Application of computational systems biology to explore environmental toxicity hazards

BACKGROUND

Computer-based modeling is part of a new approach to predictive toxicology.

OBJECTIVES

We investigated the usefulness of an integrated computational systems biology approach in a case study involving the isomers and metabolites of the pesticide dichlorodiphenyltrichloroethane (DDT) to ascertain their possible links to relevant adverse effects.

METHODS

We extracted chemical-protein association networks for each DDT isomer and its metabolites using ChemProt, a disease chemical biology database that includes both binding and gene expression data, and we explored protein-protein interactions using a human interactome network. To identify associated dysfunctions and diseases, we integrated protein-disease annotations into the protein complexes using the Online Mendelian Inheritance in Man database and the Comparative Toxicogenomics Database.

RESULTS

We found 175 human proteins linked to p,p'-DDT, and 187 to o,p'-DDT.Dichlorodiphenyldichloroethylene (p,p'-DDE) was the metabolite with the highest number of links, with 52. We grouped proteins for each compound based on their disease annotations. Although the two data sources differed in linkage to diseases, integrated results predicted that most diseases were linked to the two DDT isomers. Asthma was uniquely linked with p,p'-DDT, and autism with o,p'-DDT. Several reproductive and neurobehavioral outcomes and cancer types were linked to all three compounds.

CONCLUSIONS

Computer-based modeling relies on available information. Although differences in linkages to proteins may be due to incomplete data, our results appear meaningful and suggest that the parent DDT compounds may be responsible for more disease connections than the metabolites. The findings illustrate the potential use of computational approaches to toxicology.

Many putative endocrine disruptors inhibit prostaglandin synthesis

BACKGROUND

Prostaglandins (PGs) play key roles in development and maintenance of homeostasis of the adult body. Despite these important roles, it remains unclear whether the PG pathway is a target for endocrine disruption. However, several known endocrine-disrupting compounds (EDCs) share a high degree of structural similarity with mild analgesics.

OBJECTIVES AND METHODS

Using cell-based transfection and transduction experiments, mass spectrometry, and organotypic assays together with molecular modeling, we investigated whether inhibition of the PG pathway by known EDCs could be a novel point of endocrine disruption.

RESULTS

We found that many known EDCs inhibit the PG pathway in a mouse Sertoli cell line and in human primary mast cells. The EDCs also reduced PG synthesis in ex vivo rat testis, and this reduction was correlated with a reduced testosterone production. The inhibition of PG synthesis occurred without involvement of canonical PG receptors or the peroxisome proliferator-activated receptors (PPARs), which have previously been described as targets of EDCs. Instead, our results suggest that the compounds may bind directly into the active site of the cyclooxygenase (COX) enzymes, thereby obstructing the conversion of arachidonic acid to PG precursors without interfering with the expression of the COX enzymes. A common feature of the PG inhibitory EDCs is the presence of aromatic groups that may stabilize binding in the hydrophobic active site of the COX enzymes.

CONCLUSION

Our findings suggest a hitherto unknown mode of action by EDCs through inhibition of the PG pathway and suggest new avenues to investigate effects of EDCs on reproductive and immunological disorders that have become increasingly common in recent decades.

ChemProt: a disease chemical biology database

Systems pharmacology is an emergent area that studies drug action across multiple scales of complexity, from molecular and cellular to tissue and organism levels. There is a critical need to develop network-based approaches to integrate the growing body of chemical biology knowledge with network biology. Here, we report ChemProt, a disease chemical biology database, which is based on a compilation of multiple chemical–protein annotation resources, as well as disease-associated protein–protein interactions (PPIs). We assembled more than 700 000 unique chemicals with biological annotation for 30 578 proteins. We gathered over 2-million chemical–protein interactions, which were integrated in a quality scored human PPI network of 428 429 interactions. The PPI network layer allows for studying disease and tissue specificity through each protein complex. ChemProt can assist in the in silico evaluation of environmental chemicals, natural products and approved drugs, as well as the selection of new compounds based on their activity profile against most known biological targets, including those related to adverse drug events. Results from the disease chemical biology database associate citalopram, an antidepressant, with osteogenesis imperfect and leukemia and bisphenol A, an endocrine disruptor, with certain types of cancer, respectively. The server can be accessed at http://www.cbs.dtu.dk/services/ChemProt/.

Deciphering diseases and biological targets for environmental chemicals using toxicogenomics networks

Exposure to environmental chemicals and drugs may have a negative effect on human health. A better understanding of the molecular mechanism of such compounds is needed to determine the risk. We present a high confidence human protein-protein association network built upon the integration of chemical toxicology and systems biology. This computational systems chemical biology model reveals uncharacterized connections between compounds and diseases, thus predicting which compounds may be risk factors for human health. Additionally, the network can be used to identify unexpected potential associations between chemicals and proteins. Examples are shown for chemicals associated with breast cancer, lung cancer and necrosis, and potential protein targets for di-ethylhexyl-phthalate, 2,3,7,8-tetrachlorodibenzo-p-dioxin, pirinixic acid and permethrine. The chemical-protein associations are supported through recent published studies, which illustrate the power of our approach that integrates toxicogenomics data with other data types.

