Deciphering diseases and biological targets for environmental chemicals using toxicogenomics networks

Abstract

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.

Figure 1. Workflow of the strategy for generating a human P-PAN and predicting novel associations.

DATA: Extraction and filtering of human protein-chemical associations from CTD. The visualization of the chemical space by Principal Component Analysis projection confirms that drugs (D) and environmental chemicals (E) shared structural properties, and then may affect similar protein targets. The two first principal components, which explained about 44% of the variance on the calculated properties are shown (green: pharmaceutical actions, red: toxic actions and blue: specialty uses of chemical). All proteins (P) were mapped to Ensembl gene identifiers to facilitate further data integration. MODEL GENERATION: Construction of the P-PAN. The P-PAN was created from associations present in the CTD (dashed edge lines) between chemicals and proteins. In the P-PAN, two proteins are connected to each other (edge lines) if they share a common chemical. A weighted score, represented by the width of the black edges, was assigned to each protein-protein association. It represents the strength of the network between two proteins as defined by the number of shared compounds for both molecular targets. Selection of a scoring function and a high confidence P-PAN after overlaps comparison with two human interactomes (PPIs) based on experimental evidences. Clustering of the P-PAN and evaluation of the biological meaningful of the clusters using Gene Ontology annotations. PREDICTION: (1) Prediction of novel molecular targets for chemical using a neighbor protein procedure. DEHP (orange) is known to be connected with blue proteins and is predicted to be associated with green proteins. A confidence score was calculated for each protein, represented by the width of the edges; thick edge for high score to thin edge for low score. (2) Prediction of disease associated with chemical after integration of protein-disease information using GeneCards in clusters. As example, apocarotenal, a compound found in spinach is predicted to be link to necrosis.

Figure 2. Cross-species comparative toxicogenomics for bisphenol A (BPA).

Molecular targets are represented as nodes, and colored by gene family. Nodes presence represent available information extracted from the CTD and node absence are the unknown information. Colored nodes defined that BPA affect the protein, while nodes are not colored when BPA does not affect the protein. This figure highlights similarities and differences existing between animal model and human responses to chemical exposure.