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Transcriptomic and Network Analyses Reveals Mechanistic-Based Biomarkers of Endocrine Disruption in the Marine Mussel, Mytilus edulis

ABSTRACT: Coastal monitoring in the 21st century faces challenges as measuring exposure using traditional toxicity approaches is not practical or financially feasible for the numerous contaminants entering oceans. Transcriptomics, high-throughput assays, and adverse outcome pathways (AOP) have been one approach to overcome these obstacles but development of these methods is challenging for non-model organisms and contaminants with unknown mechanisms. Endocrine Disrupting Compounds (EDCs) have been found to cause reproductive impairments such as intersex in bivalves, however, the mechanism linked to this adverse outcome (AO) is unknown as the estrogen receptor (ER) has been identified to be unresponsive to EDCs. To develop mechanism-based biomarkers that may be linked through an AOP we exposed Mytilus edulis to 17-alpha-ethinylestradiol (5 and 50ng/L) and 4-nonylphenol (1 and 100μg/L), and performed gene expression analysis on digestive gland tissue using a M. edulis microarray. Through network and targeted analyses, we identified the non-genomic estrogen signaling pathway and steroidogenesis pathway as the likely mechanism of action and identified biomarkers and potential key events (KE) for an AOP of endocrine disruption in marine mussels. The mechanism-based biomarkers were validated in the laboratory, tested in the field, and were responsive in areas known to be contaminated with EDCs.

ORGANISM(S): Mytilus edulis

PROVIDER: GSE111836 | GEO | 2018/03/15

REPOSITORIES: GEO

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Project description:Adverse outcome pathways (AOP) contain summarizing sequences of key events (KE) relating toxicant exposures to specific adverse outcomes (AO) observed in exposed populations. Quantitative AOPs (qAOP) provide frameworks to assess AO risks based on KE quantification and the modeling of their relationships. This work aimed to develop the quantitative component of AOP 411 <Oxidative stress leading to decreased lung function>. To examine the pertinence of the resulting qAOP-based risk assessment approach, we applied it to predict the medium-term lung function evolution after switching from combustible cigarettes (CC) to heated tobacco products (HTP). We used publicly available data for oxidative stress (in vitro), ciliary beating frequency (in vitro), mucociliary clearance (human), and lung function (human). Following the course of smoking-related lung function decline (Fletcher and Peto, 1977, PMID 871704), we assumed a non-monotonic time evolution where the risk of the AO <decreased lung function> begins to decline after quitting smoking CC or switching to HTP use. Consequently, we designed our qAOP to evaluate the relative reduction (RR) of AO risk for HTP switching compared to the two scenarios of continuing or quitting CC consumption. We developed both data- and physiology-based KE relationship (KER) models. Despite sparse data with respect to dose- and time-response relationships, we successfully connected the measured KEs through in silico KER models. We then used the resulting qAOP to evaluate the exposure effects of switching from CC smoking to HTP use along AOP 411. This approach enabled in silico prediction of the AO RR for which in vivo data were not yet available. In summary, our qAOP provides a multiscale mechanistic approach to evaluate medium-term lung function evolution for the use case of switching from CC smoking to HTP use. The presented approach demonstrates how 21st Century Toxicology principles such as in vitro testing and in silico predictions can be integrated to achieve a meaningful risk assessment approach. It also represents a starting point for integrating additional AOP into an extended AOP network that includes other mechanisms leading to decreased lung function.

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Transcriptomic and Network Analyses Reveals Mechanistic-Based Biomarkers of Endocrine Disruption in the Marine Mussel, Mytilus edulis

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