Oxidation of an o -tolyl phosphine complex of platinum : C-H activation and transcyclometallation

The oxidation of the tri(o-tolyl)phospine complex of the doubly cyloplatinated 2,6-di(4-fluorophenyl)pyridine ligand with the electrophilic oxidant iodobenzenedichloride was studied. Three products were formed in the ratio 15:15:70, and all were identified. The simple cis-dichloro platinum(IV) complex 2 (15%) remained in solution and could be purified and fully characterised. The triply cyclometallated 3 (15%), formed via the activation of a methyl group on a tolyl ring, precipitated from the reaction mixture and could not be redissolved or characterised further. Transcyclometallated 4 (70%), where one of the original cyclometallated aryl rings has exchanged for a cyclometallated phosphine ring, crystallised from the reaction mixture and was characterised crystallographically. Redissolution of 4 gave a new agostic species with the phosphine moving to a less sterically demanding position

An exploration of governance and accountability issues within mutual organisations: The case of UK Building Societies

This study examines the governance and accountability practices and reforms in UK building societies following the 2008 financial crisis. Theoretically, this study explores the notion of mutual accountability and governance systems in delineating the (re)structuring of UK building societies’ governance and accountability practices, in response to the crisis. Data for the study are derived from thirty-eight in-depth interviews with key stakeholders in building societies, including executives, non-executives, ex-directors, an auditor, a regulator and customers, as well as publicly available documents and non-participant observation of a number of members’ meetings. The findings of the study demonstrate that the industry’s internal, intermediate and external governance structures have significantly altered in the post-crisis era with a positive impact on the mutual accountability. Coercive pressure from regulators has led to improvements on building societies’ internal governance structures, including but not limited to board composition, internal control and risk management frameworks. Intermediate governance structure, unique to mutual organisations, is embedded within UK building societies as the fundamental mechanism in achieving democracy and mutual accountability. However, the political and economic uncertainty and regulatory reforms in the financial services sector have continued to pose challenges in the governance and long-term performance of regional building societies. Intensifying regulations have increased the costs and workload for building societies and led many of these societies to emphasize the “form” rather than true “substance” of good governance practices. There is the need for regulators and policy makers to realise the difference among building societies and to develop appropriate codes of governance and regulations which are not one-size-fits-all

Modeling Phenotypic Metabolic Adaptations of <em>Mycobacterium tuberculosis</em> H37Rv under Hypoxia

<div><p>The ability to adapt to different conditions is key for <em>Mycobacterium tuberculosis</em>, the causative agent of tuberculosis (TB), to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of <em>M. tuberculosis</em> in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an <em>in silico</em> approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of <em>M. tuberculosis</em> H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA) program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of <em>dosR</em>, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of <em>M. tuberculosis</em> activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to differential gene expression of the enzymes catalyzing the related metabolic reactions.</p> </div

Using the Variable-Nearest Neighbor Method To Identify P‑Glycoprotein Substrates and Inhibitors

Permeability glycoprotein (Pgp) is an essential membrane-bound transporter that efficiently extracts compounds from a cell. As such, it is a critical determinant of the pharmacokinetic properties of drugs. Multidrug resistance in cancer is often associated with overexpression of Pgp, which increases the efflux of chemotherapeutic agents from the cell. This, in turn, may prevent an effective treatment by reducing the effective intracellular concentrations of such agents. Consequently, identifying compounds that can either be transported out of the cell by Pgp (substrates) or impair Pgp function (inhibitors) is of great interest. Herein, using publically available data, we developed quantitative structure–activity relationship (QSAR) models of Pgp substrates and inhibitors. These models employed a variable-nearest neighbor (v-NN) method that calculated the structural similarity between molecules and hence possessed an applicability domain, that is, they used all nearest neighbors that met a minimum similarity constraint. The performance characteristics of these v-NN-based models were comparable or at times superior to those of other model constructs. The best v-NN models for identifying either Pgp substrates or inhibitors showed overall accuracies of >80% and κ values of >0.60 when tested on external data sets with candidate Pgp substrates and inhibitors. The v-NN prediction model with a well-defined applicability domain gave accurate and reliable results. The v-NN method is computationally efficient and requires no retraining of the prediction model when new assay information becomes availablean important feature when keeping QSAR models up-to-date and maintaining their performance at high levels

Schematic description of integrating a small example metabolic network with gene expression data.

