The impact of TiO2 nanoparticles on uptake and toxicity of benzo(a)pyrene in the blue mussel (Mytilus edulis).

Nanoparticles are emerging contaminants of concern. Knowledge on their environmental impacts is scarce, especially on their interactive effects with other contaminants. In this study we investigated effects of titanium dioxide nanoparticles (TiO2NP) on the blue mussel (Mytilus edulis) and determined their influence on the bioavailability and toxicity of benzo(a)pyrene (B(a)P), a carcinogenic polyaromatic hydrocarbon (PAH). Blue mussels were exposed to either TiO2NP (0.2 and 2.0 mg L(-1)) or B(a)P (20 µg L(-1)) and to the respective combinations of these two compounds. Aqueous contaminant concentrations, the uptake of Ti and B(a)P into mussel soft tissue, effects on oxidative stress and chromosomal damage were analyzed. The uncoated TiO2NP agglomerated rapidly in the seawater. The presence of TiO2NP significantly reduced the bioavailability of B(a)P, shown by lowered B(a)P concentrations in exposure tanks and in mussel tissue. The activities of antioxidant enzyme superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) were impacted by the various exposure regimes, indicating oxidative stress in the contaminant exposure groups. While SOD activity was increased only in the 0.2TiO2NP exposure group, CAT activity was enhanced in both combined exposure groups. The GPx activity was increased only in the groups exposed to the two single compounds. In hemocytes, increased chromosomal damage was detected in mussels exposed to the single compounds, which was further increased after exposure to the combination of compounds. In this study we show that the presence of TiO2NP in the exposure system reduced B(a)P uptake in blue mussels. However, since most biomarker responses did not decrease despite of the lower B(a)P uptake in combined exposures, the results suggest that TiO2NP can act as additional stressor, or potentially alters B(a)P toxicity by activation.

HPLC analysis of polyphenols in the fruits of Rubus idaeus L. (Rosaceae).

The separation of anthocyanins present in the fruits of 11 varieties of red raspberries (Rubus idaeus L.) was performed by high performance liquid chromatography (HPLC) with a diode-array detector and evaporative light scattering detection (ELSD). The ELSD parameters--drift tube temperature, nebulising gas flow rate and gain value--were optimised to get the best detection and identification of the anthocyanins. The varieties Heritage and Willamette had the simplest anthocyanin sets consisting of only two predominant anthocyanins--cyanidin-3-O-sophoroside (1) and cyanidin-3-O-glucoside (3), while in the other varieties two other predominant compounds were also present, cyanidin-3-O-rutinoside (4) and cyanidin-3-O-(2(G)-O-glucosylrutinoside) (2). Moreover, using ELSD, simultaneous analysis of anthocyanins and sanguiin H-6 (5), an ellagitannin, was performed. The contents of anthocyanins and sanguiin H-6 (5) were estimated by HPLC with ultraviolet-visible (UV-vis) light detection. The determined concentrations of anthocyanins varied from 76.22 to 277.06 mg per 100 g of dry weight (d.w.). The content of sanguiin H-6 (5) was in the range from 135.04 to 547.48 mg per 100 g of d.w.

Advanced QSRR modeling of peptides behavior in RPLC.

In QSRR the retention is modeled as a function of structural or molecular descriptors. Since the structural datasets can be very large a selection of informative variables is often required. But beside the question which subset of variables (descriptors) produces optimum predictions one should answer the question: can good prediction be used in the QSRR community even if the physical meaning of applied descriptors is hard to interpret? The main focus in this paper is put on different modeling methodologies applied and molecular descriptors used in the QSRR approaches. Besides the widely used multiple linear regression (MLR), these methodologies include partial least squares (PLS), uninformative variable elimination partial least squares (UVE-PLS), genetic algorithms (GA) prior to MLR or PLS. The comparison will focus on the predictive performance but also on the descriptors found to be most important for the chromatographic retention prediction of peptides. The results of this study showed that stepwise-MLR and UVE-PLS are producing better predictions than the rest of the studied methodologies. From the variables selected by various methodologies one can see that the important information for the retention mechanism of RPLC was given by 2D-, 3D-descriptors and descriptors from the empirical QSRR equations, which bring the information about hydrogen-bonding properties, molecular size, and complexity. Overall, for the considered data set the empirical QSRR models were predicting the peptides retention best.

The molecular descriptor logSumAA and its alternatives in QSRR models to predict the retention of peptides.

