Polychlorinated Dibenzodioxins (PCDDs), Dibenzofurans (PCDFs) and Biphenyls (PCBs) are industrial compounds or byproducts that can cause toxic effects after binding to aryl hydrocarbon receptor (AhR). But the mechanism about PCDDs, PCDFs and PCBs binding to AhR is unclear. To study the interaction and significant amino acid residues in binding of PCDDs, PCDFs and PCBs to AhR, a docking-based Comparative Molecular Similarity Indices Analysis (CoMSIA) was performed on a set of structurally diverse PCDDs, PCDFs and PCBs with known binding affinities. The docking-based CoMSIA model (non-cross-validated regression coefficient of 0.942 and cross-validated regression coefficient of 0.768) was developed and compared with previous report, the presented docking-based CoMSIA model showed good robustness and predictive performance. The obtained docking conformations and predictive CoMSIA model could provide clues to understand key residues and interactions between receptor and compounds of interest.
Polychlorinated dibenzodioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and polychlorinated biphenyls (PCBs) cause toxic effects after binding to an intracellular cytosolic receptor called the aryl hydrocarbon receptor (AhR). Thymic atrophy, weight loss, immunotoxicity, acute lethality, and induction of cytochrome P4501A1 have all been correlated with the binding affinity to AhR. To study the key molecular features for determining binding affinity to AhR, a homology model of AhR ligand-binding domains was developed, a molecular docking approach was employed to obtain docking-based conformations of all molecules in the whole set, and 3-dimensional quantitative structure-activity relationship (3D-QSAR) methodology, namely, comparative molecular field analysis (CoMFA), was applied. A partial least square analysis was performed, and QSAR models were generated for a training set of 59 compounds. The generated QSAR model showed good internal and external statistical reliability, and in a comparison with other reported CoMFA models using different alignment methods, the docking-based CoMFA model showed some advantages.
A linear discriminant analysis (LDA) coupled with an enhanced replacement method (ERM) was used as an alternative method to predict the carcinogenicity of N-nitroso compounds (NOCs) in rats. This presented LDA based on the topological substructural molecular descriptors (TOPS-MODE) approach was developed to predict the carcinogenic and noncarcinogenic activity on a data set of 111 NOCs with a good classification value of 90.1%. The predictive power of the LDA model was validated through an external validation set (37 compounds) with a prediction accuracy of 94.6% and a leave-one-out cross-validation procedure (LOOCV) with a good prediction of 86.5%. This methodology showed that the TOPS-MODE descriptors weighted, respectively, by bond dipole moment and Abraham solute descriptor dipolarity/polarizability affected the NOC carcinogenicity. The contributions of certain bonds and fragments to carcinogenicity were used to assess biotransformation and carcinogenic mechanisms. The positive contribution of the carbon-nitrogen single bond (between the N-nitroso group and ?-carbon to the N-nitroso group) indicated that the ?-hydroxylation reaction could occur at the ?-carbon or otherwise not occur. Similarly, the contributions from the molecular fragment could be applied to indicate whether the fragments generated an alkylating agent. These results suggested that this approach could discriminate between carcinogenic and noncarcinogenic NOCs, thereby providing insight into the structural features and chemical factors related to NOC carcinogenicity.
Microcystin-leucine arginine (MC-LR), a cyclic heptapeptide produced by several bloom-forming cyanobacteria, has strong reproductive toxicity. We examined whether MC-LR could enter spermatogonia and investigated the toxic effects of MC-LR on spermatogonia in vitro. Multispecific organic anion-transporting polypeptides (Oatps), which transported MCs, were screened as well. Spermatogonia were exposed to 0, 0.5, 5, 50, and 500 nmol/L (nM) MC-LR for 6 h. Cell viability and total antioxidant capacity significantly decreased, meanwhile, the ratio of apoptotic cells, reactive oxidative species (ROS) production, mitochondrial membrane potential (MMP), and intracellular free Ca²? increased after exposure to 5 nM and higher concentrations of MC-LR. MC-LR can immigrate into spermatogonia. At least 5 Oatps (Oatp1a5, -3a1, -6b1, -6c1, and -6d1) were detected at the mRNA level in spermatogonia, and the expression of these Oatps was affected by MC-LR, especially Oatp3a1. This study demonstrated that MC-LR can be transported into spermatogonia and leads to cytotoxicity.
The quantitative structure-activity relationship (QSAR) of N-nitroso compounds (NOCs) for rat liver was developed by a topological sub-structural molecular-descriptors (TOPS-MODE) approach to predict non-liver-carcinogenic and liver-carcinogenic N-nitroso compounds based on a data set of 108 NOCs. Three descriptors calculated solely from the molecular structures of the compounds were selected by enhanced replacement method (ERM) and were weighted, respectively, with atomic weight, bond dipole moments and Abraham solute descriptor partition between water and aqueous solvent systems to indicate the importance of their roles in liver specificity. A detailed discussion on these three descriptors was carried out, and the contributions of different fragments to rat-liver specificity and the interactions among fragments were analyzed. Such results can offer some useful theoretical references for understanding the chemical structural and biological factors related to the liver-specific biological activity of NOCs.
This study aimed to determine the most significant molecular features associated with the liver specificity of the carcinogenicity of N-nitroso compounds (NOCs). Accordingly, quantitative structure-activity relationship (QSAR) analysis was performed to extract molecular information from NOCs using a topological substructural molecular descriptor (TOPS-MODE) approach. A linear discriminant analysis (LDA) model of a series of NOCs for rat liver was developed using TOPS-MODE descriptors to predict nonliver- and liver-carcinogenic NOCs. Two descriptors exclusively calculated from the molecular structures of the compounds were selected by a genetic algorithm. The descriptors were then weighted with bond distances as well as the Abraham solute descriptor partition between water and aqueous solvent systems to indicate the importance of their roles in liver specificity. The performances of the LDA model were rigorously validated by leave-one-out cross-validation and external validation, with the prediction accuracy reaching 88.3% and 80.0%, respectively. The contributions of the different molecular fragments to rat-liver specificity were computed. The results served as important information related to liver specificity and were analyzed from the chemical-molecular perspective. The resulting model can provide an efficient method to discriminate between as well as extrapolate nonliver- and liver-carcinogenic NOCs. The contribution of the entire nitrosamine molecule was determined as being responsible for the liver specificity of nitrosamine carcinogenicity. Although the QSAR showed limitations in complex hepatocarcinogenicity, the proposed method may considerably help elucidate the role of nitrosamines in liver specificity from the chemical-molecular perspective. The nature of these enzyme-substrate interactions is characterized. Insight into the chemical-structural and biological factors related to the liver-specific biological activity of NOCs is also provided.
Related JoVE Video
Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.