Whole-genome sequencing analysis reveals the spread of a vanB-carrying transposon among different vancomycin-resistant Enterococcus faecium clinical isolates in a non-endemic setting.

BACKGROUND: Vancomycin-resistant enterococci (VRE), particularly Enterococcus faecium (VREfm), can cause serious nosocomial infections, and have been responsible for healthcare-associated outbreaks. Spreading of VREfm can occur both clonally and by the dissemination of mobile genetic elements. AIM: To report prospective analysis of whole-genome sequencing (WGS) data, including both core-genome multi-locus sequence typing (cgMLST) and transposon analysis, during a vanB VREfm outbreak. METHODS: Screening for vanB-positive VREfm isolates was performed by real-time polymerase chain reaction (PCR) on an overnight enriched broth and, if positive, subculture was performed. vanB-positive VREfm isolates underwent WGS. Generated data were used for molecular typing that was performed by cgMLST using SeqSphere. For transposon characterization, sequence data were mapped against the reference sequence of transposon Tn1549 using CLC Genomics Workbench, or de-novo assemblies were used for BLASTN comparisons. RESULTS: In total, 1358 real-time PCRs were performed. Two hundred and fifty-one specimens from 207 patients tested positive on PCR for vanB, of which 13 specimens obtained from six patients were identified as vanB VREfm positive on culture. These six patients harboured seven unique isolates belonging to four cluster types: CT118 (N=2), CT2483 (N=3), CT2500 (N=1) and CT2501 (N=1). Transposon analysis revealed the presence of an identical vanB-carrying transposon in the isolates cultured from all six patients that could be linked based on epidemiological data. CONCLUSION: A vanB VREfm outbreak occurred in the study hospital, including six patients with isolates belonging to four cluster types. In-depth transposon analysis revealed that dissemination of transposon Tn1549 rather than clonal spread was the cause of the outbreak.

On the increase of predictive performance with high-level data fusion.

The combination of the different data sources for classification purposes, also called data fusion, can be done at different levels: low-level, i.e. concatenating data matrices, medium-level, i.e. concatenating data matrices after feature selection and high-level, i.e. combining model outputs. In this paper the predictive performance of high-level data fusion is investigated. Partial least squares is used on each of the data sets and dummy variables representing the classes are used as response variables. Based on the estimated responses y(j) for data set j and class k, a Gaussian distribution p(g(k)|y(j)) is fitted. A simulation study is performed that shows the theoretical performance of high-level data fusion for two classes and two data sets. Within group correlations of the predicted responses of the two models and differences between the predictive ability of each of the separate models and the fused models are studied. Results show that the error rate is always less than or equal to the best performing subset and can theoretically approach zero. Negative within group correlations always improve the predictive performance. However, if the data sets have a joint basis, as with metabolomics data, this is not likely to happen. For equally performing individual classifiers the best results are expected for small within group correlations. Fusion of a non-predictive classifier with a classifier that exhibits discriminative ability lead to increased predictive performance if the within group correlations are strong. An example with real life data shows the applicability of the simulation results.

Dynamic metabolomic data analysis: a tutorial review.

In metabolomics, time-resolved, dynamic or temporal data is more and more collected. The number of methods to analyze such data, however, is very limited and in most cases the dynamic nature of the data is not even taken into account. This paper reviews current methods in use for analyzing dynamic metabolomic data. Moreover, some methods from other fields of science that may be of use to analyze such dynamic metabolomics data are described in some detail. The methods are put in a general framework after providing a formal definition on what constitutes a 'dynamic' method. Some of the methods are illustrated with real-life metabolomics examples.

Matrix correlations for high-dimensional data: the modified RV-coefficient.

