Using transcriptomic profiles in the diatom Phaeodactylum tricornutum to identify and prioritize stressors.

The transcriptomic profile of the marine diatom, Phaeodactylum tricornutum, exposed to several ecologically relevant stressors, was used to develop toxicity identification evaluation (TIE)-like gene expression assays. Algal growth inhibition was measured by flow cytometry to determine exposure concentrations that elicited a sublethal toxic response. P. tricornutum was exposed to concentrations of copper (2 μg L⁻¹), cadmium (5 μg L⁻¹), silver (20 μg L⁻¹), simazine (75 μg L⁻¹), the water accommodated fraction (WAF) of weathered crude oil (5 mg L⁻¹), 50 μg L⁻¹ ammonia, a decreased salinity treatment (15‰), and a mixture exposure of ammonia, decreased salinity and cadmium (10 μg L⁻¹). Analysis of the gene expression via microarray indicated that unique transcriptomic signals were generated for each of the individual treatments. Transcriptomic profiles of ammonia and the mixture treatment overlapped substantially. Photosynthesis related transcripts were altered in the simazine (herbicide) treatment. A transcript involved in degrading hydrocarbons, dioxygenase, had increased abundance after crude oil exposure. Overall, transcriptomic responses in the different treatments were associated with stress responses, membrane transport, transcription and translation and could be linked to contaminant mode of action. The transcriptomic profiles were used to design real-time (quantitative) polymerase chain reaction (qPCR) assays that would link changes in transcript abundance to a particular stressor in a TIE-based approach. At least one transcript for each contaminant tested (copper, cadmium, silver, salinity and ammonia) responded exclusively to that contaminant. With further development of additional transcriptomic markers for each contaminant, this new approach has potential to enhance traditional toxicology bioassays by providing additional lines of evidence to identify biologically relevant stressors within a contaminated ecosystem based on changes in the transcriptomic profile.