasic and continuous firing patterns; 2) under conditions of hyperosmolality, burst durations are regulated by the frequency-dependent fatigue of dynorphin secretion; and 3) the transitions between firing patterns are controlled by EPSP and IPSP frequencies (0, 2, 4, 8, 16, 32, 64 and 128 Hz). Moreover, this simulation predicts that EPSPs and IPSPs do not modify the spiking frequency (SF) of phasic firing patterns (0.0034 Hz/Hz [EPSP]; 0.0012 Hz/Hz [IPSP]). Rather, these afferents strongly regulate SF during irregular and continuous firing patterns (0.075 Hz/Hz [EPSP]; 0.027 Hz/Hz [IPSP]). The use of the realistic MCN model developed here allows for an improved understanding of the determinants driving the firing patterns and spiking frequencies of vasopressinergic magnocellular neurons.     

Development of quantitative metabolomics for <Emphasis Type="Italic">Pichia pastoris</Emphasis>

Accurate, reliable and reproducible measurement of intracellular metabolite levels has become important for metabolic studies of microbial cell factories. A first critical step for metabolomic studies is the establishment of an adequate quenching and washing protocol, which ensures effective arrest of all metabolic activity and removal of extracellular metabolites, without causing leakage of metabolites from the cells. Five different procedures based on cold methanol quenching and cell separation by filtration were tested for metabolomics of Pichia pastoris regarding methanol content and temperature of the quenching solution as key parameters. Quantitative evaluation of these protocols was carried out through mass balance analysis, based on metabolite measurements in all sample fractions, those are whole broth, quenched and washed cells, culture filtrate and quenching and washing solution. Finally, the optimal method was used to study the time profiles of free amino acid and central carbon metabolism intermediates in glucose-limited chemostat cultures. Acceptable recoveries (>90%) were obtained for all quenching procedures tested. However, quenching at −27°C in 60% v/v methanol performed slightly better in terms of leakage minimization. We could demonstrate that five residence times under glucose limitation are enough to reach stable intracellular metabolite pools. Moreover, when comparing P. pastoris and S. cerevisiae metabolomes, under the same cultivation conditions, similar metabolite fingerprints were found in both yeasts, except for the lower glycolysis, where the levels of these metabolites in P. pastoris suggested an enzymatic capacity limitation in that part of the metabolism.     

Leakage-free rapid quenching technique for yeast metabolomics

Accurate determination of intracellular metabolite levels requires reliable, reproducible techniques for sampling and sample treatment. Quenching in 60% (v/v) methanol at −40°C is currently the standard method for sub-second arrest of metabolic activity in microbial metabolomics but there have been contradictory reports in the literature on whether leakage of metabolites from the cells occurs. We have re-evaluated this method in S. cerevisiae using a comprehensive, strictly quantitative approach. By determining the levels of a large range of metabolites in different sample fractions and establishing mass balances we could trace their fate during the quenching procedure and confirm that leakage of metabolites from yeast cells does occur during conventional cold methanol quenching, to such an extent that the levels of most metabolites have been previously underestimated by at least twofold. In addition, we found that the extent of leakage depends on the time of exposure, the temperature and the properties of the methanol solutions. Using the mass balance approach we could study the effect of different quenching conditions and demonstrate that leakage can be entirely prevented by quenching in pure methanol at ≤−40°C, which we propose as a new improved method. Making use of improved data on intracellular metabolite levels we also re-evaluated the need of sub-second quenching of metabolic activity and of removing the extracellular medium. Our findings have serious implications for quantitative metabolomics-based fields such as non-stationary ¹³C flux analysis, in vivo kinetic modeling and thermodynamic network analysis.

Aip1p Dynamics Are Altered by the R256H Mutation in Actin

Mutations in actin cause a range of human diseases due to specific molecular changes that often alter cytoskeletal function. In this study, imaging of fluorescently tagged proteins using total internal fluorescence (TIRF) microscopy is used to visualize and quantify changes in cytoskeletal dynamics. TIRF microscopy and the use of fluorescent tags also allows for quantification of the changes in cytoskeletal dynamics caused by mutations in actin. Using this technique, quantification of cytoskeletal function in live cells valuably complements in vitro studies of protein function. As an example, missense mutations affecting the actin residue R256 have been identified in three human actin isoforms suggesting this amino acid plays an important role in regulatory interactions. The effects of the actin mutation R256H on cytoskeletal movements were studied using the yeast model. The protein, Aip1, which is known to assist cofilin in actin depolymerization, was tagged with green fluorescent protein (GFP) at the N-terminus and tracked in vivo using TIRF microscopy. The rate of Aip1p movement in both wild type and mutant strains was quantified. In cells expressing R256H mutant actin, Aip1p motion is restricted and the rate of movement is nearly half the speed measured in wild type cells (0.88 ± 0.30 μm/sec in R256H cells compared to 1.60 ± 0.42 μm/sec in wild type cells, p < 0.005).     

Quantitative prediction of vasopressin secretion using a computational population model of rat magnocellular neurons

The goal of this study was to create a realistic and quantitative simulation of vasopressin (AVP) secretion under iso-osmotic and short-term challenged plasma osmolality. The relationship between AVP concentration ([AVP]) and plasma osmolality was computed using a sophisticated and integrated model that chronologically simulates (1) the overall firing rate of the hypothalamus’ magnocellular neuronal (MCN) population, (2) the propagation of the spike activity down the axons, (3) the fatigue and facilitation mechanisms of AVP release at the axon terminals and (4) the [AVP] pharmacodynamics based on the trains of AVP release. This global simulation predicted that the differential MCN sensitivity to dynorphin would be the most critical mechanism underlying the individual variability of MCN firing behaviors (silence, irregular, phasic and continuous firing patterns). However, at the level of the MCN population, the simulation predicted that the dynorphin factor must be combined with the distribution of the resting membrane potentials among the MCNs to obtain a realistic overall firing rate in response to a change in osmolality. Moreover, taking advantage of the integrated model, the simulation predicted that the selective removal of the frequency-dependent facilitation of AVP secretion has a major impact on the overall [AVP]-to-osmolality relationship (mean absolute change of 2.59?pg/ml); the action potential propagation failure, while critical, has a smaller quantitative impact on the overall [AVP] (0.58?pg/ml). The present integrated model (from a single MCN to a quantitative plasma [AVP]) improves our knowledge of the mechanisms underlying overall MCN firing and AVP excitation-secretion coupling.