TY - JOUR

T1 - Deciphering Diseases and Biological Targets for Environmental Chemicals using Toxicogenomics Networks

A1 - Audouze,Karine Marie Laure

A1 - Juncker,Agnieszka

A1 - Roque,Francisco José Sousa Simões Almeida

A1 - Krysiak-Baltyn,Konrad

A1 - Weinhold,Nils

A1 - Taboureau,Olivier

A1 - Jensen,Thomas Skøt

A1 - Brunak,Søren

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

PB - Public Library of Science

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

JO - P L o S Computational Biology

JF - P L o S Computational Biology

SN - 1553-734X

IS - 5

VL - 6

SP - e1000788

ER -