You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Deciphering Diseases and Biological Targets for Environmental Chemicals using Toxicogenomics Networks}
Article:
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[1]–[2]. 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[3]–[5]. 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[6]. 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[7]–[10]. For example Keiser et al.[8] applied network analysis to drugs and their targets. The authors identified unexpected molecular targets such as muscarinic acetylcholine receptor M3, 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[11]–[12]. 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( linking chemical exposures to diseases) and more detailed associations( linking chemicals to proteins) 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[3]. 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( P-PAN). We have validated our network by comparison with two high confidence protein-protein interaction( PPI) 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( DEHP) and gamma-aminobutyric acid A receptor beta target[13], 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.
Discussion:
We propose an approach different from existing computational chemical biology networks, which primarily integrate drugs information, to identify new molecular targets for chemicals and to link them to diseases. In our approach we have integrated toxicogenomics data for drugs and environmental compounds. The ability to make new findings using a different network is illustrated by a comparison with a similar method, showing the capacity of our P-PAN to identify novel chemical-protein associations. Using phthalate as an example, our model suggests potential associations between DEHP and GABA receptors, which have not been predicted previously. An extension of this network by integrating more data, for example other chemical-protein associations or dose levels for which a compound may affect human health, would be beneficial to the proposed approach. Paracelsus( 1493–1541) is often cited for his quote, “all things are poisons and nothing is without poison, only the dose permits something not to be poisonous”. This emphasize that the dose of a chemical is an issue to consider in the deregulation of systems biology. Nevertheless, a global mapping could allow a better understanding of adverse effects of drugs and toxic effects of environmental compounds. This could be used as a new approach for risk assessment and regulatory decision-making for human health. Among the examples presented, some predictions are supported by literature for other organisms. Regarding toxicogenomics, the available human data are generally sparse compared to rodents. Data on toxicity- adverse effects of chemicals on humans – can be acquired through epidemiologic studies and from occupational, accident-related exposures as intentional human testing of environmental compounds remains limited. However, differences exist between model animal and human responses to chemicals, including differences in the type of adverse effects experienced and the dosages at which they occur. The differences may reflect variations in the underlying biochemical mechanisms, in metabolism, or in the distribution of the chemicals. As an example, bisphenol A( BPA) does not affect proteins in a similar way across species( Figure 2). In the human systems studied to date, BPA does not affect the proto-oncogene c-FOS( FOS) and the mitogen-activated protein kinase 8( MAKP8) but seems to modify their expression in rodent species. BPA binds and modifies the activity of the estrogen receptor alpha( ESR1) in a very conservative way across organisms[14]. BPA has an ability to function as an estrogen like receptor( ER) agonist, and thus has the potential to disrupt normal endocrine signaling through regulation of ER target genes e. g. androgen receptors, estrogen receptor, progesterone receptors. There is a need to integrate data with cross-species extrapolation in order to have a more accurate understanding of the human risk from chemical exposure. The major limitation of our integrative systems biology approach is that the molecular target predictions are limited to the 3, 528 proteins present in our P-PAN, which represent only 15% of the estimated human proteome[55]. Hence, the current lack of high quality data is the limiting factor in approaches such as the one described here. Today high throughput methodologies result in available large scale data in both chemical biology and systems biology, but these data are discipline specific[56]. There is an evident need for the development of databases[57] to integrate disparate datasets such as toxicogenomics data in order progress in systems biology research. In addition, the results of the disease-compound association analysis will improve in the future as newer, more complete and curated data will become available.
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 .