Besnoitia besnoiti protein disulfide isomerase (BbPDI): molecular characterization, expression and in silico modelling

Besnoitia besnoiti is an apicomplexan parasite responsible for bovine besnoitiosis, a disease with a high prevalence in tropical and subtropical regions and re-emerging in Europe. Despite the great economical losses associated with besnoitiosis, this disease has been underestimated and poorly studied, and neither an effective therapy nor an efficacious vaccine is available. Protein disulfide isomerase (PDI) is an essential enzyme for the acquisition of the correct three-dimensional structure of proteins. Current evidence suggests that in Neosporacaninum and Toxoplasmagondii, which are closely related to B. besnoiti, PDI play an important role in host cell invasion, is a relevant target for the host immune response, and represents a promising drug target and/or vaccine candidate. In this work, we present the nucleotide sequence of the B. besnoiti PDI gene. BbPDI belongs to the thioredoxin-like superfamily (cluster 00388) and is included in the PDI_a family (cluster defined cd02961) and the PDI_a_PDI_a'_c subfamily (cd02995). A 3D theoretical model was built by comparative homology using Swiss-Model server, using as a template the crystallographic deduced model of Tapasin-ERp57 (PDB code 3F8U chain C). Analysis of the phylogenetic tree for PDI within the phylum apicomplexa reinforces the close relationship among B. besnoiti, N. caninum and T. gondii. When subjected to a PDI-assay based on the polymerisation of reduced insulin, recombinant BbPDI expressed in E. coli exhibited enzymatic activity, which was inhibited by bacitracin. Antiserum directed against recombinant BbPDI reacted with PDI in Western blots and by immunofluorescence with B. besnoiti tachyzoites and bradyzoites.

Stereoselective virtual screening of the ZINC database using atom pair 3D-fingerprints

Background Tools to explore large compound databases in search for analogs of query molecules provide a strategically important support in drug discovery to help identify available analogs of any given reference or hit compound by ligand based virtual screening (LBVS). We recently showed that large databases can be formatted for very fast searching with various 2D-fingerprints using the city-block distance as similarity measure, in particular a 2D-atom pair fingerprint (APfp) and the related category extended atom pair fingerprint (Xfp) which efficiently encode molecular shape and pharmacophores, but do not perceive stereochemistry. Here we investigated related 3D-atom pair fingerprints to enable rapid stereoselective searches in the ZINC database (23.2 million 3D structures). Results Molecular fingerprints counting atom pairs at increasing through-space distance intervals were designed using either all atoms (16-bit 3DAPfp) or different atom categories (80-bit 3DXfp). These 3D-fingerprints retrieved molecular shape and pharmacophore analogs (defined by OpenEye ROCS scoring functions) of 110,000 compounds from the Cambridge Structural Database with equal or better accuracy than the 2D-fingerprints APfp and Xfp, and showed comparable performance in recovering actives from decoys in the DUD database. LBVS by 3DXfp or 3DAPfp similarity was stereoselective and gave very different analogs when starting from different diastereomers of the same chiral drug. Results were also different from LBVS with the parent 2D-fingerprints Xfp or APfp. 3D- and 2D-fingerprints also gave very different results in LBVS of folded molecules where through-space distances between atom pairs are much shorter than topological distances. Conclusions 3DAPfp and 3DXfp are suitable for stereoselective searches for shape and pharmacophore analogs of query molecules in large databases. Web-browsers for searching ZINC by 3DAPfp and 3DXfp similarity are accessible at www.gdb.unibe.ch webcite and should provide useful assistance to drug discovery projects.

