The validation of a bioanalytical method for the determination of clopidogrel in human plasma

A fast, sensitive and specific LC-MS/MS bioanalytical method for the determination of unchanged clopidogrel in human plasma has been developed and validated over the range of 10-12,000 pg mL(-1) (r2 0.9993) by the Contract Research group at HFL. Samples (0.3 mL) were buffered (pH 6.8), extracted using diethyl ether and 10 microL of the sample extract was injected onto the LC-MS/MS system. Analysis was performed using a C8 column (temperature controlled to 50 degrees C) by gradient elution at a flow rate of 0.9 mL min(-1) over a 3 min run time. Retention times of 1.61 and 1.59 min were observed for clopidogrel and 2H3-clopidogrel (I.S.), respectively. Detection was achieved using a Sciex API 4000, triple quadrupole mass spectrometer, in positive TurboIonspray (electrospray) ionisation mode. Ion transitions were monitored using MRM (multiple reaction monitoring) for clopidogrel (m/z 322-212) and for 2H3-clopidogrel (m/z 327-217). This validated method was used to support a pharmacokinetic study in healthy volunteers.     

Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model

This paper examines the multiple atlas random diffeomorphic orbit model in Computational Anatomy (CA) for parameter estimation and segmentation of subcortical and ventricular neuroanatomy in magnetic resonance imagery. We assume that there exist multiple magnetic resonance image (MRI) atlases, each atlas containing a collection of locally-defined charts in the brain generated via manual delineation of the structures of interest. We focus on maximum a posteriori estimation of high dimensional segmentations of MR within the class of generative models representing the observed MRI as a conditionally Gaussian random field, conditioned on the atlas charts and the diffeomorphic change of coordinates of each chart that generates it. The charts and their diffeomorphic correspondences are unknown and viewed as latent or hidden variables. We demonstrate that the expectation-maximization (EM) algorithm arises naturally, yielding the likelihood-fusion equation which the a posteriori estimator of the segmentation labels maximizes. The likelihoods being fused are modeled as conditionally Gaussian random fields with mean fields a function of each atlas chart under its diffeomorphic change of coordinates onto the target. The conditional-mean in the EM algorithm specifies the convex weights with which the chart-specific likelihoods are fused. The multiple atlases with the associated convex weights imply that the posterior distribution is a multi-modal representation of the measured MRI. Segmentation results for subcortical and ventricular structures of subjects, within populations of demented subjects, are demonstrated, including the use of multiple atlases across multiple diseased groups.     

Protein-protein interactions and substrate channeling in orthologous and chimeric aldolase-dehydrogenase complexes

Bacterial aldolase-dehydrogenase complexes catalyze the last steps in the meta cleavage pathway of aromatic hydrocarbon degradation. The aldolase (TTHB246) and dehydrogenase (TTHB247) from Thermus thermophilus were separately expressed and purified from recombinant Escherichia coli. The aldolase forms a dimer, while the dehydrogenase is a monomer; these enzymes can form a stable tetrameric complex in vitro, consisting of two aldolase and two dehydrogenase subunits. Upon complex formation, the K(m) value of 4-hydroxy-2-oxopentanoate, the substrate of TTHB246, is decreased 4-fold while the K(m) of acetaldehyde, the substrate of TTHB247, is increased 3-fold. The k(cat) values of each enzyme were reduced by ~2-fold when they were in a complex. The half-life of TTHB247 at 50 °C increased by ~4-fold when it was in a complex with TTHB246. The acetaldehyde product from TTHB246 could be efficiently channelled directly to TTHB247, but the channeling efficiency for the larger propionaldehyde was ~40% lower. A single A324G substitution in TTHB246 increased the channeling efficiency of propionaldehyde to a value comparable to that of acetaldehyde. Stable and catalytically competent chimeric complexes could be formed between the T. thermophilus enzymes and the orthologous aldolase (BphI) and dehydrogenase (BphJ) from the biphenyl degradation pathway of Burkholderia xenovorans LB400. However, channeling efficiencies for acetaldehyde in these chimeric complexes were ~10%. Structural and sequence analysis suggests that interacting residues in the interface of the aldolase-dehydrogenase complex are highly conserved among homologues, but coevolution of partner enzymes is required to fine-tune this interaction to allow for efficient substrate channeling.     

Bayesian Analysis Using a Simple Likelihood Model Outperforms Parsimony for Estimation of Phylogeny from Discrete Morphological Data

Despite the introduction of likelihood-based methods for estimating phylogenetic trees from phenotypic data, parsimony remains the most widely-used optimality criterion for building trees from discrete morphological data. However, it has been known for decades that there are regions of solution space in which parsimony is a poor estimator of tree topology. Numerous software implementations of likelihood-based models for the estimation of phylogeny from discrete morphological data exist, especially for the Mk model of discrete character evolution. Here we explore the efficacy of Bayesian estimation of phylogeny, using the Mk model, under conditions that are commonly encountered in paleontological studies. Using simulated data, we describe the relative performances of parsimony and the Mk model under a range of realistic conditions that include common scenarios of missing data and rate heterogeneity.     

How do SNP ascertainment schemes and population demographics affect inferences about population history?

Background

The selection of variable sites for inclusion in genomic analyses can influence results, especially when exemplar populations are used to determine polymorphic sites. We tested the impact of ascertainment bias on the inference of population genetic parameters using empirical and simulated data representing the three major continental groups of cattle: European, African, and Indian. We simulated data under three demographic models. Each simulated data set was subjected to three ascertainment schemes: (I) random selection; (II) geographically biased selection; and (III) selection biased toward loci polymorphic in multiple groups. Empirical data comprised samples of 25 individuals representing each continental group. These cattle were genotyped for 47,506 loci from the bovine 50 K SNP panel. We compared the inference of population histories for the empirical and simulated data sets across different ascertainment conditions using F_ST and principal components analysis (PCA).

Results

Bias toward shared polymorphism across continental groups is apparent in the empirical SNP data. Bias toward uneven levels of within-group polymorphism decreases estimates of F_ST between groups. Subpopulation-biased selection of SNPs changes the weighting of principal component axes and can affect inferences about proportions of admixture and population histories using PCA. PCA-based inferences of population relationships are largely congruent across types of ascertainment bias, even when ascertainment bias is strong.

Conclusions

Analyses of ascertainment bias in genomic data have largely been conducted on human data. As genomic analyses are being applied to non-model organisms, and across taxa with deeper divergences, care must be taken to consider the potential for bias in ascertainment of variation to affect inferences. Estimates of F_ST, time of separation, and population divergence as estimated by principal components analysis can be misleading if this bias is not taken into account.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1469-5) contains supplementary material, which is available to authorized users.