Atomistic Simulations of Calcium Uranyl(VI) Carbonate Adsorption on Calcite and Stepped-Calcite Surfaces

Adsorption of actinyl ions onto mineral surfaces is one of the main mechanisms that control the migration of these ions in environmental systems. Here, we present computational classical molecular dynamics (MD) simulations to investigate the behavior of U­(VI) in contact with different calcite surfaces. The calcium-uranyl-carbonate [Ca₂UO₂(CO₃)₃] species is shown to display both inner- and outer-sphere adsorption to the flat {101̅4} and the stepped {314̅8} and {31̅2̅16} planes of calcite. Free energy calculations, using the umbrella sampling method, are employed to simulate adsorption paths of the same uranyl species on the different calcite surfaces under aqueous condition. Outer-sphere adsorption is found to dominate over inner-sphere adsorption because of the high free energy barrier of removing a uranyl–carbonate interaction and replacing it with a new uranyl–surface interaction. An important binding mode is proposed involving a single vicinal water monolayer between the surface and the sorbed complex. From the free energy profiles of the different calcite surfaces, the uranyl complex was also found to adsorb preferentially on the acute-stepped {314̅8} face of calcite, in agreement with experiment

The Development of a Classical Force Field To Determine the Selectivity of an Aqueous Fe³⁺–EDA Complex for TcO₄^– and SO₄^2–

A classical force field has been developed in order to investigate the selective exchange of oxyanions (TcO₄^– vs SO₄^2–) with other ligands (H₂O, Cl^–) to an aqueous Fe³⁺–ethylenediamine (EDA) complex. Potentials of mean force for a range of exchange reactions were generated using umbrella sampling and classical molecular dynamics simulations in order to calculate the affinity of each oxyanion for the Fe³⁺–EDA complex in aqueous solution. In order to accurately introduce a degree of specificity for the interaction of Fe³⁺ with each ligand type, force field parameters were tuned to match the results of density functional theory calculations. Preferential exchange of H₂O, Cl^–, and SO₄^2– for TcO₄^– via an interchange mechanism is observed, in agreement with experimental observations. Both the relative solvation entropies and enthalpies of the anions were found to be critically important factors governing the magnitude of the observed selectivities. These results have important implications for the design and modeling of functionalized materials for the remediation of land contaminated with radioactive ⁹⁹Tc

Mouse spleen colonization of <i>Salmonella</i> mutants with metabolic defects.

<p>The data represent competitive indices (CI) of mutants vs. wildtype <i>Salmonella</i> in spleen of individual mice at three (open symbols) or four days (filled symbols) post infection (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301.s009" target="_blank">Table S3</a>). A log₂(CI) value of 0 (equivalent to a CI of 1) represents full virulence. Down triangles represent mutants with utilization defects, up triangles represent auxotrophic mutants. Grey symbols represent data from a previous study <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301-Becker1" target="_blank">[34]</a> obtained in the same disease model. Red triangles represent data from an independently reconstructed <i>glpFK gldA glpT ugpB</i> mutant. The data provided evidence for access to a number of host nutrient which are shown in black (for detailed interpretation see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301.s011" target="_blank">Table S5</a>). Nutrients with apparently low availability are shown in grey. Statistical analysis was carried out with the Benjamini-Hochberg false discovery rate (FDR) approach for multiple comparisons <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301-Benjamini1" target="_blank">[45]</a> (***, FDR<0.001; **, FDR<0.01; *, FDR<0.05).</p

Large-scale experimental data are consistent with computational model predictions.

