Dynamic Photophysiological Stress Response of a Model Diatom to Ten Environmental Stresses.
Zheng-Ke LiWei LiYong ZhangYingyu HuRosie ShewardAndrew J IrwinZoe V FinkelPublished in: Journal of phycology (2021)
Stressful environmental conditions can induce many different acclimation mechanisms in marine phytoplankton, resulting in a range of changes in their photophysiology. Here we characterize the common photophysiological stress response of the model diatom Thalassiosira pseudonana to ten environmental stressors and identify diagnostic responses to particular stressors. We quantify the magnitude and temporal trajectory of physiological parameters including the functional absorption cross-section of PSII (σPSII ), quantum efficiency of PSII, non-photochemical quenching (NPQ), cell volume, Chl a, and carotenoid (Car) content in response to nutrient starvation (nitrogen (N), phosphorus (P), silicon (Si), and iron (Fe)), changes in temperature, irradiance, pH, and reactive oxygen species (ROS) over 5 time points (0, 2, 6, 24, 72 h). We find changes in conditions: temperature, irradiance, and ROS, often result in the most rapid changes in photophysiological parameters (<2 h), and in some cases are followed by recovery. In contrast, nutrient starvation (N, P, Si, Fe) often has slower (6-72 h) but ultimately larger magnitude effects on many photophysiological parameters. Diagnostic changes include large increases in cell volume under Si-starvation, very large increases in NPQ under P-starvation, and large decreases in the σPSII under high light. The ultimate goal of this analysis is to facilitate and enhance the interpretation of fluorescence data and our understanding of phytoplankton photophysiology from laboratory and field studies.
Keyphrases
- reactive oxygen species
- single cell
- dna damage
- cell death
- cell therapy
- room temperature
- human health
- energy transfer
- magnetic resonance
- magnetic resonance imaging
- stem cells
- risk assessment
- computed tomography
- climate change
- mesenchymal stem cells
- molecular dynamics
- electronic health record
- machine learning
- metal organic framework
- oxidative stress
- water quality
- artificial intelligence
- iron deficiency