Selective fatty acid release from intracellular phospholipids caused by PCBs in rat renal tubular cell cultures

Molecular interaction of PCB153 to human serum albumin: insights from spectroscopic and molecular modeling studies

Polychlorinated biphenyls (PCBs) possessed much potential hazard to environment because of its chemical stability and biological toxicity. Here, we identified the binding mode of a representative compound, PCB153, to human serum albumin (HSA) using fluorescence and molecular dynamics simulation methods. The fluorescence study showed that the intrinsic fluorescence of HSA was quenched by addition of PCB153 through a static quenching mechanism. The thermodynamic analysis proved the binding behavior was mainly governed by hydrophobic force. Furthermore, as evidenced by site marker displacement experiments using two probe compounds, it revealed that PCB153 acted exactly on subdomain IIIA (site II) of HSA. On the other hand, the molecular dynamics studies as well as free energy calculations made another important contribution to understand the conformational changes of HSA and the stability of HSA-PCB153 system. Molecular docking revealed PCB153 can bind in a large hydrophobic activity of subdomain IIIA by the hydrophobic interaction and hydrogen bond interactions between chlorine atoms and residue ASN391. The present work provided reasonable models helping us further understand the transporting, distribution and toxicity effect of PCBs when it spread into human blood serum.

Prediction of biomagnification factors for some organochlorine compounds using linear free energy relationship parameters and artificial neural networks

Multiple linear regression and artificial neural networks (ANNs) as feature mapping techniques were used for the prediction of the biomagnification factor (BMF) of some organochlorine pollutants. As independent variables, or compound descriptors, the Abraham descriptors often employed in linear free energy relationships were used. Much better results were obtained from the nonlinear ANN model than from multiple linear regression. The average absolute error, average relative error and root mean square error in the calculation of log (BMF) by the ANN model were 0.055, 0.051 and 0.097 for the training set and 0.11, 0.086 and 0.175 for the internal validation set, respectively. The degree of importance of each descriptor was evaluated by carrying out a sensitivity analysis approach for the nonlinear model. The results obtained reveal that the order of importance is the pollutant volume, the pollutant dipolarity/polarizability and the pollutant excess molar refraction. In order to examine the credibility of the obtained ANN model the leave-many-out cross-validation test was applied which gave Q(2)= 0.827 and SPRESS = 0.15. Also the Y-scrambling procedure was applied to the ANN model in order to examine the effect of chance correlations. The results obtained reveal that it is possible to predict the BMFs of organochlorine pollutants using a nonlinear ANN model with Abraham descriptors as inputs.

Modelling the depuration rates of polychlorinated biphenyls in Oncorhynchus mykiss with quantum chemical descriptors

Using quantum chemical descriptors and partial least squares regression, a quantitative structure-activity relationship (QSAR) model is developed for the depuration rate constants (log k(d)) of 62 polychlorinated biphenyls (PCBs) in juvenile rainbow trout (Oncorhynchus mykiss). The values of the cross-validated regression coefficient (Qcum(2)) and standard deviation (SD) are 0.655 and 0.05, respectively. The high cross-validated coefficient and low standard deviation indicate that the QSAR model is well predictive. In the QSAR model, the following six descriptors are highly significant: QH(+) (the most positive charge of a hydrogen atom), HOF (standard heat of formation), CCR (core-core repulsion), EE (electronic energy), alpha(2) (squared average molecular polarisability), and S (molecular surface area). The significant descriptors show that the depuration of PCBs in rainbow trout may be mainly attributed to the biota-water partitioning process, and the reactive activity of PCB molecules may play a subordinate role.

Differential effects of PCBs on the induction of apoptosis machinery and PKCα translocation in rat renal tubular cell cultures

