Improved Algal Toxicity Test System for Robust <i>Omics</i>-Driven Mode-of-Action Discovery in <i>Chlamydomonas reinhardtii</i>

Algae are key components of aquatic food chains. Consequently, they are internationally recognised test species for the environmental safety assessment of chemicals. However, existing algal toxicity test guidelines are not yet optimized to discover molecular modes of action, which require highly-rep...

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Bibliographic Details
Main Authors: Stefan Schade, Emma Butler, Steve Gutsell, Geoff Hodges, John K. Colbourne, Mark R. Viant
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Metabolites
Subjects:
Online Access:https://www.mdpi.com/2218-1989/9/5/94
Description
Summary:Algae are key components of aquatic food chains. Consequently, they are internationally recognised test species for the environmental safety assessment of chemicals. However, existing algal toxicity test guidelines are not yet optimized to discover molecular modes of action, which require highly-replicated and carefully controlled experiments. Here, we set out to develop a robust, miniaturised and scalable <i>Chlamydomonas reinhardtii</i> toxicity testing approach tailored to meet these demands. We primarily investigated the benefits of synchronised cultures for molecular studies, and of exposure designs that restrict chemical volatilisation yet yield sufficient algal biomass for omics analyses. Flow cytometry and direct-infusion mass spectrometry metabolomics revealed significant and time-resolved changes in sample composition of synchronised cultures. Synchronised cultures in sealed glass vials achieved adequate growth rates at previously unachievably-high inoculation cell densities, with minimal pH drift and negligible chemical loss over 24-h exposures. Algal exposures to a volatile test compound (chlorobenzene) yielded relatively high reproducibility of metabolic phenotypes over experimental repeats. This experimental test system extends existing toxicity testing formats to allow highly-replicated, <i>omics</i>-driven, mode-of-action discovery.
ISSN:2218-1989