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In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass (Micropterus salmoides)

Author:
Basili, Danilo, Zhang, Ji-Liang, Herbert, John, Kroll, Kevin, Denslow, Nancy D., Martyniuk, Christopher J., Falciani, Francesco, Antczak, Philipp
Source:
Environmental science & technology 2018 v.52 no.13 pp. 7553-7565
ISSN:
1520-5851
Subject:
Micropterus salmoides, animal ovaries, databases, dynamic models, endocrine-disrupting chemicals, environmental science, estradiol, females, fish communities, gene expression, gene expression regulation, genes, gonadosomatic index, liver, males, ovarian development, prediction, quantitative polymerase chain reaction, quercetin, transcription (genetics), transcriptomics, vitellogenin
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.
Agid:
6008507