Toxicity Going Nano: Ionic Versus Engineered Cu Nanoparticles Impacts on the Physiological Fitness of the Model Diatom Phaeodactylum tricornutum

Abstract

Increasing input of Metal Engineered Nano Particles (MeENPs) in marine ecosystems has raised concerns about their potential toxicity on phytoplankton. Given the lack of knowledge on MeENPs impact on these important primary producers, the effects of Copper Oxide (CuO) ENPs on growth, physiology, pigment profiles, fatty acid (FA) metabolism, and oxidative stress were investigated in the model diatom Pheodactylum tricornutum, to provide suitable biomarkers of CuO ENP exposure versus its ionic counterpart. Diatom growth was inhibited by CuO ENPs but not Ionic Cu, suggesting CuO ENP cytotoxicity. Pulse Modulated Amplitude (PAM) phenotyping evidenced a decrease in the electron transport energy flux, pointing to a reduction in chemical energy generation following CuO ENPs exposure, as well as an increase in the content of the non-functional Cu-substituted chlorophyll a (CuChl a). A significant decrease in eicosapentaenoic acid (C20:5) associated with a significant rise in thylakoid membranes FAs reflected the activation of counteractive measures to photosynthetic impairment. Significant increase in the omega 6/omega 3 ratio, underline expectable negative repercussions to marine food webs. Increased thiobarbituric acid reactive substances reflected heightened oxidative stress by CuO ENP. Enhanced Glutathione Reductase and Ascorbate Peroxidase activity were also more evident for CuO ENPs than ionic Cu. Overall, observed molecular changes highlighted a battery of possible suitable biomarkers to efficiently determine the harmful effects of CuO ENPs. The results suggest that the occurrence and contamination of these new forms of metal contaminants can impose added stress to the marine diatom community, which could have significant impacts on marine ecosystems, namely through a reduction of the primary productivity, oxygen production and omega 6 production, all essential to sustain heterotrophic marine life.

Artificial Intelligence Meets Marine Ecotoxicology: Applying Deep Learning to Bio-Optical Data from Marine Diatoms Exposed to Legacy and Emerging Contaminants

“…In the present work, the authors aim to study the applicability of the obtained fluorescence data in automatic machine-learning-based systems, for ecotoxicological classification of marine diatoms-based exposure trials, and, thus, the physiological responses of the cells will not be discussed in the present work, as this is out of the scope of the present work. The physiological effects of each contaminant are discussed elsewhere [6][7][8]16,23,24]. Nevertheless, and in order to better understand the magnitude of the ecotoxicological effects observed in the cultures, relative growth inhibition and half-maximal inhibitory concentrations were also determined as proxy measures of the toxicity effects observed in the cultures, that are unequivocally connected with the photochemical results (Table 2).…”

“…Table 1 contains some details regarding this dataset, including the identification of the contaminants and different concentrations used, and the distribution of samples between contaminants and concentrations. Further details on the techniques and protocols used for obtaining the data can be found in [6][7][8]16,24]. Cell density was evaluated in all experimental reactors at the end of the exposure trials and the percentage of growth inhibition towards the respective control condition was calculated, as well as the half-maximal inhibitory concentration (IC50), according to the OECD (2011) guidelines.…”

“…Any disturbance at this level has inevitable impacts on the whole marine ecosystem. Contaminants toxic effects are known to have severe and specific influence on these organisms, especially impairing their photosynthetic metabolism [ 6 , 7 , 8 , 9 , 10 ]. Having both biochemical and biophysical components allows the photosynthetic mechanisms to be addressed remotely [ 11 ].…”

“…Any disturbance at this level has inevitable impacts on the whole marine ecosystem. Contaminants toxic effects are known to have severe and specific influence on these organisms, especially impairing their photosynthetic metabolism [6][7][8][9][10].…”

“…These facts are the cornerstone of the pioneering research field of toxicophenomics, which merges plant phenomics, measuring traits, such as plant growth and performance using non-invasive technologies, with ecotoxicology, shifting the use of these phenotyping tools to address contaminant induced stress in autotrophs. Optical techniques to disclose different groups of samples exposed to different degrees of contamination has been applied in marine phototrophs with a high degree of efficiency, in both ecotoxicological trials and under field conditions [6,8,16], and using communities as meta-organisms instead of single species [17]. The application of these bio-optical technologies produces large data packages with physiological interest, regarding the effects of a given compound in an autotrophic organism, and also with a high volume of data points that can be used by machine learning techniques, to produce classifiers of the toxicity degree to which organisms are exposed [18].…”