MOTIVATION: Modern functional genomics generates high-dimensional datasets. It is often convenient to have a single simple number characterizing the relationship between pairs of such high-dimensional datasets in a comprehensive way. Matrix correlations are such numbers and are appealing since they can be interpreted in the same way as Pearson's correlations familiar to biologists. The high-dimensionality of functional genomics data is, however, problematic for existing matrix correlations. The motivation of this article is 2-fold: (i) we introduce the idea of matrix correlations to the bioinformatics community and (ii) we give an improvement of the most promising matrix correlation coefficient (the RV-coefficient) circumventing the problems of high-dimensional data. RESULTS: The modified RV-coefficient can be used in high-dimensional data analysis studies as an easy measure of common information of two datasets. This is shown by theoretical arguments, simulations and applications to two real-life examples from functional genomics, i.e. a transcriptomics and metabolomics example. AVAILABILITY: The Matlab m-files of the methods presented can be downloaded from http://www.bdagroup.nl.

Cross-validation of component models: a critical look at current methods.

In regression, cross-validation is an effective and popular approach that is used to decide, for example, the number of underlying features, and to estimate the average prediction error. The basic principle of cross-validation is to leave out part of the data, build a model, and then predict the left-out samples. While such an approach can also be envisioned for component models such as principal component analysis (PCA), most current implementations do not comply with the essential requirement that the predictions should be independent of the entity being predicted. Further, these methods have not been properly reviewed in the literature. In this paper, we review the most commonly used generic PCA cross-validation schemes and assess how well they work in various scenarios.

Examples of NIR based real time release in tablet manufacturing.

Real time release (RTR) of products is a new paradigm in the pharmaceutical industry. An RTR system assures that when the last manufacturing step is passed all the final release criteria are met. Various types of models can be used within the RTR framework. For each RTR system, the monitoring capability, control capability and RTR capability need to be tested. This paper presents some practical examples within the RTR framework using near-infrared and process data obtained from a tablet manufacturing process.

Simple assessment of homogeneity in pharmaceutical mixing processes using a near-infrared reflectance probe and control charts.

Determination of homogeneous mixing of the active pharmaceutical ingredient (API) is an important in-process control within the manufacturing of solid dosage forms. In this paper two new near-infrared (NIR) based methods were presented; a qualitative and a quantitative method. Both methods are based on the calculation of net analyte signal (NAS) models which were very easy to develop, specific with respect to the API and required no additional reference analysis. Using a well-mixed batch as a 'golden standard' batch, control charts were developed and used for monitoring the homogeneity of other batches with NIR. The methods were fast, easy to use, non-destructive and provided statistical tests of homogeneity. A mixing study was characterized with the two methods and the methods were validated by comparison with traditional HPLC analysis.

Net analyte signal based statistical quality control.

Net analyte signal statistical quality control (NAS-SQC) is a new methodology to perform multivariate product quality monitoring based on the net analyte signal approach. The main advantage of NAS-SQC is that the systematic variation in the product due to the analyte (or property) of interest is separated from the remaining systematic variation due to all other compounds in the matrix. This enhances the ability to flag products out of statistical control. Using control charts, the analyte content, variation of other compounds, and residual variation can be monitored. As an example, NAS-SQC is used to appreciate the control content uniformity of a commercially available pharmaceutical tablet product measured with near-infrared spectroscopy. Using the NAS chart, the active pharmaceutical ingredient (API) content is easily monitored for new tablets. However, since quality is a multivariate property, other quality parameters of the tablets are also monitored simultaneously. It will be demonstrated that, besides the API content, the water content of the tablets as well as the homogeneity of the other compounds is monitored.

Classification of highly similar crude oils using data sets from comprehensive two-dimensional gas chromatography and multivariate techniques.