Exposure to environmental chemicals and drugs may have a negative effect on human health. An essential step towards understanding the effect of chemicals on human health is to identify all possible molecular targets of a given chemical. Recently, various network-oriented chemical pharmacology approaches have been published. However, these methods limit the protein prediction to already known molecular drug targets. New findings can for example be made by using high-confidence protein-protein association databases. Here, we describe a generic, computational systems biology model with the aim of understanding the underlying molecular mechanisms of chemicals and the biological pathways they perturb. We present a novel and complementary approach to existing models by integrating toxicogenomics data, chemical structures, protein-protein interaction data, disease information and functional annotation of proteins. The high confidence protein-protein association network proposed reveals unexpected connections between chemicals and diseases or human proteins. We provide literature support to demonstrate the validity of some predictions, and thereby illustrate the power of an approach that integrates toxicogenomics data with other data types.

New Ligands with Affinity for the α4β2 Subtype of Nicotinic Acetylcholine Receptors. Synthesis, Receptor Binding, and 3D-QSAR Modeling

A new series of piperazines, diazepanes, diazocanes, diazabicyclononanes, and diazabicyclodecanes with affinity for the alpha4beta2 subtype of nicotinic acetylcholine receptors were synthesized on the basis of results from a previous computational study. A predictive 3D-QSAR model was developed using the GRID/GOLPE approach (R2 = 0.94, Q2 = 0.83, SDEP = 0.34). The SAR was interpreted in terms of contour maps of the PLS coefficients and in terms of a homology model of the alpha4beta2 subtype of the nicotinic acetylcholine receptors. The results reveal that hydrogen bonding from both hydrogens on the protonated amine and from the pyridine nitrogen to a water molecule as well as van der Waals interactions between the substituent bearing the protonated amine and the receptor is of importance for ligand affinity. The combination of 3D-QSAR and homology modeling proved successful for the interpretation of structure-affinity relationships as well as the validation of the individual modeling approaches.

Emerging classes of protein–protein interaction inhibitors and new tools for their development

Protein-protein interactions play a key role in the signal transduction pathways that regulate cellular function. Three years ago, few descriptions of small molecule protein-protein interaction inhibitors (SMPPIIs) existed in the literature. Today, the number of examples of both the biology and chemistry of such interaction inhibitors is growing rapidly. This growth occurs at the convergence of medicinal chemistry, signaling biology and novel assay technology for profiling emerging compound classes and modes of action. Protein translocation assays provide a unique new tool for identifying, profiling, and optimizing SMPPIIs. This review summarizes recent work in the field, and outlines future developments we can anticipate.

New Series of Morpholine and 1,4-Oxazepane Derivatives as Dopamine D4 Receptor Ligands: Synthesis and 3D-QSAR Model

Since the identification of the dopamine D(4) receptor subtype and speculations about its possible involvement in schizophrenia, much work has been put into development of selective D(4) ligands. These selective ligands may be effective antipsychotics without extrapyramidal side effects. This work describes the synthesis of a new series of 2,4-disubstituted morpholines and 2,4-disubstituted 1,4-oxazepanes with selectivity for the dopamine D(4) receptor. A 3D-QSAR analysis using the GRID/GOLPE methodology was performed with the purpose to get a better understanding of the relationship between chemical structure and biological activity. Inspection of the coefficient plots allowed us to identify that regions which are important for affinity are situated around the two benzene ring systems, a p-chlorobenzyl group, and the aliphatic amine belonging to the morpholine or 1,4-oxazepane system. In addition, the size of the morpholine or 1,4-oxazepane ring seems to be important for affinity.

Hybrid systems for virtual screening: interest of fuzzy clustering applied to olfaction

Kohonen neural networks, also known as Self Organizing Map (SOM), offer a useful 2D representation of the compound distribution inside a large chemical database. This distribution results from the compound organization in a molecular diversity hyperspace derived from a large set of molecular descriptors. Fuzzy techniques based on the "concept of partial truth" reveal to be also a valuable tool for the direct exploitation of chemical databases or SOM. In such cases a fuzzy clustering algorithm is used. In this paper, a complete hybrid system, combining SOM and fuzzy clustering, is applied. As example, a series of olfactory compounds was selected. The complexity of such information is that a same compound may exhibit different odors. It is shown how fuzzy logic helps to have a better understanding of the organization of the compounds. These hybrid systems, using simultaneously SOM and fuzzy clustering, are foreseen as powerful tools for "virtual pre-screening".