<p>Construction of the altered metabolic state used gene expression data to constrain and alter the reference fluxes obtained from a metabolic network compatible with the reference condition. The example network contained six metabolites (A–F), two uptake reactions, six enzymatic reactions, and one biomass reaction. In the reference condition, the biomass function contained equal amounts of metabolites E and F, set to 1.0 millimoles per gram dry weight of the organism (mmol/gDW), and the uptake rates for the metabolites A and B were each assigned an upper limit of 2.0 mmol/(h·gDW). In Step I, we obtained the minimum and maximum fluxes under the optimal biomass production rate via flux variability analysis and calculated the average normalized flux for the reference metabolic network. In Step II, the gene expression ratios were mapped to their corresponding reactions. In Step III, we initially set constraints for reactions that were associated with altered gene expression values. These constraints were based on the normalized reference network with the biomass production rate set to one and resulted in increased normalized fluxes through reactions related to up-regulated genes (reactions A→B and D→F) and decreased fluxes related to down-regulated genes (reaction C→D). Because biological activities other than gene transcription can influence reaction fluxes, we introduced a set of non-negative slack variables (L1, L2, and L3) to account for possible violations of the constraints. In Step IV, we further performed a number of optimizations subject to the constraints from the previous step and obtained a new minimum and maximum normalized flux for each reaction. We first minimized the overall violation of the developed constraints in the form of the sum of the slack variables (highest priority). We then minimized the modifications in the biomass objective function and those in the upper limits of metabolite uptakes (medium priority), and, last, we minimized and maximized each reaction flux (lowest priority). Finally, in Step V, we constructed the new metabolic state by calculating the new average normalized flux for each reaction as the mean of its new minimum and maximum fluxes. This metabolic state was representative of the new condition and in this case was associated with altered uptake rates, pathway preferences, and an altered biomass composition.</p

Identifying Cytochrome P450 Functional Networks and Their Allosteric Regulatory Elements

<div><p>Cytochrome P450 (CYP) enzymes play key roles in drug metabolism and adverse drug-drug interactions. Despite tremendous efforts in the past decades, essential questions regarding the function and activity of CYPs remain unanswered. Here, we used a combination of sequence-based co-evolutionary analysis and structure-based anisotropic thermal diffusion (ATD) molecular dynamics simulations to detect allosteric networks of amino acid residues and characterize their biological and molecular functions. We investigated four CYP subfamilies (CYP1A, CYP2D, CYP2C, and CYP3A) that are involved in 90% of all metabolic drug transformations and identified four amino acid interaction networks associated with specific CYP functionalities, i.e., membrane binding, heme binding, catalytic activity, and dimerization. Interestingly, we did not detect any co-evolved substrate-binding network, suggesting that substrate recognition is specific for each subfamily. Analysis of the membrane binding networks revealed that different CYP proteins adopt different membrane-bound orientations, consistent with the differing substrate preference for each isoform. The catalytic networks were associated with conservation of catalytic function among CYP isoforms, whereas the dimerization network was specific to different CYP isoforms. We further applied low-temperature ATD simulations to verify proposed allosteric sites associated with the heme-binding network and their role in regulating metabolic fate. Our approach allowed for a broad characterization of CYP properties, such as membrane interactions, catalytic mechanisms, dimerization, and linking these to groups of residues that can serve as allosteric regulators. The presented combined co-evolutionary analysis and ATD simulation approach is also generally applicable to other biological systems where allostery plays a role.</p> </div

Predictions of hypoxia-induced changes in fluxes through central carbon metabolism.

<p>The left panel shows the flux ratios, i.e., the ratios of reaction fluxes under hypoxia to those under normoxia, of wild type <i>Mycobacterium tuberculosis</i> H37Rv and the right panel shows those of the Δ<i>dosR</i> deletion mutant. If the normoxic flux of a reaction was close to zero, we did not calculate the flux ratio for this reaction due to the numerical uncertainty associated with creating the corresponding ratio. The results indicated that the wild type strain activated glucose processing pathways and the predominant reaction flow was on the reductive side of the tricarboxylic acid (TCA) cycle. Conversely, the Δ<i>dosR</i> deletion mutant was not able to cope under hypoxic conditions as evident by an overall reduced activity in the TCA cycle. NAD, nicotinamide adenine dinucleotide; NADP, nicotinamide adenine dinucleotide phosphate; FAD, flavin adenine dinucleotide. NADH, NADPH, and FADH2 are the reduced forms of NAD, NADP, and FAD, respectively. NAD(P), NAD or NADP; NAD(P)H, NADH or NADPH.</p

Genes predicted to be essential for <i>Mycobacterium tuberculosis</i> H37Rv to adapt to hypoxia.