Comprehensive two-dimensional gas chromatography (GCxGC) has proven to be an extremely powerful separation technique for the analysis of complex volatile mixtures. This separation power can be used to discriminate between highly similar samples. In this article we will describe the use of GCxGC for the discrimination of crude oils from different reservoirs within one oil field. These highly complex chromatograms contain about 6000 individual, quantified components. Unfortunately, small differences in most of these 6000 components characterize the difference between these reservoirs. For this reason, multivariate-analysis (MVA) techniques are required for finding chemical profiles describing the differences between the reservoirs. Unfortunately, such methods cannot discern between 'informative variables', or peaks describing differences between samples, and 'uninformative variables', or peaks not describing relevant differences. For this reason, variable selection techniques are required. A selection based on information between duplicate measurements was used. With this information, 292 peaks were used for building a discrimination model. Validation was performed using the ratio of the sum of distances between groups and the sum of distances within groups. This step resulted in the detection of an outlier, which could be traced to a production problem, which could be explained retrospectively.

New indicator for optimal preprocessing and wavelength selection of near-infrared spectra.

Preprocessing of near-infrared spectra to remove unwanted, i.e., non-related spectral variation and selection of informative wavelengths is considered to be a crucial step prior to the construction of a quantitative calibration model. The standard methodology when comparing various preprocessing techniques and selecting different wavelengths is to compare prediction statistics computed with an independent set of data not used to make the actual calibration model. When the errors of reference value are large, no such values are available at all, or only a limited number of samples are available, other methods exist to evaluate the preprocessing method and wavelength selection. In this work we present a new indicator (SE) that only requires blank sample spectra, i.e., spectra of samples that are mixtures of the interfering constituents (everything except the analyte), a pure analyte spectrum, or alternatively, a sample spectrum where the analyte is present. The indicator is based on computing the net analyte signal of the analyte and the total error, i.e., instrumental noise and bias. By comparing the indicator values when different preprocessing techniques and wavelength selections are applied to the spectra, the optimal preprocessing technique and the optimal wavelength selection can be determined without knowledge of reference values, i.e., it minimizes the non-related spectral variation. The SE indicator is compared to two other indicators that also use net analyte signal computations. To demonstrate the feasibility of the SE indicator, two near-infrared spectral data sets from the pharmaceutical industry were used, i.e., diffuse reflectance spectra of powder samples and transmission spectra of tablets. Especially in pharmaceutical spectroscopic applications, it is expected beforehand that the non-related spectral variation is rather large and it is important to remove it. The indicator gave excellent results with respect to wavelength selection and optimal preprocessing. The SE indicator performs better than the two other indicators, and it is also applicable to other situations where the Beer-Lambert law is valid.

Multiblock PLS analysis of an industrial pharmaceutical process.

The performance of an industrial pharmaceutical process (production of an active pharmaceutical ingredient by fermentation, API) was modeled by multiblock partial least squares (MBPLS). The most important process stages are inoculum production and API production fermentation. Thirty batches (runs) were produced according to an experimental planning. Rather than merging all these data into a single block of independent variables (as in ordinary PLS), four data blocks were used separately (manipulated and quality variables for each process stage). With the multiblock approach it was possible to calculate weights and scores for each independent block. It was found that the inoculum quality variables were highly correlated with API production for nominal fermentations. For the nonnominal fermentations, the manipulations of the fermentation stage explained the amount of API obtained (especially the pH and biomass concentration). Based on the above process analysis it was possible to select a smaller set of variables with which a new model was built. The amount of variance predicted of the final API concentration (cross-validation) for this model was 82.4%. The advantage of the multiblock model over the standard PLS model is that the contributions of the two main process stages to the API volumetric productivity were determined.

A multiway 3D QSAR analysis of a series of (S)-N-[(1-ethyl-2-pyrrolidinyl)methyl]-6-methoxybenzamides.

Recently, the multilinear PLS algorithm was presented by Bro and later implemented as a regression method in 3D QSAR by Nilsson et al. In the present article a well-known set of (S)-N-[(1-ethyl-2-pyrrolidinyl)methyl]-6-methoxybenzamides, with affinity towards the dopamine D2 receptor subtype, was utilised for the validation of the multilinear PLS method. After exhaustive conformational analyses on the ligands, the active analogue approach was employed to align them in their presumed pharmacologically active conformations, using (-)-piquindone as a template. Descriptors were then generated in the GRID program, and 40 calibration compounds and 18 test compounds were selected by means of a principal component analysis in the descriptor space. The final model was validated with different types of cross-validation experiments, e.g. leave-one-out, leave-three-out and leave-five-out. The cross-validated Q2 was 62% for all experiments, confirming the stability of the model. The prediction of the test set with a predicted Q2 of 62% also established the predictive ability. Finally, the conformations and the alignment of the ligands in combination with multilinear PLS, obviously, played an important role for the success of our model.