We have demonstrated previously [Pérez-Reyes, P.L., Sánchez-Alonso, J.A., López-Aparicio, P., Recio, M.N., Pérez-Albarsanz, M.A., 2001. Different molecular capacity in the induction of apoptosis by polychlorinated biphenyl congeners in rat renal tubular cell cultures. Biosci. Rep. 6, 765-778] that the polychlorinated biphenyls (PCBs) cause loss of cell viability and accelerate apoptosis in cell kidney cultures. Further investigations are necessary to elucidate the mechanism of apoptosis induction. In this way, we have analyzed in the present work the effects of PCBs on protein kinase C (PKC, a protein family intimately involved in the regulation of cell survival) and the expression of two proapoptotic (caspase-3 and Bax) and one antiapoptotic (Bcl-2) proteins. Aroclor 1248 (a commercial PCB mixture with 48% chlorine by weight), PCB 153 (2,2',4,4',5,5'-hexachlorobiphenyl, a di-ortho-substituted nonplanar congener) and PCB 77 (3,3',4,4'-tetrachlorobiphenyl, a non-ortho-substituted planar congener), significantly increased PKCalpha activity compared to control cells in the cytosolic and particulate cell fractions, and increased the PKCalpha protein content in the particulate fraction. The nonplanar PCB 153 showed stronger effects than the coplanar congener PCB 77. In addition, Aroclor 1248 decreased both, procaspase-3 levels and the Bcl-2/Bax protein ratio. These findings indicate that PCBs, particularly nonplanar congeners, can induce apoptosis in primary renal tubular cells through the PKCalpha, caspase-3 and Bcl-2/Bax pathway.

QSPR models for polychlorinated biphenyls: n-Octanol/water partition coefficient

The logarithmic n-octanol/water partition coefficient (logK(ow)) is an important property for pharmacology, toxicology and medicinal chemistry. Quantitative structure-property relationship (QSPR) model for the lipophilic behaviour (logK(ow)) of the data set containing 133 polychlorinated biphenyl (PCB) congeners is analyzed using the conceptual density functional theory based global reactivity parameter such as electrophilicity index (omega) along with energy of lowest unoccupied molecular orbital (E(LUMO)) and number of chlorine substituents (N(Cl)) as descriptors. A reasonably good coefficient of determination (r(2) = 0.914) and the internal predictive ability (r(cv)(2) = 0.909) values are obtained indicating the significance of the considered descriptors in the property analysis of PCBs. Further, the developed method has widespread applicability from chemical reactivity to toxicity analysis and in studies related to various physicochemical properties in the series of dioxins and other polyaromatic hydrocarbons.

Quantitative relationships between molecular structures, environmental temperatures and octanol-air partition coefficients of polychlorinated biphenyls

A quantitative model that incorporates information on both environmental temperatures (T) and molecular structures, for logarithm of octanol-air partition coefficient to base 10 (logK(OA)) of polychlorinated biphenyls (PCBs) was developed. Partial least squares (PLS) analysis together with 16 theoretical molecular structural descriptors was used to develop the Quantitative relationships between structures, environmental temperatures and properties (QRSETP) model. The cross-validated Q(cum)(2) value for the optimal QRSETP model is 0.976, indicating a good predictive ability for logK(OA) of PCBs at different environmental temperatures. T, E(LUMO) (the energy of the lowest unoccupied molecular orbital), molecular size or average molecular polarizability (alpha), and the net atomic charges on chlorine, hydrogen and carbon atoms of PCB molecules, are major factors governing logK(OA). The lower the E(LUMO), the greater the intermolecular interactions between octanol and PCB molecules, and thus the greater the logK(OA) values. Because of intermolecular dispersive forces, the more chlorine atoms in PCB molecules, the greater the molecular size or alpha, the greater the logK(OA). The largest negative net atomic charge on a carbon atom (q(C)(-)) and molecular size or average molecular polarizability (alpha) are major factors governing temperature dependence of logK(OA). PCB molecules with low q(C)(-) values and more chlorines (big size or alpha) tend to have strong temperature dependence, due to intermolecular electrostatic interactions and dispersive forces, respectively.

Quantitative structure-property relationships for octanol-air partition coefficients of polychlorinated biphenyls

Based on nine quantum chemical descriptors computed by PM3 Hamiltonian, using partial least squares analysis, a significant quantitative structure-property relationship for the logarithm of octanol-air partition coefficients (logK(OA)) of polychlorinated biphenyls (PCBs) was obtained. The cross-validated Q2cum value of the model is 0.962, indicating a good predictive ability. The intermolecular dispersive interactions and thus the size of the PCB molecules play a key role in governing log K(OA). The greater the size of PCB molecules, the greater the logK(OA) values. Increasing ELUMO (the energy of the lowest unoccupied molecular orbital) values of the PCBs leads to decreasing logK(OA) values, indicating possible interactions between PCB and octanol molecules. Increasing Q(Cl)+, (the most positive net atomic charges on a chlorine atom) and Q(C)- (the largest negative net atomic charge on a carbon atom) values of PCBs results in decreasing lg K(OA) values, implying possible intermolecular electrostatic interactions between octanol and PCB molecules.