Brockmeier, Erica K, Basili, Danilo, Herbert, John, Rendal, Cecilie, Boakes, Leigh, Grauslys, Arturas, Taylor, Nadine S, Danby, Emma Butler, Gutsell, Steve, Kanda, Rakesh et al (show 6 more authors)
(2022)
Data-driven learning of narcosis mode of action identifies a CNS transcriptional signature shared between whole organism Caenorhabditis elegans and a fish gill cell line.
SCIENCE OF THE TOTAL ENVIRONMENT, 849.
157666-.
Abstract
With the large numbers of man-made chemicals produced and released in the environment, there is a need to provide assessments on their potential effects on environmental safety and human health. Current regulatory frameworks rely on a mix of both hazard and risk-based approaches to make safety decisions, but the large number of chemicals in commerce combined with an increased need to conduct assessments in the absence of animal testing makes this increasingly challenging. This challenge is catalysing the use of more mechanistic knowledge in safety assessment from both in silico and in vitro approaches in the hope that this will increase confidence in being able to identify modes of action (MoA) for the chemicals in question. Here we approach this challenge by testing whether a functional genomics approach in C. elegans and in a fish cell line can identify molecular mechanisms underlying the effects of narcotics, and the effects of more specific acting toxicants. We show that narcosis affects the expression of neuronal genes associated with CNS function in C. elegans and in a fish cell line. Overall, we believe that our study provides an important step in developing mechanistically relevant biomarkers which can be used to screen for hazards, and which prevent the need for repeated animal or cross-species comparisons for each new chemical.
| Item Type: | Article |
|---|---|
| Additional Information: | Source info: STOTEN-D-22-02612 |
| Uncontrolled Keywords: | Narcosis, Omics, Biomarkers, Bioinformatics, Mode of action, Cross-species analysis |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 22 Aug 2022 09:15 |
| Last Modified: | 18 Jan 2023 20:47 |
| DOI: | 10.1016/j.scitotenv.2022.157666 |
| Open Access URL: | https://doi.org/10.1016/j.scitotenv.2022.157666 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3161962 |
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