Abstract
During the past decades, many epidemiological, toxicological and biological studies have been performed to assess the role of environmental chemicals as potential toxicants associated with diverse human disorders. However, the relationships between diseases based on chemical exposure rarely have been studied by computational biology. We developed a human environmental disease network (EDN) to explore and suggest novel disease-disease and chemical-disease relationships. The presented scored EDN model is built upon the integration of systems biology and chemical toxicology using information on chemical contaminants and their disease relationships reported in the TDDB database. The resulting human EDN takes into consideration the level of evidence of the toxicant-disease relationships, allowing inclusion of some degrees of significance in the disease-disease associations. Such a network can be used to identify uncharacterized connections between diseases. Examples are discussed for type 2 diabetes (T2D). Additionally, this computational model allows confirmation of already known links between chemicals and diseases (e.g., between bisphenol A and behavioral disorders) and also reveals unexpected associations between chemicals and diseases (e.g., between chlordane and olfactory alteration), thus predicting which chemicals may be risk factors to human health. The proposed human EDN model allows exploration of common biological mechanisms of diseases associated with chemical exposure, helping us to gain insight into disease etiology and comorbidity. This computational approach is an alternative to animal testing supporting the 3R concept.
Highlights
It is well established that genes and environmental factors influence common human diseases, understanding disease-causing defects is still a challenge (Hunter, 2005)
Three cases fall under this category: (a) the chemical toxicity is well known and the chemical is recognized to cause the disease, (b) the causal associations have been found in recent, large, prospective or retrospective cohort studies, and (c) the chemicals are listed as group 1 human carcinogens by the International Agency for Research on Cancer (IARC)2
Diseases and gene ontology (GO) information were integrated from two different sources in each gene/protein list
Summary
It is well established that genes and environmental factors influence common human diseases, understanding disease-causing defects is still a challenge (Hunter, 2005). Several network based approaches have allowed deciphering disease comorbidity (Goh et al, 2007; Lee et al, 2008; Hidalgo et al, 2009; Suthram et al, 2010; Davis and Chawla, 2011; Sun et al, 2014a; Menche et al, 2015). Roque et al (2011) created an approach to gather phenotypic descriptions of patients from medical records that would suggest new disease-disease associations. All these studies provide comprehensive views of links between diseases, they all rely on existing knowledge, i.e., genes, pathways and phenotypic associations (Hidalgo et al, 2009)
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