Structural Identification of Metalloproteomes in Marine Diatoms, an Efficient Algae Model in Toxic Metals Bioremediation

The biosorption of pollutants using microbial organisms has received growing interest in the last decades. Diatoms, the most dominant group of phytoplankton in oceans, are (i) pollution tolerant species, (ii) excellent biological indicators of water quality, and (iii) efficient models in assimilation and detoxification of toxic metal ions. Published research articles connecting proteomics with the capacity of diatoms for toxic metal removal are very limited. In this work, we employed a structural based systematic approach to predict and analyze the metalloproteome of six species of marine diatoms: Thalassiosira pseudonana, Phaeodactylum tricornutum, Fragilariopsis cylindrus, Thalassiosira oceanica, Fistulifera solaris, and Pseudo-nitzschia multistriata. The results indicate that the metalloproteome constitutes a significant proportion (~13%) of the total diatom proteome for all species investigated, and the proteins binding non-essential metals (Cd, Hg, Pb, Cr, As, and Ba) are significantly more than those identified for essential metals (Zn, Cu, Fe, Ca, Mg, Mn, Co, and Ni). These findings are most likely related to the well-known toxic metal tolerance of diatoms. In this study, metalloproteomes that may be involved in metabolic processes and in the mechanisms of bioaccumulation and detoxification of toxic metals of diatoms after exposure to toxic metals were identified and described.

Elucidating the Role of Catalytic Amino Acid Residues in the Peptide-Mediated Silica Oligomerization Reaction Mechanism

Understanding the detailed mechanism by which the proteins of marine diatoms such as silaffins are able to control the morphology of silica oligomers has eluded synthetic chemists and materials scientists...

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

Over recent decades, the world has experienced the adverse consequences of uncontrolled development of multiple human activities. In recent years, the total production of chemicals has been composed of environmentally harmful compounds, the majority of which have significant environmental impacts. These emerging contaminants (ECs) include a wide range of man-made chemicals (such as pesticides, cosmetics, personal and household care products, pharmaceuticals), which are of worldwide use. Among these, several ECs raised concerns regarding their ecotoxicological effects and how to assess them efficiently. This is of particular interest if marine diatoms are considered as potential target species, due to their widespread distribution, being the most abundant phytoplankton group in the oceans, and also being responsible for key ecological roles. Bio-optical ecotoxicity methods appear as reliable, fast, and high-throughput screening (HTS) techniques, providing large datasets with biological relevance on the mode of action of these ECs in phototrophic organisms, such as diatoms. However, from the large datasets produced, only a small amount of data are normally extracted for physiological evaluation, leaving out a large amount of information on the ECs exposure. In the present paper, we use all the available information and evaluate the application of several machine learning and deep learning algorithms to predict the exposure of model organisms to different ECs under different doses, using a model marine diatom (Phaeodactylum tricornutum) as a test organism. The results show that 2D convolutional neural networks are the best method to predict the type of EC to which the cultures were exposed, achieving a median accuracy of 97.65%, while Rocket is the best at predicting which concentration the cultures were subjected to, achieving a median accuracy of 100%.

Occurrence of structural aluminum (Al) in marine diatom biological silica: Visible evidence from microscopic analysis

The global marine biogeochemical cycle of aluminum (Al) is believed to be driven by marine diatoms, due to the uptake of dissolved Al (DAl) by living diatoms from surface seawater. The occurrence of Al in diatom biogenic silica (BSi) can inhibit the dissolution of BSi, thus benefiting the effects of the ballast role of diatoms in the biological pump and forming a coupled Si-Al biogeochemical cycle. However, the occurrence mechanism of Al in marine diatoms is still unclear. In particular, whether or not Al is incorporated into the structure of BSi of living diatoms is unrevealed, resulting in difficulties in understanding the biogeochemical behaviors of Al. In this study, Thalassiosira weissflogii, a widely distributed marine diatom in marginal seas, was selected as the model to evaluate the occurrence of structural Al in BSi based on culturing experiments with the addition of DAl. The structural Al in BSi was detected by combining focused ion beam (FIB) scanning electron microscopy and energy dispersive X-ray spectroscopy (EDS) mapping analysis. Direct evidence of structural Al in living BSi was obtained for the first time. The distribution and content of this Al were revealed by the EDS-mapping analysis. The structural Al in the BSi exhibited a homogeneous distribution, and the average Al / Si atomic ratio obtained through the FIB-EDS mapping analysis was 0.011. The effects of structural Al on BSi dissolution-inhibition are discussed based on the content of this Al. The fundamental results indicate the significant contribution of marine diatoms to the biogeochemical migration of marine Al.

Intraspecific variation in metal tolerance modulate competition between two marine diatoms

Despite widespread metal pollution of coastal ecosystems, little is known of its effect on marine phytoplankton. We designed a co-cultivation experiment to test if toxic dose–response relationships can be used to predict the competitive outcome of two species under metal stress. Specifically, we took into account intraspecific strain variation and selection. We used 72 h dose–response relationships to model how silver (Ag), cadmium (Cd), and copper (Cu) affect both intraspecific strain selection and competition between taxa in two marine diatoms (Skeletonema marinoi and Thalassiosira baltica). The models were validated against 10-day co-culture experiments, using four strains per species. In the control treatment, we could predict the outcome using strain-specific growth rates, suggesting low levels of competitive interactions between the species. Our models correctly predicted which species would gain a competitive advantage under toxic stress. However, the absolute inhibition levels were confounded by the development of chronic toxic stress, resulting in a higher long-term inhibition by Cd and Cu. We failed to detect species differences in average Cu tolerance, but the model accounting for strain selection accurately predicted a competitive advantage for T. baltica. Our findings demonstrate the importance of incorporating multiple strains when determining traits and when performing microbial competition experiments.