Influence of temperature on vibrational spectra and consequences for the predictive ability of multivariate models.

Temperature, pressure, viscosity, and other process variables fluctuate during an industrial process. When vibrational spectra are measured on- or in-line for process analytical and control purposes, the fluctuations influence the shape of the spectra in a nonlinear manner. The influence of these temperature-induced spectral variations on the predictive ability of multivariate calibration model is assessed. Short-wave NIR spectra of ethanol/water/2-propanol mixtures are taken at different temperatures, and different local and global partial least-squares calibration strategies are applied. The resulting prediction errors and sensitivity vectors of a test set are compared. For data with no temperature variation, the local models perform best with high sensitivity but the knowledge of the temperature for prediction measurements cannot aid in the improvement of local model predictions when temperature variation is introduced. The prediction errors of global models are considerably lower when temperature variation is present in the data set but at the expense of sensitivity. To be able to build temperature-stable calibration models with high sensitivity, a way of explicitly modeling the temperature should be found.

The suicide substrate reaction between plasminogen activator inhibitor 1 and thrombin is regulated by the cofactors vitronectin and heparin.

The interaction of thrombin with plasminogen activator inhibitor 1 (PAI-1) is shown to result in the simultaneous formation of both cleaved PAI-1 and a sodium dodecyl sulfate-stable thrombin-PAI-1 complex. The kinetics of this reaction can be described by a "suicide substrate" mechanism that includes a branched reaction pathway, which terminates in either the stable inhibitor-enzyme complex or the cleaved inhibitor plus free enzyme. Because of the branched pathway, approximately three moles of PAI-1 are needed to completely inhibit one mole of thrombin. Heparin and vitronectin enhance the rate of inhibition from 9.8 x 10(2) L mol(-1) s(-1) to 6.2 x 10(4) L mol(-1) s(-1) and 2.1 x 10(5) L mol(-1) s(-1), respectively, under optimal conditions. In addition to enhancing the rate of inhibition, both cofactors increase the apparent stoichiometry of the PAI-1-thrombin interaction, with cofactor concentration dependencies similar to the inhibition reaction. Thus, at 37 degrees C approximately six cleavage reactions occur per inhibition reaction. Therefore, thrombin will efficiently inactivate PAI-1 in the presence of either vitronectin or heparin, unless a sufficient excess of the inhibitor is present. These results show that physiological cofactors are able to switch a protease-serpin inhibition reaction to a substrate reaction, depending on the local concentrations of each of the components.

GRID/GOLPE 3D quantitative structure-activity relationship study on a set of benzamides and naphthamides, with affinity for the dopamine D3 receptor subtype.

In the search for drugs against schizophrenia and depression without extrapyramidal side effects, compounds that selectively antagonize the dopamine D3 receptor subtype are thought to be a solution. In order to create a model with which the D3 activity can be predicted and that can generate new ideas for future synthesis, we performed a comparative molecular field analysis (CoMFA). In our model 30 ligands were described quantitatively in the GRID program, and the model was optimized by selecting only the most informative variables in the GOLPE program. We found the predictive ability of the model to increase significantly when the number of variables was reduced from 25110 to 784. A Q2 of 0.65 was obtained with the final model, confirming the predictive ability of the model. By studying the PLS coefficients in informative 3D contour plots, ideas for the synthesis of new compounds can be generated.

The composition of complexes between plasminogen activator inhibitor 1, vitronectin and either thrombin or tissue-type plasminogen activator.