The use of the experimental molecular descriptor logSum(AA) and some possible alternatives were evaluated in the QSRR analysis of peptides. To quantitatively characterize the structure of analytes in a previously proposed QSRR the following three structural descriptors were applied: the logarithm of the sum of gradient retention times of the amino acids composing the individual peptide, logSum(AA); the logarithm of the peptide's van der Waals volume, logVDW(Vol); and the logarithm of its theoretically calculated n-octanol-water partition coefficient, clogP. Taking into consideration that most amino acids were hardly retained in the different RP-HPLC systems on which the peptides retention was measured, the contribution of most amino acids to the logSum(AA) descriptor is rather constant. Therefore, to enlarge the variability of the descriptor and the amino acids contributions for a given series of peptides, in a first instance, it was evaluated whether, by changing the chromatographic conditions, the retention differences between the amino acids could be increased, while maintaining their mutual selectivity. It was not evident to find such conditions. Secondly, it was also investigated whether the experimental descriptor logSum(AA) can be replaced by a theoretical, either based on a simple or on a weighted counting of the amino acids composing the peptide. The weighting factor for the retained amino acids was determined by their experimental gradient retention times measured on different systems. The predictive abilities of the new QSRR models (applying the alternative descriptors) were assessed using the leave-one-out cross-validation procedure and compared to that of the initial model. Finally, a descriptor was defined for which the retention measurement of only a limited number of amino acids is required. It resulted in QSRR models with similar predictive properties as those with logSum(AA), but with a reduced workload.

Retention prediction of peptides based on uninformative variable elimination by partial least squares.

A quantitative structure-retention relationship analysis was performed on the chromatographic retention data of 90 peptides, measured by gradient elution reversed-phase liquid chromatography, and a large set of molecular descriptors computed for each peptide. Such approach may be useful in proteomics research in order to improve the correct identification of peptides. A principal component analysis on the set of 1726 molecular descriptors reveals a high information overlap in the descriptor space. Since variable selection is advisable, the retention of the peptides is modeled with uninformative variable elimination partial least squares, besides classic partial least squares regression. The Kennard and Stone algorithm was used to select a calibration set (63 peptides) from the available samples. This set was used to build the quantitative structure-retention relationship models. The remaining 27 peptides were used as independent external test set to evaluate the predictive power of the constructed models. The UVE-PLS model consists of 5 components only (compared to 7 components in the best PLS model), and has the best predictive properties, i.e., the average error on the retention time is less than 30 s. When compared also to stepwise regression and an empirical model, the obtained UVE-PLS model leads to better and much better predictions, respectively.

Quantitative structure/retention relationships in affinity chromatography.

Affinity chromatography (AC) followed by quantitative structure/retention relationships (QSRR) analysis provides information on both the analytes and the macromolecules forming the stationary phases. QSRR equations derived for test series of analytes (often drugs) are interpreted in terms of structural requirements of the specific binding sites on macromolecules. Chromatographically demonstrated differences in analyte/macromolecule interactions may be relevant to molecular pharmacology and rational drug design. Multiple regression analysis of appropriately designed sets of affinity-chromatographic data may help increase the speed and efficiency of search as for new drugs and reduce the need for in vivo screening. Specific high-performance affinity-chromatographic separations can be optimized by rational selection of chiral columns, the characteristics of which are provided by QSRR.

Computer simulation for the simultaneous optimization of any two variables and any chromatographic procedure.

Computer software that allows the simulation of any chromatographic separation as a function of simultaneous changes in any one or two variables that can affect sample separation order (selectivity) is described. For one example, an application is described for the simultaneous variation of the mobile phase pH and gradient time in reversed-phase liquid chromatography. The accuracy of such predictions is examined for a sample mixture of 17 substituted benzoic acids and anilines, and requirements for an acceptable predictive accuracy are summarized. In a second example, the separation of three peptides by capillary electrophoresis is optimized.

Reversed-phase liquid chromatographic separation of complex samples by optimizing temperature and gradient time III. Improving the accuracy of computer simulation.

Previous studies have shown that four experimental runs, where both temperature T and gradient time tG are varied, can be used for the reliable prediction of separation as a function of these two variables (two-dimensional optimization). Computer simulation (e.g., DryLab) can then be used to predict "optimized" conditions for maximum sample resolution using either isocratic or gradient elution. Samples that contain a large number of components (e.g., n>15-20) present a greater challenge. Resolution for these more complex samples is often quite sensitive to small changes in T or tG in turn requiring greater accuracy in predictions that result from computer simulation. In the present study of several samples, we have examined computer simulation errors that can arise from inexact expressions for retention time as a function of T, tG or isocratic %B. Resulting conclusions are applicable to both complex and simpler samples, in either one- or two-dimensional optimization. Means to anticipate and minimize the impact of these predictive errors are examined.