### 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.
### introduction ###
Humans are daily exposed to diverse hazardous chemicals via skincare products, plastic cups, computers and pesticides to mention but a few sources.
The potential effect of these environmental compounds on human health is a major concern CITATION CITATION.
For example chemicals such as phthalate plasticizers have been widely linked to allergies, reproductive disorders and neurological defects.
Humans are intentionally exposed to drugs used for treatment and cure of diseases.
Many drugs affect multiple targets and may interact or affect the same proteins as environmental chemicals CITATION CITATION.
The mechanism of action of these small molecules is often not completely understood and can be associated to adverse and toxic effects through for example drug-drug interactions CITATION.
There is thus a need to improve our understanding of the underlying mechanism of action of chemicals and the biological pathways they perturb to fully evaluate the impact of small molecules on human health.
An essential step towards deciphering the effect of chemicals on human health is to identify all possible molecular targets of a given chemical.
Various network-oriented chemical pharmacology approaches have been published recently to identify novel protein candidates for drugs, using structural chemical similarity CITATION CITATION.
For example Keiser et al. CITATION applied network analysis to drugs and their targets.
The authors identified unexpected molecular targets such as muscarinic acetylcholine receptor M 3, alpha-2 adrenergic receptor and neurokinin NK2 receptor for methadone, emetine and loperamide, respectively.
Additionally, recent studies have demonstrated that chemicals could be classified based upon their effect on mRNA expression detected by microarrays CITATION CITATION.
Lamb et al. showed that genomic signatures could be used to recognize drugs with common mechanism of action allowing discovery of unknown modes of action.
Despite the explosion of chemical-biological networks, the chemical toxicity remains a major issue in human health.
Analysis of environmental chemicals with similar gene expression profiles is still lacking.
With the recent advances in toxicogenomics, information on gene/protein activity in response to small molecule exposures becomes more available.
This provide necessary data to develop computational systems biology models to predict both high level associations and more detailed associations
In this paper we present a method that can associate chemicals to disease and identify potential molecular targets based on the integration of toxicogenomics data, chemical structures, protein-protein interaction data, disease information and functional annotation.
The core of our procedure is derived from the target hopping concept defined previously CITATION.
But instead of considering only binding activity, we extended the concept to gene expression.
If two proteins are affected with two chemicals, then both proteins are deemed associating in chemical space.
Our approach is not only a statistical model but mimics the true biological system by constructing a network of associations between human proteins defined as Protein-Protein Association Network.
We have validated our network by comparison with two high confidence protein-protein interaction networks, and by assessing the functional enrichment of clusters in the network generated.
The P-PAN revealed both known as well as many novel surprising connections between chemicals and diseases or proteins.
We provide literature support for some of the unexpected associations, such as the connection between diethylhexylphthalate and gamma-aminobutyric acid A receptor beta target CITATION, as well as between apocarotenal, a chemical found in spinach, and necrosis.
This illustrates the usefulness of an approach that integrates toxicogenomics data with other diverse data types.
