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Deciphering diseases and biological targets for environmental chemicals using toxicogenomics networks.


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.

SUBMITTER: Audouze K 

PROVIDER: S-EPMC2873901 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Deciphering diseases and biological targets for environmental chemicals using toxicogenomics networks.

Audouze Karine K   Juncker Agnieszka Sierakowska AS   Roque Francisco J S S A FJ   Krysiak-Baltyn Konrad K   Weinhold Nils N   Taboureau Olivier O   Jensen Thomas Skøt TS   Brunak Søren S  

PLoS computational biology 20100520 5


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 facto  ...[more]

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