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Fish Connectivity Mapping: Linking Chemical Stressors by Their MOA-Driven Transcriptomic Profiles
Citation:
Wang, R., A. Biales, N. Garcia-Reyero, E. Perkins, Dan Villeneuve, G. Ankley, AND D. Bencic. Fish Connectivity Mapping: Linking Chemical Stressors by Their MOA-Driven Transcriptomic Profiles. BMC Genomics. BioMed Central Ltd, London, Uk, 17:84, (2016). https://doi.org/10.1186/s12864-016-2406-y
Impact/Purpose:
The purpose of this study is to explore a data-driven approach to develop "-omics"-based ecotoxicological applications for bio-monitoring, exposure assessment, and toxicity extrapolation. Modeled on human connectivity mapping (Cmap) developed in biomedical science, fish Cmap proved to be a cost- and time-efficient tool, particularly within species. With the rapidly growing "-omics" data in public repositories, Cmap could be easily scaled up to provide a greater chemical coverage and become more powerful.
Description:
Background: modeled on human connectivity mapping (Cmap), this study was undertaken to investigate the potential applications of Cmap approach in ecotoxicology. Over 3500 zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) transcriptomic profiles, each associated with one of several dozen chemical treatment conditions, were compiled into three distinct collections of rank-ordered gene lists (ROGLs) by species and microarray platforms. Individual query signatures, each consisting of multiple gene probes differentially expressed in a chemical condition, were used to interrogate the reference ROGLs. Results: Informative connections were established at high success rates within species when, as defined by their mechanisms of action (MOAs), both query signatures and ROGLs were associated with the same or similar chemicals. In other words, a simple query signature functioned effectively as an exposure biomarker, without going through a typical time-consuming process of development and validation. More importantly, a large reference database of ROGLs also enabled a query signature to cross- interrogate other chemical conditions with overlapping MOAs, leading to novel groupings and subgroupings of seemingly unrelated chemicals at a finer resolution. This approach confirmed the identities of several estrogenic chemicals, as well as a polycyclic aromatic hydrocarbon and a neuro-toxin, in the largely uncharacterized water samples near several waste water treatment plants, and thus demonstrates its future potential in real world applications. Conclusions: The power of Cmap should grow along with the chemical coverage of its ROGLs, making it a framework easily scalable in the future. For toxicity extrapolation across fish species, however, a sufficient number of gene expression profiles linked to chemical conditions common to multiple fish species are needed to conduct a more thorough feasibility study of interspecific Cmap.
URLs/Downloads:
DOI: Fish Connectivity Mapping: Linking Chemical Stressors by Their MOA-Driven Transcriptomic Profiles