<p><b>A</b>) Validation of mutant phenotype predictions. The colors show the predicted gene relevance for spleen colonization (red, essential; orange, contributing; blue, non-detectable; see text for definitions). Comparison of model predictions with 738 experimental <i>Salmonella</i> mutant phenotypes revealed 92% prediction accuracy (inner dark colors) but also 61 discrepancies (pale outer colors). Numbers (correct/total number of experimentally validated predictions) are also given. <b>B</b>) Potential reasons for inaccurate phenotype predictions (redu, unrealistic redundancy; biom, incomplete biomass/maintenance issues; part, partially contributing functions; toxic, accumulation of toxic upstream metabolites; gap, missing enzyme; or exp, possibly inaccurate experimental data). For detailed descriptions see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301.s016" target="_blank">Table S10</a>. <b>C</b>) Detection of enzymes with predicted differential relevance for optimal <i>Salmonella</i> in vivo growth. Enzyme relevance was classified by parsimonious enzyme usage flux-balance analysis (pFBA) (ess, essential enzymes; optima, enzymes predicted to be used for optimal in vivo growth; ELE, enzymatically less efficient enzymes that will increase flux if used; MLE, metabolically less efficient enzymes that will impair growth rate if used; zeroFlux, enzymes that cannot be not used in vivo). Filled bars represent enzymes that were detected by <i>Salmonella</i> ex vivo proteomics, open bars represent enzymes that were not detected. Statistical significance of the relationship between enzyme classes and the proportion of detected proteins was determined using the Chi square trend test. <b>D</b>) Feasibility of predicted reaction rates. For each reaction, the range of flux rates compatible with full <i>Salmonella</i> growth was determined using Flux-Variability Analysis. The circles represent the most economical state with minimal total flux (see text). Predicted reaction rates are compared to corresponding catalytic capacities calculated form experimental enzyme abundance and turnover numbers (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301.s008" target="_blank">Table S2</a>). The reddish area represents infeasible fluxes. Reactions with substantial infeasible fluxes in the most economic simulated state are labeled (1, formyltetrahydrofolate dehydrogenase; 2, phosphoserine aminotransferase; 3, glycerol dehydrogenase). <b>E</b>) Predicted flux ranges and corresponding catalytic capacities after constraining all reactions to feasible fluxes (except for the three aminoacyl tRNA ligations mentioned in the text). <b>F</b>) Relative flux ranges of the initial unrestrained (straight line) and the enzyme capacity-restrained (dotted line) models. For each reaction, the flux range was divided by the respective flux value in the most economical state. Reactions that carried no flux in the most economical state were not considered. Statistical significance of the difference between both distributions was tested using the Mann-Whitney U test.</p

CCS values of {Cr₆} horseshoe clusters for a manuscript in preparation "Self-Association and Dissociation of {Cr₆}_n Horseshoe Clusters: Insights from Ion Mobility Mass Spectrometry"

CCS values of {Cr6} horseshoe clusters for a manuscript in preparation "Self-Association and Dissociation of {Cr6}n Horseshoe Clusters: Insights from Ion Mobility Mass Spectrometry"</p

Nutrient utilization capabilities of <i>Salmonella</i> in infected mouse tissues.

<p>Colored names represent transporters and enzymes that were detected in <i>Salmonella</i> purified from mouse spleen (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301.s007" target="_blank">Table S1</a>). The color shows enzyme abundance in copies per <i>Salmonella</i> cell. Grey proteins were not detected. Arrows represent metabolic reactions. Transport reactions are labeled with cylinders. Arrow colors show maximal catalytic capacities calculated from enzyme abundance and reported turnover numbers (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301.s008" target="_blank">Table S2</a>). Grey arrows represent reactions, for which enzymes were not detected and/or turnover numbers were unavailable. Tsx is an outer membrane general nucleoside channel; NupC is a high affinity transporter for all nucleosides except guanosine and deoxyguanosine. An interactive map with detailed description of all detected metabolic capabilities is available at <a href="http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html" target="_blank">http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html</a>.</p

A common nutritional pattern for mammalian pathogens.

<p><b>A</b>) Presence of 254 nutrient utilization pathways in genomes of 153 mammalian pathogens (excluding all <i>Salmonella</i> serovars). Data were based on pathway annotations available in MetaCyc <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301-Caspi1" target="_blank">[62]</a>. Degradation pathways for nutrients that support <i>Salmonella</i> in mouse spleen were highly overrepresented among pathogen genomes (<i>P</i><0.001; Mann-Whitney U test) suggesting similar nutritional preferences (filled circles; 1, purine nucleosides; 2, pyrimidine nucleosides; 3, fatty acids; 4, glycerol; 5, arginine; 6, N-Acetylglucosamine; 7, glucose; 8, gluconate). <b>B</b>) Depletion frequency of 118 biosynthesis pathways in mammalian pathogens. The values represent differences in pathway frequency in sets of 153 pathogens and 316 environmental bacteria (see text for explanation). Biosynthesis pathways for biomass components that <i>Salmonella</i> could obtain from the host were selectively depleted among pathogen genomes (<i>P</i><0.0001; Mann-Whitney U test) suggesting similar host supplementation patterns (filled circles; 1, tyrosine; 2, histidine; 3, arginine; 4, cysteine; 5, methionine; 6, tryptophan; 7, threonine; 8, valine; 9 leucine; 10, isoleucine; 11, proline; 12, pyridoxal; 13, purine nucleosides; 14, pyrimidine nucleosides; 15, glutamine; 16, thiamin; 17, pantothenate).</p

A quantitative genome-scale model of <i>Salmonella</i> nutrition, metabolism, and growth in infected mouse spleen.