Vitronectin (VN) is an obligatory cofactor for the inhibition of thrombin by plasminogen activator inhibitor 1 (PAI-1). It accelerates the rate of association between thrombin and PAI-1 more than two orders of magnitude. In contrast, VN does not accelerate the association between tissue-type plasminogen activator (t-PA) and PAI-1. Previously, we reported that the anti-PAI-1 monoclonal antibody (MoAb) CLB-2C8 binds to a short stretch of amino acids of PAI-1, located between residues 128 and 145, and prevents PAI-1 binding to VN. Furthermore, MoAb CLB-2C8 fully blocks the inhibitory activity of PAI-1 towards t-PA, emphasizing the importance of this area for the interaction with t-PA. Here, we show that this area is also required for the interaction between thrombin and PAI-1, since MoAb CLB-2C8 fully prevents inhibition of thrombin by PAI-1. In spite of similar structural requirements for the interaction between t-PA, PAI-1 and VN and between thrombin, PAI-1 and VN, the intermediate reaction products are clearly distinct. By employing surface plasmon resonance (SPR), using the BIAcore equipment, and by immunoprecipitation we demonstrate that, in the presence of VN, t-PA and PAI-1 form exclusively equimolar binary t-PA/PAI-1 complexes. Thrombin, PAI-1 and VN generate equimolar, binary thrombin/PAI-1 complexes and in addition equimolar, ternary complexes and multimers.

The activation-resistant conformation of recombinant human plasminogen is stabilized by basic residues in the amino-terminal hinge region.

Fully activable recombinant human plasminogen (rPlg) was expressed in mammalian cells employing either recombinant vaccinia virus or stable lines coexpressing alpha 2-plasmin inhibitor. A panel of eight variants of rPlg was constructed, in which progressively up to 6 basic amino acid residues in the hinge region of rPlg between the NH2-terminal acidic domain ("proactivation peptide") and kringle 1 were substituted by neutral residues. Analysis of the cleavage rates of these variants by plasmin revealed that the peptide bond at Arg68 is most susceptible, followed by Lys62 and Lys77. A variant with all 6 basic residues substituted was cleaved at Lys20. Three of these variants, PlgB (R68A, R70A), PlgF (R68A, R70A, K77H, K78H), and PlgG (R61A, K62A, R68A, R70A, K77H, K78H), as well as rPlg, were analyzed in more detail. The conformation of these plasminogens was analyzed by monitoring the change in intrinsic fluorescence upon binding of lysine analogs. This revealed that rPlg exhibits the native tight Glu1-plasminogen conformation, whereas PlgB, PlgF, and Plg G display an open conformation similar to Lys78-plasminogen, leading to an increased affinity for lysine analogs. This allowed a direct study of the impact of the activation-resistant conformation on the properties of Glu1-plasminogen. The open conformation of rPlg variants leads to an increased rate of activation by urokinase-type plasminogen activator and streptokinase and increased binding to a fibrin clot. Fibrin clot lysis mediated by tissue-type plasminogen activator was accelerated for the variants as a result of a lower Km for tissue-type plasminogen activator-mediated plasminogen activation, resulting from the increased affinity of rPlg (variants) for intact fibrin. We conclude that the basic residues in the extremely plasmin susceptible hinge region of plasminogen are directly involved in maintaining the activation resistant Glu1-plasminogen conformation.

The role of the finger domain of tissue-type plasminogen activator (t-PA) and plasminogen activator inhibitor 1 (PAI-1) in initiation and progression of thrombolysis.

