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1. Anthropogenic changes in the global climate are shifting the potential ranges of many plant species. 2. Changing climates will allow some species the opportunity to expand their range, others may experience a contraction in their potential range, while the current and future ranges of some species may not overlap. Our capacity to generalize about the threat these range shifts pose to plant diversity is limited by many sources of uncertainty. 3. In this paper we summarize sources of uncertainty for migration forecasts and suggest a research protocol for making forecasts in the context of uncertainty.
Prokaryotic solute binding protein-dependent ATP-binding cassette import systems are divided into type land type II and mechanistic differences in the transport process going along with this classification are under intensive investigation. Little is known about the conformational dynamics during the catalytic cycle especially concerning the transmembrane domains. The type I transporter for positively charged amino acids from Salmonella enterica serovar Typhimurium (1A0-Hi5QMP2) was studied by limited proteolysis in detergent solution in the absence and presence of co-factors including ATP, ADP, LAO/arginine, and Mg2+ ions. Stable peptide fragments could be obtained and differentially susceptible cleavage sites were determined by mass spectrometry as Lys-258 in the nucleotide-binding subunit, HisP, and Arg-217/Arg-218 in the transmembrane subunit, HisQ In contrast, transmembrane subunit HisM was gradually degraded but no stable fragment could be detected. HisP and HisQ were equally resistant under pre- and post-hydrolysis conditions in the presence of arginine-loaded solute-binding protein LAO and ATP/ADP. Some protection was also observed with LAO/arginine alone, thus reflecting binding to the transporter in the apo-state and transmembrane signaling. Comparable digestion patterns were obtained with the transporter reconstituted into proteoliposomes and nanodiscs. Fluorescence lifetime spectroscopy confirmed the change of HisQ(R218) to a more apolar microenvironment upon ATP binding and hydrolysis. Limited proteolysis was subsequently used as a tool to study the consequences of mutations on the transport cycle. Together, our data suggest similar conformational changes during the transport cycle as described for the maltose ABC transporter of Escherichia coli, despite distinct structural differences between both systems.
Highly porous poly(ether imide) (PEI) microparticles prepared by a spraying/coagulation process are discussed as candidate adsorber materials for apheresis applications, i.e. removal of uremic toxins from the blood of renal failure patients. PEI particles obtained by the aforementioned procedure can have a broad size distribution with particle diameters ranging from 20 to 800 mu m. In order to further estimate the adsorption behavior of PEI microparticles packed in application relevant apheresis modules, a quantitative information about the relation between particle size and pore morphology is required. In this study, we explored whether the intraparticle porosity of PEI microparticles varies with altering the diameter of the particulate adsorbers. By an analytical wet sieving procedure, the obtained PEI microparticles were separated into five size fractions, which were analyzed by mercury intrusion porosimetry, nitrogen adsorption, and scanning electron microscopy. Mercury intrusion porosimetry revealed for all size fractions high porosity values in the range from 78% to 84% with pore diameters in the range from 10 to 1000nm. A bimodal pore size distribution was found having a first peak at around 100nm, while a second pronounced peak maximum was found at higher pore sizes that increased with raising particle diameter from 300nm for the smallest particle size fraction (50-100 mu m) to 700nm for particles with a diameter of 200 to 250 mu m. Based on these findings, it can be assumed that the main PEI particle size fraction (200-250 mu m) should exhibit the highest adsorption capacity in an apheresis module. Copyright (c) 2016 John Wiley & Sons, Ltd.
Mathematical models of bacterial growth have been successfully applied to study the relationship between antibiotic drug exposure and the antibacterial effect. Since these models typically lack a representation of cellular processes and cell physiology, the mechanistic integration of drug action is not possible on the cellular level. The cellular mechanisms of drug action, however, are particularly relevant for the prediction, analysis and understanding of interactions between antibiotics. Interactions are also studied experimentally, however, a lacking consent on the experimental protocol hinders direct comparison of results. As a consequence, contradictory classifications as additive, synergistic or antagonistic are reported in literature.
In the present thesis we developed a novel mathematical model for bacterial growth that integrates cell-level processes into the population growth level. The scope of the model is to predict bacterial growth under antimicrobial perturbation by multiple antibiotics in vitro.
To this end, we combined cell-level data from literature with population growth data for Bacillus subtilis, Escherichia coli and Staphylococcus aureus. The cell-level data described growth-determining characteristics of a reference cell, including the ribosomal concentration and efficiency. The population growth data comprised extensive time-kill curves for clinically relevant antibiotics (tetracycline, chloramphenicol, vancomycin, meropenem, linezolid, including dual combinations).
The new cell-level approach allowed for the first time to simultaneously describe single and combined effects of the aforementioned antibiotics for different experimental protocols, in particular different growth phases (lag and exponential phase). Consideration of ribosomal dynamics and persisting sub-populations explained the decreased potency of linezolid on cultures in the lag phase compared to exponential phase cultures. The model captured growth rate dependent killing and auto-inhibition of meropenem and - also for vancomycin exposure - regrowth of the bacterial cultures due to adaptive resistance development. Stochastic interaction surface analysis demonstrated the pronounced antagonism between meropenem and linezolid to be robust against variation in the growth phase and pharmacodynamic endpoint definition, but sensitive to a change in the experimental duration.
Furthermore, the developed approach included a detailed representation of the bacterial cell-cycle. We used this representation to describe septation dynamics during the transition of a bacterial culture from the exponential to stationary growth phase. Resulting from a new mechanistic understanding of transition processes, we explained the lag time between the increase in cell number and bacterial biomass during the transition from the lag to exponential growth phase. Furthermore, our model reproduces the increased intracellular RNA mass fraction during long term exposure of bacteria to chloramphenicol.
In summary, we contribute a new approach to disentangle the impact of drug effects, assay readout and experimental protocol on antibiotic interactions. In the absence of a consensus on the corresponding experimental protocols, this disentanglement is key to translate information between heterogeneous experiments and also ultimately to the clinical setting.