Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker...

Area of Science:

• Genomics
• Computational Biology
• Endocrinology

Background:

• Microarray profiling is crucial for studying chemical effects in high-throughput formats.
• Estrogen receptor alpha (ERα) is a key target for endocrine disrupting chemicals.
• Identifying ERα modulators is vital for chemical safety assessment.

Purpose of the Study:

• To develop and validate a computational method for identifying ERα modulators using whole-genome microarray data.
• To establish an ERα gene expression biomarker for predicting chemical interactions with ERα.
• To assess the biomarker's predictive accuracy against known ERα agonists and antagonists.

Main Methods:

• Identification of ERα biomarker genes based on consistent expression in ERα-positive MCF-7 cells after exposure to ERα modulators.
• Validation of direct ERα regulation using ESR1 gene knockdown and ChIP-seq analysis.
• Evaluation of the biomarker's predictive performance using the Running Fisher algorithm on diverse gene expression datasets.

Main Results:

• The ERα biomarker achieved high balanced accuracies: 94% for activation and 93% for suppression in 141 comparisons.
• It correctly classified 86% of ER reference chemicals, including weak agonists.
• Biomarker predictions closely mirrored results from 18 in vitro high-throughput screening assays, with 95% and 98% accuracy for activation/suppression on 114 chemicals.

Conclusions:

• The developed ERα gene expression biomarker is a robust and accurate tool for identifying ERα modulators in large microarray datasets.
• This method facilitates the screening of numerous chemicals for potential endocrine-disrupting activity.
• The biomarker enhances the reliability of chemical safety assessments by leveraging gene expression data.