Fishing for contaminants: identification of three mechanism specific transcriptome signatures using Danio rerio embryos

Abstract

In ecotoxicology, transcriptomics is an effective way to detect gene expression changes in response to environmental pollutants. Such changes can be used to identify contaminants or contaminant classes and can be applied as early warning signals for pollution. To do so, it is important to distinguish contaminant-specific transcriptomic changes from genetic alterations due to general stress. Here we present a first step in the identification of contaminant class-specific transcriptome signatures. Embryos of zebrafish (Danio rerio) were exposed to three substances (methylmercury, chlorpyrifos and Aroclor 1254, each from 24 to 48 hpf exposed) representing sediment typical contaminant classes. We analyzed the altered transcriptome to detect discriminative genes significantly regulated in reaction to the three applied contaminants. By comparison of the results of the three contaminants, we identified transcriptome signatures and biologically important pathways (using Cytoscape/ClueGO software) that react significantly to the contaminant classes. This approach increases the chance of finding genes that play an important role in contaminant class-specific pathways rather than more general processes.

Transcriptomic Points of Departure Calculated from Rainbow Trout Gill, Liver, and Gut Cell Lines Exposed to Methylmercury and Fluoxetine

“…Overall, the enriched pathways were related to various functions including stress response (adenosine monophosphateactivated protein kinase signaling pathway, FoxO signaling pathway), synthesis and metabolism of biomolecules (glutathione, carbohydrates, steroids), promotion of cell growth, proliferation, and metabolism (PI3K-Akt signaling pathway), DNA repair, mineral absorption, immune response (cytokine-cytokine receptor interaction and antigen processing and presentation), muscle contraction (regulation of actin cytoskeleton), fatty acid and energy metabolism (fatty acid elongation, carbon metabolism), and cell death (apoptosis, ferroptosis, necroptosis). These pathways have similarly been identified in previous transcriptomic studies concerning methylmercury exposure in fish species including rainbow trout (Liu et al, 2013), zebrafish (DeBofsky et al, 2018;Hausen et al, 2018;Zhu et al, 2019), Atlantic cod (Yadetie et al, 2013), and European cusk (Olsvik et al, 2021). Furthermore, an overall examination of the Comparative Toxicogenomic Database (CTD; North Carolina State University, 2022) showed that many of these same pathways were dysregulated in response to exposure to methylmercury compounds (i.e., the top three were metabolism, immune system, and signal transduction).The significantly enriched pathways with the lowest BMD values (i.e., those with pathway BMD values below 10 ppb) included those involved in mineral absorption, metabolism, and biosynthesis (e.g., glutathione metabolism and steroid biosynthesis), and ferroptosis.…”

PI3K and Inhibitor of Apoptosis Proteins Modulate Gentamicin- Induced Hair Cell Death in the Zebrafish Lateral Line

“…The default EASE threshold of 0.1.0 was used. Global co-expression networks were then constructed using the Cytoscape app GeneMANIA ( Campanaro et al, 2007 ; Montojo et al, 2010 ; Vied et al, 2014 ; Hausen et al, 2017 ). The desired organism used was human, with the options to display up to 50 related genes, 20 related attributes, and automatic weighting.…”

Towards a holistic and solution-oriented monitoring of chemical status of European water bodies: how to support the EU strategy for a non-toxic environment?

“…Open digital repositories can be used to archive current-state full scan HRMS analysis for retrospective and joint exploitation [ 53 , 54 ]. At the same time, a bunch of bio-informatics tools including innovative clustering algorithms from metabolomics and genomics including independent component analysis (ICA) and cytoscape/ClueGO pathway analysis [ 55 , 56 ], hierarchical clustering, K -means, self-organizing maps and fuzzy clustering [ 57 , 58 ] are available and help unravel big and complex analytical datasets. It may be assumed that complex environmental mixtures in water bodies are not just randomly composed but the result of confluence of source-related patterns modified by environmental fate.…”