Effect of Mutation and Genetic Background on Drug Resistance in Mycobacterium tuberculosis

Bacterial factors may contribute to the global emergence and spread of drug-resistant tuberculosis (TB). Only a few studies have reported on the interactions between different bacterial factors. We studied drug-resistant Mycobacterium tuberculosis isolates from a nationwide study conducted from 2000 to 2008 in Switzerland. We determined quantitative drug resistance levels of first-line drugs by using Bactec MGIT-960 and drug resistance genotypes by sequencing the hot-spot regions of the relevant genes. We determined recent transmission by molecular methods and collected clinical data. Overall, we analyzed 158 isolates that were resistant to isoniazid, rifampin, or ethambutol, 48 (30.4%) of which were multidrug resistant. Among 154 isoniazid-resistant strains, katG mutations were associated with high-level and inhA promoter mutations with low-level drug resistance. Only katG(S315T) (65.6% of all isoniazid-resistant strains) and inhA promoter -15C/T (22.7%) were found in molecular clusters. M. tuberculosis lineage 2 (includes Beijing genotype) was associated with any drug resistance (adjusted odds ratio [OR], 3.0; 95% confidence interval [CI], 1.7 to 5.6; P < 0.0001). Lineage 1 was associated with inhA promoter -15C/T mutations (OR, 6.4; 95% CI, 2.0 to 20.7; P = 0.002). We found that the genetic strain background influences the level of isoniazid resistance conveyed by particular mutations (interaction tests of drug resistance mutations across all lineages; P < 0.0001). In conclusion, M. tuberculosis drug resistance mutations were associated with various levels of drug resistance and transmission, and M. tuberculosis lineages were associated with particular drug resistance-conferring mutations and phenotypic drug resistance. Our study also supports a role for epistatic interactions between different drug resistance mutations and strain genetic backgrounds in M. tuberculosis drug resistance.

Effects of drilling and stress release on hydraulic properties and porewater chemistry of crystalline rocks

The experimental verification of matrix diffusion in crystalline rocks largely relies on indirect methods performed in the laboratory. Such methods are prone to perturbations of the rock samples by collection and preparation and therefore the laboratory-derived transport properties and fluid composition might not represent in situ conditions. We investigated the effects induced by the drilling process and natural rock stress release by mass balance considerations and sensitivity analysis of analytical out-diffusion data obtained from originally saturated, large-sized drillcore material from two locations drilled using traced drilling fluid. For in situ stress-released drillcores of quartz-monzodiorite composition from the Aspo HRL, Sweden, tracer mass balance considerations and 1D and 2D diffusion modelling consistently indicated a contamination of <1% of the original pore water. This chemically disturbed zone extends to a maximum of 0.1 mm into the drillcore (61.8 mm x 180.1 mm) corresponding to about 0.66% of the total pore volume (0.77 vol.%). In contrast, the combined effects of stress release and the drilling process, which have influenced granodioritic drillcore material from 560 m below surface at Forsmark. Sweden, resulted in a maximum contamination of the derived porewater Cl(-) concentration of about 8%. The mechanically disturbed zone with modified diffusion properties covers the outermost similar to 6 mm of the drillcore (50 mm x 189 mm), whereas the chemically disturbed zone extends to a maximum of 0.3 mm based on mass balance considerations, and to 0.15 mm to 0.2 mm into the drillcore based on fitting the observed tracer data. This corresponds to a maximum of 2.4% of the total pore volume (0.62 vol.%) being affected by the drilling-fluid contamination. The proportion of rock volume affected initially by drilling fluid or subsequently with experiment water during the laboratory diffusion and re-saturation experiments depends on the size of the drillcore material and will become larger the smaller the sample used for the experiment. The results are further in support of matrix diffusion taking place in the undisturbed matrix of crystalline rocks at least in the cm range.

