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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass ( Micropterus
Danilo Basili1, Ji-Liang Zhang2,3, John Herbert1
1Institute for Integrative Biology, University of Liverpool , L69 7ZB , Liverpool , United Kingdom.
Abstract:
In recent years, decreases in fish populations have been attributed, in part, to the effect of environmental chemicals on ovarian development. To understand the underlying molecular events we developed a dynamic model of ovary development linking gene transcription to key physiological end points, such as gonadosomatic index (GSI), plasma levels of estradiol (E2) and vitellogenin (VTG), in largemouth bass ( Micropterus salmoides). We were able to identify specific clusters of genes, which are affected at different stages of ovarian development. A subnetwork was identified that closely linked gene expression and physiological end points and by interrogating the Comparative Toxicogenomic Database (CTD), quercetin and tretinoin (ATRA) were identified as two potential candidates that may perturb this system. Predictions were validated by investigation of reproductive associated transcripts using qPCR in ovary and in the liver of both male and female largemouth bass treated after a single injection of quercetin and tretinoin (10 and 100 μg/kg). Both compounds were found to significantly alter the expression of some of these genes. Our findings support the use of omics and online repositories for identification of novel, yet untested, compounds. This is the first study of a dynamic model that links gene expression patterns across stages of ovarian development.
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