We further investigated the role of the finger (F) and the kringle-2 (K2) domains of tissue-type plasminogen activator (t-PA) in fibrin-stimulated plasminogen activation. To that end, the action of purified (wt) t-PA or of variants lacking F (del.F) or K2 (del.K2) was assessed either in a static, human whole blood clot-lysis system or in whole blood thrombi generated in the "Chandler loop". In both clot-lysis systems, significant differences were observed for the initiation of thrombolysis with equimolar concentrations of the t-PA variants. A relatively minor "lag phase" occurred in thrombolysis mediated by wt t-PA, whereas a 6.4-fold and 1.6-fold extension is found for del.F and del.K2, respectively. We observed identical lag-times, characteristic for each t-PA variant, in platelet-rich heads and in platelet-poor tails of thrombi. Since plasminogen activator inhibitor 1 (PAI-1) is preferentially retained in the platelet-rich heads, we conclude that the inhibitor does not interfere with the initial stage of thrombolysis but exerts its action in later stages, resulting in a reduction of the rate of clot lysis. A complementation clot-lysis assay was devised to study a potential interplay of del.F and del.K2. Accordingly, clot lysis was determined with combinations of del.F and del.K2 that were inversely varied in relation to equipotent dosage to distinguish between additive, antagonistic or synergistic effects of these variants. The isobole for combinations of del.F and del.K2 shows an independent, additive action of del.F and del.K2 in clot lysis.(ABSTRACT TRUNCATED AT 250 WORDS)

The specific roles of finger and kringle 2 domains of tissue-type plasminogen activator during in vitro fibrinolysis.

The specific roles of the finger (F) and kringle 2 (K2) domains of tissue-type plasminogen activator (t-PA) were quantified with regard to fibrin binding and kinetic parameters for plasminogen activation by employing domain-deletion variants. On an intact fibrin clot, active site-blocked 125I-t-PA has a dissociation constant (Kd) of 0.36 +/- 0.08 microM and a single binding site per fibrin monomer (n = 1.1 +/- 0.1). Deletion of the K2 domain results in a 3-fold increase of the Kd (1.1 +/- 0.2 microM) and a 2-fold increase of the binding sites per fibrin monomer (n = 2.0 +/- 0.3). Deletion of the F domain results in nonsaturable binding with high Kd (3.2 +/- 0.6 microM). These results indicate that the high affinity binding of t-PA to intact fibrin is the resultant of the cooperation of two low affinity binding sites assembled on intact t-PA. Furthermore, fibrin clot lysis experiments were performed, using polymerized fibrin and plasminogen. Enzymatic activity of t-PA (variants) was assessed by following the decrease in turbidity of the polymerized fibrin. Intact recombinant t-PA exhibited a Michaelis constant for plasminogen activation (Km) of 37 +/- 2 nM. Deletion of either the K2 or F domain results in an increase of the Km for plasminogen of 4- and 16-fold, respectively. We interpret these kinetic parameters in terms of the ternary complex model: binding of t-PA to fibrin, mediated simultaneously by both the F and K2 domain, is essential for a correct orientation of the enzyme on the fibrin polymer to yield the optimal "Km-driven" stimulation of fibrin on the activation of plasminogen. The different potencies of various deletion mutants in a plasma clot are explained by decreased affinity of the enzymes both for fibrin and for the substrate plasminogen.

Liquid chromatographic analysis of carboxylic acids using N-(4-aminobutyl)-N-ethylisoluminol as chemiluminescent label: determination of ibuprofen in saliva.

N-(4-Aminobutyl)-N-ethylisoluminol was used for labelling of carboxylic acids. The derivatization reaction was carried out with 1-hydroxybenzotriazole as pre-activator of the carboxylic acid function and N-ethyl-N'-(3-dimethylaminopropyl)carbodiimide as the coupling reagent. Optimum conditions for the derivatization were determined by using factorial design analysis, with ibuprofen as the test compound. Chemiluminescence detection was carried out using a post-column on-line electrochemical hydrogen peroxide generation system and the addition of microperoxidase as the catalyst. The detection limit of derivatized ibuprofen in human saliva was 0.7 ng per 0.5 ml of saliva, with a recovery of 96.1 +/- 1.3%. The method was linear over at least three decades (2.5 ng to 2.5 micrograms) and the repeatability was satisfactory (R.S.D. = 5.2% at the 25 ng level; n = 4).