<p>Shown are the genes predicted to be nonessential under normoxia but essential under hypoxia for the wild type strain. Given the metabolic state shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002688#pcbi-1002688-g005" target="_blank">Figure 5</a>, the genes predicted to be essential for hypoxic adaptation were mostly located in the glucose/glycerol processing pathways and on the reductive side of the tricarboxylic acid (TCA) cycle. dctA, Na⁺/H⁺-dicarboxylate symporter; eno, enolase; fba, fructose-bisphosphate aldolase; frdA, frdB, frdC, frdD, fumarate reductase; gap, glyceraldehyde-3-phosphate dehydrogenase; gpm, phosphoglycerate mutase; pfKA, pfkB, phosphofructokinase; pgk, phosphoglycerate kinase; ppgK, polyphosphate glucokinase; sdhA, sdhC, sdhD, succinate dehydrogenase; tpi, triosephosphate isomerase.</p

General Purpose 2D and 3D Similarity Approach to Identify hERG Blockers

Screening compounds for human ether-à-go-go-related gene (hERG) channel inhibition is an important component of early stage drug development and assessment. In this study, we developed a high-confidence (p-value < 0.01) hERG prediction model based on a combined two-dimensional (2D) and three-dimensional (3D) modeling approach. We developed a 3D similarity conformation approach (SCA) based on examining a limited fixed number of pairwise 3D similarity scores between a query molecule and a set of known hERG blockers. By combining 3D SCA with 2D similarity ensemble approach (SEA) methods, we achieved a maximum sensitivity in hERG inhibition prediction with an accuracy not achieved by either method separately. The combined model achieved 69% sensitivity and 95% specificity on an independent external data set. Further validation showed that the model correctly picked up documented hERG inhibition or interactions among the Food and Drug Administration- approved drugs with the highest similarity scoreswith 18 of 20 correctly identified. The combination of ascertaining 2D and 3D similarity of compounds allowed us to synergistically use 2D fingerprint matching with 3D shape and chemical complementarity matching

Phenotypic characteristics of wild type <i>Mycobacterium tuberculosis</i> H37Rv and Δ<i>dosR</i> under normoxia and hypoxia.

<p>(A) The predicted normalized oxygen uptake rates of <i>Mycobacterium tuberculosis</i> H37Rv and the Δ<i>dosR</i> deletion mutant under normoxia and hypoxia. The oxygen uptake rates were normalized by each strain's biomass production rate. Supplemental <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002688#pcbi.1002688.s002" target="_blank">Table S1</a> gives the biomass production rates of each strain under different conditions, which were calculated as described in the Materials and Methods Section. We based the metabolic network models of the hypoxic state on differential gene expression data associated with the change from normoxic air to hypoxic nitrogen gas with 0.2% oxygen (1.5 mm Hg) after 2 hours <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002688#pcbi.1002688-Park1" target="_blank">[25]</a>. The wild type metabolic response involved reducing its oxygen requirement to cope with the low-oxygen stress, while the Δ<i>dosR</i> deletion mutant was not capable of adjusting. (B) The predicted ATP production levels for the same systems as in panel A showed a slight reduction for the wild type and a much larger decrease for the Δ<i>dosR</i> deletion mutant in response to hypoxia. Note that the ATP production rates were not normalized so to facilitate a direct comparison with the experimental data in Refs. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002688#pcbi.1002688-Watanabe1" target="_blank">[16]</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002688#pcbi.1002688-Leistikow1" target="_blank">[49]</a>. (C) The modeled growth characteristic of the wild type and Δ<i>dosR</i> deletion mutant were compared with the corresponding experimental data <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002688#pcbi.1002688-Leistikow1" target="_blank">[49]</a>. Following the experimental data presentation, the x-axis plots two different time intervals, 0–20 and 20–200 days, using two different time scales. The initial aerobic growth phase for the first 5 days was followed by a slight decrease in cell concentration upon switching to hypoxic conditions on day 5. Our metabolic model interpretation was also compatible with a slight decrease in cell concentration for wild type and a substantial decrease for the deletion mutant. Because the gene expression data was compatible with the immediate hypoxic response, the validity range of the metabolic model cannot be expected to capture genotypic and phenotypic adaptations beyond an initial adaptation. Here, the calculated growth reductions for the wild type and Δ<i>dosR</i> deletion mutant mimicked the experimental data up to days 60 and 12, respectively.</p