TY - JOUR
T1 - Deciphering Diseases and Biological Targets for Environmental Chemicals using Toxicogenomics Networks
AU - Audouze, Karine Marie Laure
AU - Juncker, Agnieszka
AU - Roque, Francisco José Sousa Simões Almeida
AU - Krysiak-Baltyn, Konrad
AU - Weinhold, Nils
AU - Taboureau, Olivier
AU - Jensen, Thomas Skøt
AU - Brunak, Søren
N1 - This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
U2 - 10.1371/journal.pcbi.1000788
DO - 10.1371/journal.pcbi.1000788
M3 - Journal article
C2 - 20502671
SN - 1553-7358
VL - 6
SP - e1000788
JO - P L o S Computational Biology (Online)
JF - P L o S Computational Biology (Online)
IS - 5
ER -