Ketoprofen in piglets: enantioselective pharmacokinetics, pharmacodynamics and PK/PD modelling

The chiral pharmacokinetics and pharmacodynamics of ketoprofen were investigated in a placebo-controlled study in piglets after intramuscular administration of 6 mg/kg racemic ketoprofen. The absorption half-lives of both enantiomers were short, and S-ketoprofen predominated over R-ketoprofen in plasma. A kaolin-induced inflammation model was used to evaluate the anti-inflammatory, antipyretic and analgesic effects of ketoprofen. Skin temperatures increased after the kaolin injection, but the effect of ketoprofen was small. No significant antipyretic effects could be detected, but body temperatures tended to be lower in the ketoprofen-treated piglets. Mechanical nociceptive threshold testing was used to evaluate the analgesic effects. The piglets in the ketoprofen-treated group had significantly higher mechanical nociceptive thresholds compared to the piglets in the placebo group for 12-24 h following the treatment. Pharmacokinetic/pharmacodynamic modelling of the results from the mechanical nociceptive threshold testing gave a median IC(50) for S-ketoprofen of 26.7 mug/mL and an IC(50) for R-ketoprofen of 1.6 mug/mL. This indicates that R-ketoprofen is a more potent analgesic than S-ketoprofen in piglets. Estimated ED(50) for racemic ketoprofen was 2.5 mg/kg.

Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study

OBJECTIVES Mortality in patients starting antiretroviral therapy (ART) is higher in Malawi and Zambia than in South Africa. We examined whether different monitoring of ART (viral load [VL] in South Africa and CD4 count in Malawi and Zambia) could explain this mortality difference. DESIGN Mathematical modelling study based on data from ART programmes. METHODS We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years. In baseline scenario A all parameters were identical between strategies except for more timely and complete detection of treatment failure with VL monitoring. Additional scenarios introduced delays in switching to second-line ART (scenario B) or higher virologic failure rates (due to worse adherence) when monitoring was based on CD4 counts only (scenario C). Results are presented as relative risks (RR) with 95% prediction intervals and percent of observed mortality difference explained. RESULTS RRs comparing VL with CD4 cell count monitoring were 0.94 (0.74-1.03) in scenario A, 0.94 (0.77-1.02) with delayed switching (scenario B) and 0.80 (0.44-1.07) when assuming a 3-times higher rate of failure (scenario C). The observed mortality at 3 years was 10.9% in Malawi and Zambia and 8.6% in South Africa (absolute difference 2.3%). The percentage of the mortality difference explained by VL monitoring ranged from 4% (scenario A) to 32% (scenarios B and C combined, assuming a 3-times higher failure rate). Eleven percent was explained by non-HIV related mortality. CONCLUSIONS VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates.

Kernels and Designs for Modelling Invariant Functions: From Group Invariance to Additivity

We focus on kernels incorporating different kinds of prior knowledge on functions to be approximated by Kriging. A recent result on random fields with paths invariant under a group action is generalised to combinations of composition operators, and a characterisation of kernels leading to random fields with additive paths is obtained as a corollary. A discussion follows on some implications on design of experiments, and it is shown in the case of additive kernels that the so-called class of “axis designs” outperforms Latin hypercubes in terms of the IMSE criterion.

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

The Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP) was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH4) emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO2) forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013). Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO2 concentration). The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH4 fluxes from wetland systems. Even though modelling of wetland extent and CH4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models.

Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP)

Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH4). Increased wetland CH4 emissions could act as a positive feedback to future warming. The Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large-scale wetland characteristics and corresponding CH4 emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO2) forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO2 concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prognostically, while other models relied on remotely sensed inundation datasets, or an approach intermediate between the two. Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH4 emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH4 emissions, but large variation between the models remains. For annual global CH4 emissions, the models vary by ±40% of the all-model mean (190 Tg CH4 yr−1). Second, all models show a strong positive response to increased atmospheric CO2 concentrations (857 ppm) in both CH4 emissions and wetland area. In response to increasing global temperatures (+3.4 °C globally spatially uniform), on average, the models decreased wetland area and CH4 fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH4 fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatial scale comparable to model grid cells (commonly 0.5°). This limitation severely restricts our ability to model global wetland CH4 emissions with confidence. Our simulated wetland extents are also difficult to evaluate due to extensive disagreements between wetland mapping and remotely sensed inundation datasets. Fourth, the large range in predicted CH4 emission rates leads to the conclusion that there is both substantial parameter and structural uncertainty in large-scale CH4 emission models, even after uncertainties in wetland areas are accounted for.