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

Abstract

Transcriptomics, high-throughput assays, and adverse outcome pathways (AOP) are promising approaches applied to toxicity monitoring in the 21st century, but development of these methods is challenging for nonmodel organisms and emerging contaminants. For example, Endocrine Disrupting Compounds (EDCs) may cause reproductive impairments and feminization of male bivalves; however, the mechanism linked to this adverse outcome is unknown. To develop mechanism-based biomarkers that may be linked through an AOP, we exposed Mytilus edulis to 17-alpha-ethinylestradiol (5 and 50 ng/L) and 4-nonylphenol (1 and 100 μg/L) for 32 and 39 days. When mussels were exposed to these EDCs, we found elevated female specific transcripts and significant female-skewed sex ratios using a RT-qPCR assay. We performed gene expression analysis on digestive gland tissue using an M. edulis microarray and through network and targeted analyses identified the nongenomic estrogen signaling pathway and steroidogenesis pathway as the likely mechanisms of action for a putative AOP. We also identified several homologues to genes within the vertebrate steroidogenesis pathway including the cholesterol side chain cleavage complex. From this AOP, we designed the Coastal Biosensor for Endocrine Disruption (C-BED) assay which was confirmed in the laboratory and tested in the field.

Endocrine disruption and altered sexual development in aquatic organisms: an invertebrate perspective

“…A review of research on the identification and functioning of invertebrate receptors and pathways, in comparison to those in their vertebrate counterparts can be found in Lafont & Mathieu (2007). More recently Blalock et al (2018) have reported evidence of a cholesterol side chain cleavage enzyme consistent with endogenous production of steroids and oestrogen signalling pathway, as a likely mechanism of action in mussels M. edulis exposed to 17-α-ethinylestradiol and 4-nonylphenol. A ligand-activated ER-like receptor has also been found in reproductive tissue of marine rotifers Brachionus manjavacas (Fontaneto, Giordani, Melone & Serra, 2007) which appears to bind human oestradiol and regulates reproductive output in females.…”

Machine Learning-based Biomarkers Identification and Validation from Toxicogenomics - Bridging to Regulatory Relevant Phenotypic Endpoints

“…Presence of numerous contaminants from various (e.g., wastewater effluent, industrial wastewater discharge, oil and chemical spills, urban runoff, agricultural runoff) sources make the risk assessment and monitoring challenging. 17 High-throughput in vitro assays and AOP-based approach is promising for the assessment of health and ecotoxicological risks from exposure to pollutants and their mixtures. [17][18][19][20][21] Establishment of a quantitative AOP framework through integration of computational modeling with in-vitro assays would require identification and selection of relevant biomarkers from appropriate bioassays that link to risk assessment pathways and phenotypic impacts.…”

“…17 High-throughput in vitro assays and AOP-based approach is promising for the assessment of health and ecotoxicological risks from exposure to pollutants and their mixtures. [17][18][19][20][21] Establishment of a quantitative AOP framework through integration of computational modeling with in-vitro assays would require identification and selection of relevant biomarkers from appropriate bioassays that link to risk assessment pathways and phenotypic impacts. 18,21,66 Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking; 3,51,67 therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity risk monitoring.…”

“…[9][10][11][12][13][14][15][16] Adverse-outcome pathway (AOP) framework is the state-of-the-art approach to link mechanistic toxicity mechanisms with the phenotypic adverse outcome that would enable the assessment of health risk as well as ecotoxicological risks from exposure to pollutants and their mixtures. [17][18][19][20][21] Coalesce of effective biomarkers and proper predicting framework would enable more cost-effective and wider implementation of toxicomics in monitoring of genotoxicity and predict adverse toxic responses. 22-our ability to link molecular-level effects recorded in in-vitro assays to the in-vivo regulatory relevant phenotypic endpoints, or system-level impacts in many fields such as, environmental toxicity, disease prediction and health risk identification.…”

Integration of Computational Toxicology, Toxicogenomics Data Mining, and Omics Techniques to Unveil Toxicity Pathways

“…More recently, toxicogenomics data mining and in silico techniques have been gradually developing as efficient approaches to speed up AOP development. − Premier public resources (such as the Comparative Toxicogenomics Database (CTD), ToxCast, AOP Wiki database, etc.) can provide a chance to generate analytical toxic pathways. ,− Oki and Edwards generated multiple AOP predictions by evaluating the associations of chemical–gene–disease data in the CTD . Jeong et al developed AOPs using mammalian toxicity data obtained from ToxCast .…”