<p>This schematic map shows available host nutrients, their respective uptake rates represented by color and font size, and their conversion to new <i>Salmonella</i> biomass through the <i>Salmonella</i> metabolic network (see text and <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301.s012" target="_blank">Tables S6</a>, <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301.s013" target="_blank">S7</a>, <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301.s014" target="_blank">S8</a>, <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301.s015" target="_blank">S9</a> for detailed explanation and quantitative values). Symbols represent metabolites (squares, carbohydrates; pointing up triangles, amino acids; vertical ellipses, purines; horizontal ellipses, pyrimidines; pointing down triangles, cofactors; tees, tRNAs; circles, other metabolites; filled symbols, phosphorylated metabolites) and proteins (diamonds). The connecting lines present metabolic reactions. The brown lines represent the inner and outer membranes. An interactive map with detailed annotation of all reactions and the computational model in SBML format are available at <a href="http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html" target="_blank">http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html</a>. The model is also available in the supporting information (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003301#ppat.1003301.s019" target="_blank">Model S1</a>).</p

Nutrient limitation of intracellular <i>Salmonella</i> growth.

<p><b>A</b>) Schematic representation of external supplementation of intracellular <i>Salmonella</i> (red) in infected macrophages (grey). <b>B</b>) Increasing external nutrient availability accelerates intracellular <i>Salmonella</i> growth, and this depends on specific <i>Salmonella</i> nutrient utilization capabilities (open symbols, 0.5 g l⁻¹ glucose; filled black symbols, 1 g l⁻¹ glucose; filled grey symbols, 0.5 g l⁻¹ glucose 0.5 g l⁻¹ mannitol; circles, wildtype <i>Salmonella</i>; upward triangles, <i>Salmonella ptsG manX galP mglB</i>, deficient for high-affinity glucose transport; downward triangles, <i>Salmonella mtlAD</i>, deficient for high-affinity mannitol transport and degradation). Colony-forming units (CFU) at 10 h post infection for triplicate wells containing 300’000 RAW 264.7 cells are shown. <b>C</b>) Flux-balance analysis of nutrient excess scenarios. The computational model was set to incorporate various amounts of excess nutrients (beyond what was needed for cell maintenance and growth). Model parameters were adjusted to yield predictions that were consistent with experimental mutant and wildtype colonization data. Simulation of up to 18% nutrient excess was possible but required unrealistically high maintenance costs (shown in multiples of maintenance costs for axenic conditions). Simulated scenarios with nutrient excess beyond 18% were incompatible with experimental colonization data.</p

Adduct Ions as Diagnostic Probes of Metallosupramolecular Complexes Using Ion Mobility Mass Spectrometry

Following electrospray ionization, it is common for analytes to enter the gas phase accompanied by a charge-carrying ion, and in most cases, this addition is required to enable detection in the mass spectrometer. These small charge carriers may not be influential in solution but can markedly tune the analyte properties in the gas phase. Therefore, measuring their relative influence on the target molecule can assist our understanding of the structure and stability of the analyte. As the formed adducts are usually distinguishable by their mass, differences in the behavior of the analyte resulting from these added species (e.g., structure, stability, and conformational dynamics) can be easily extracted. Here, we use ion mobility mass spectrometry, supported by density functional theory, to investigate how charge carriers (H+, Na+, K+, and Cs+) as well as water influence the disassembly, stability, and conformational landscape of the homometallic ring [Cr8F8(O2CtBu)16] and the heterometallic rotaxanes [NH2RR′][Cr7MF8(O2CtBu)16], where M = MnII, FeII, CoII, NiII, CuII, ZnII, and CdII. The results yield new insights on their disassembly mechanisms and support previously reported trends in cavity size and transition metal properties, demonstrating the potential of adduct ion studies for characterizing metallosupramolecular complexes in general