Literature DB >> 31325560

Identification of potential endocrine disrupting chemicals using gene expression biomarkers.

J Christopher Corton1, Nicole C Kleinstreuer2, Richard S Judson3.   

Abstract

Recent technological advances have moved the field of toxicogenomics from reliance on microarray platforms to high-throughput transcriptomic (HTTr) technologies that measure global gene expression. Gene expression biomarkers are emerging as useful tools for interpreting gene expression profiles to identify perturbations of targets of xenobiotic chemicals including those that act as endocrine disrupting chemicals (EDCs). Gene expression biomarkers are lists of similarly-regulated genes identified in global gene expression comparisons of cells or tissues 1) exposed to known agonists or antagonists of the transcription factor (TF) and 2) after expression of the TF itself is knocked down/knocked out or overexpressed. Estrogen receptor α (ERα) and androgen receptor (AR) biomarkers have been shown to be very accurate at identifying both agonists (94-97%) and antagonists (93-98%) in microarray data derived from human breast or prostate cancer cell lines. Importantly, the biomarkers have been shown to accurately replicate the results of computational models that predict ERα or AR modulation using multiple ToxCast HT screening assays. An integrated screening strategy using sets of biomarkers that simultaneously predict various EDC targets in relevant cell lines should simplify chemical screening without sacrificing accuracy. The biomarker predictions can be put into the context of the adverse outcome pathway framework to help prioritize chemicals with the greatest risk of potential adverse outcomes in the endocrine systems of animals and people. Published by Elsevier Inc.

Entities:  

Keywords:  Androgen receptor; Biomarkers; Endocrine disrupting chemicals; Estrogen receptor; Nuclear receptors; Toxicogenomics

Year:  2019        PMID: 31325560     DOI: 10.1016/j.taap.2019.114683

Source DB:  PubMed          Journal:  Toxicol Appl Pharmacol        ISSN: 0041-008X            Impact factor:   4.219


  5 in total

1.  Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens.

Authors:  Thomas Hill; John Rooney; Jaleh Abedini; Hisham El-Masri; Charles E Wood; J Christopher Corton
Journal:  Toxicol Sci       Date:  2020-09-01       Impact factor: 4.849

2.  Modernization of chemical risk assessment to make use of novel toxicological data.

Authors: 
Journal:  Toxicol Appl Pharmacol       Date:  2020-03-18       Impact factor: 4.460

3.  GC-MS Profile of Hua-Feng-Dan and RNA-Seq Analysis of Induced Adaptive Responses in the Liver.

Authors:  Jia-Jia Liu; Yan Liang; Ya Zhang; Rui-Xia Wu; Ying-Lian Song; Feng Zhang; Jing-Shan Shi; Jie Liu; Shang-Fu Xu; Zhang Wang
Journal:  Front Pharmacol       Date:  2022-03-08       Impact factor: 5.810

4.  Potential Molecular Target Prediction and Docking Verification of Hua-Feng-Dan in Stroke Based on Network Pharmacology.

Authors:  Ping Yang; Haifeng He; Shangfu Xu; Ping Liu; Xinyu Bai
Journal:  Evid Based Complement Alternat Med       Date:  2020-10-28       Impact factor: 2.629

5.  A gene expression biomarker for predictive toxicology to identify chemical modulators of NF-κB.

Authors:  Katharine L Korunes; Jie Liu; Ruili Huang; Menghang Xia; Keith A Houck; J Christopher Corton
Journal:  PLoS One       Date:  2022-02-02       Impact factor: 3.240

  5 in total

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