Exploring the biophysicochemical alteration of green alga Asterococcus superbus interactively affected by nanoparticles, triclosan and illumination

Interactive effects of nanoplastics, multi-contaminants, and environmental conditions on prairie aquatic ecosystems: A factorial composite toxicity analysis within a Canadian context

Limited data exist on the interactions between nanoplastics (NPs) and co-contaminants under diverse environmental conditions. Herein, a factorial composite toxicity analysis approach (FCTA) was developed to analyze the time-dependent composite effects of NPs (0 ∼ 60 mg/L), copper (Cu, 0.2 ∼ 6 mg/L) and phenanthrene (PHE, 0.001 ∼ 1 mg/L) on microalgae under diverse pH (6.7 ∼ 9.1), dissolved organic matter (DOM, 1.5 ∼ 25.1 mg/L), salinity (1 ∼ 417 mg/L) and temperature (23 ∼ 33 °C) within the Canadian prairie context. The toxic mechanism was revealed by multiple toxic endpoints. The combined toxicity of NPs, Cu and PHE within prairie aquatic ecosystems was assessed by the developed FCTA-multivariate regression model. Contrary to individual effects, NPs exhibited a promotional effect on microalgae growth under complex environmental conditions. Although Cu and PHE were more hazardous, NPs mitigated their single toxicity. Environmental conditions and exposure times significantly influenced the main effects and interactions of NPs, Cu and PHE. The synergistic effect of NPs*Cu and NPs*PHE on microalgae growth became antagonistic with increased pH or DOM. Microalgae in the Souris River, Saskatchewan, were projected to suffer the most toxic effects. Our findings have significant implications for the risk management of NPs.

Identifying resistance molecules in TiO2 nanoparticle-tolerant strains to facilitate the development of strategies for combating TiO2 nanoparticle pollution

The severity of environmental pollution caused by TiO2 nanoparticles (nTiO2) is increasing, highlighting the urgent need for the development of strategies to combat nTiO2 pollution. Insights into resistance molecules from nTiO2-tolerant strains may facilitate such development. In this study, we utilized multi-omics, genetic manipulation, physiological and biochemical experiments to identify relevant resistance molecules in two strains (Physarum polycephalum Z259 and T83) tolerated to mixed-phase nTiO2 (MPnTiO2). We discovered that a competing endogenous RNA (ceRNA) network, comprising one long non-coding RNA (lncRNA), four microRNAs, and nine mRNAs, influenced metabolic rearrangement and was associated with significant resistance in these strains. Additionally, we found that the lncRNA in the ceRNAs network and certain small-weight metabolites associated with the ceRNA exhibited notable mitigation effects not only against MPnTiO2 but also against other types of nTiO2 with broad species applicability (they significantly improved the resistance of several non-nTiO2-tolerant cells/organisms in the laboratory and reduced cell damage of non-nTiO2-tolerant cells/organisms in highly suspected nTiO2-polluted areas of the real world). In summary, this study deepens our understanding of nTiO2-tolerant strains, provides valuable insights into resistance molecules in these strains, and facilitates the development of strategies to combat nTiO2 pollution.

Binary toxicity of polystyrene nanoplastics and polybrominated diphenyl ethers to Arctic Cyanobacteria under ambient and future climates

Cyanobacteria are the predominant biota in the Arctic. Interactive effects on Arctic cyanobacteria between climate-change-shifting parameters and anthropogenic contaminants are largely unknown. We utilized a fractional factorial experiment and Arctic cyanobacteria Pseudanabaena biceps Strain PCCC_O-153 to capture the complexity of interacting climate factors, nano-polystyrene (nano-PS) and 2,2´,4,4´-tetrabromodipenyl ether (BDE-47). The short-term binary toxicity of nano-PS and BDE-47 was then examined through experiments, toxicity units, and reference models. The toxic mechanism was further revealed through biochemical analyses and multivariate statistics. We found that BDE-47 and nano-PS had more hazardous effects than changing climate conditions. The mixture had antagonistic effects on PCCC_O-153, attributing to the aggregation of nano-PS, the adsorption of BDE-47, and the wrapping of both contaminants by released extracellular polymeric substances. Binary toxicity was caused by the chain reactions triggered by combining individual contaminants. Total protein was a sensitive target and positively correlated to chlorophyll pigment. Oxidative stress for the mixture mainly resulted from the presence of nano-PS. This is the first study to access the hazardous effects of a mixture of anthropogenic contaminants on Arctic cyanobacteria under ambient and future climates.

Probing Herbicide Toxicity to Algae (Selenastrum capricornutum) by Lipid Profiling with Machine Learning and Microchip/MALDI-TOF Mass Spectrometry

Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS)-based lipid profiling is a powerful method to study the cytotoxicity of chemical exposure to microorganisms at the single cell level. We report here a combined approach of machine learning (ML) and microchip-based MALDI-time of flight (TOF) mass spectrometry to investigate the cytotoxic effect of herbicides on algae through single cell lipid profiling. Algal species Selenastrum capricornutum was chosen as the target system, and its exposure to different doses of common chemical herbicides and the resulting cytotoxic behaviors under various stress conditions were characterized. A lipid library for S. capricornutum has been established with 63 identified lipids that include glycosyldiacylglycerols and triacylglycerols. We demonstrated that major alternations occurred for lipids with functional groups of digalactosyldiacylglycerol (DGDG), triacylglycerol (TAG), and monogalactosyldiacylglycerol (MGDG). DGDG was shown to decrease upon exposure to herbicides of norflurazon and atrazine, while some MGDG and TAG lipids would increase for norflurazon. Compared to other algae, S. capricornutum was more strongly impacted by norflurazon than atrazine while the latter was observed to have a greater effect on C. reinhardtii. Machine learning algorithms have been applied to improve the classification of herbicide impact and help identify lipid species affected by the chemical exposure. A total of 69 machine learning models were trained and tested for the identification of ideal algorithms in the classification process, in which flexible discriminant analysis and support vector machine model were found to be the most accurate and consistent. The ML algorithms accurately differentiated herbicide impact and have identified cytotoxic differences that were previously hidden. The results suggest that herbicides express toxicity among different algae likely on the basis of metabolic differences. The ML-assisted method proves to be highly effective and can provide an advanced technological platform for probing cytotoxicity for bacterial species and in metabolic pathway analysis.

The response of three typical freshwater algae to acute acid stress in water

The effect of acidic pH conditions on the physiological response of three typical freshwater algae, Chlorella vulgaris, Microcystis aeruginosa and Scenedesmus quadricauda, was investigated in this study. The results of the cultivation experiment indicated that the mortality of the three algae in the logarithmic growth phase increased with increasing exposure time and acidity under acute acidic conditions. The tolerance of S. quadricauda was stronger than that of the other two species under long-term (6 h) exposure to the same acidity; in contrast, C. vulgaris exhibited the greatest tolerance under short-term exposure conditions. The decrease in chlorophyll a (Chl a) content indicated that the photosynthesis of algae was inhibited under acid stress and that the algae could not continue to grow normally. This was consistent with the changes in lipid peroxidation and antioxidant enzyme activity, which were reflected by the malondialdehyde (MDA) content and superoxide dismutase (SOD) activity under acid stress, respectively. The results of this study demonstrated that when exposed to acute acidic conditions, the tolerance of three typical freshwater algae to acidity was significantly different. These findings provide valuable information for poorly mixed acidification operations designed to adjust the pH in lakes, reservoirs, or intake pipes of purification plants.

Monitoring of the yogurt fermentation process based on a rapid bio-luminescent chiral pattern recognition of amino acids

A luminescent bacterial sensor array was established for the discrimination of multiple chiral amino acids and the monitoring of the yogurt fermentation process.

Transcriptional response of a green alga (Raphidocelis subcapitata) exposed to triclosan: photosynthetic systems and DNA repair

Recent studies show that triclosan (TCS) exposure causes reduction in pigments, suppression of photosynthesis, and induction of oxidative stress at the physiological level, resulting in morphological alteration and growth inhibition in algae including Raphidocelis subcapitata (R. subcapitata, a freshwater model green alga). However, the underlying molecular mechanisms remain to be elucidated, especially at environmentally relevant concentrations. The present study uncovered the transcriptional profiles and molecular mechanisms of TCS toxicity in R. subcapitata using next-generation sequencing. The algal growth was drastically inhibited following a 7-day exposure at both 75 and 100 μg/L TCS, but not at 5 μg/L (environmentally realistic level). The transcriptomic analysis shows that molecular signaling pathways including porphyrin and chlorophyll metabolism, photosynthesis - antenna proteins, and photosynthesis were suppressed in all three TCS treatments, and the perturbations of these signaling pathways were exacerbated with increased TCS exposure concentrations. Additionally, signaling of replication-coupled DNA repair was only activated in 100 μg/L TCS treatment. These results indicate that photosynthesis systems were sensitive targets of TCS toxicity in R. subcapitata, which is distinct from the inhibition of lipid synthesis by TCS in bacteria. This study provides novel knowledge on molecular mechanisms of TCS toxicity in R. subcapitata.

Trophic transfer potential of nTiO2, nZnO, and triclosan in an algae-algae eating fish food chain

Little is known about the trophic transfer of nanoparticles and personal care products via dietary exposure in an algae-algae eating fish food chain. The bioaccumulation of nano-TiO₂ (P25 - nTiO₂), nano-ZnO (nZnO), and triclosan (TCS) in eight different combinations were explored in this study through algae, Asterococcus superbus, to fish, Gyrinocheilus aymonieri. Results found the bioaccumulation of TCS changed with algal biomass, while the bioaccumulation of Ti and Zn varied with the amount of lipids and proteins in algal cells. In algae, Ti was in the form of nTiO₂ and Zn in the form of zinc ion. Due to dietary exposure, Ti and Zn quantity in fish was closely related to that in algae. The quantity of Ti and Zn in algae and fish exposed to the interaction of nTiO₂ * nZnO* TCS was higher than that in other treatments. The uptake of Ti and Zn in algae exposed to the interaction of nTiO₂ * nZnO had been inhibited, and the corresponding fish also had less Ti and Zn in their tissues. nTiO₂-containing treatments had higher Ti proportion in muscle than gill in fish. Treatment nZnO had the most Zn in gill, whereas nZnO * TCS-containing treatments had higher Zn proportion in gut than other tissues. No observation of TCS in fish in all treatments suggested the removal and metabolism of TCS might be induced by tissue recovery and acclimation. This is the first report on trophic transfer of mixed nanoparticles and personal care product in an algae-algae eating fish two-stage food chain.

Exploring the effects of microalgal biomass on the oil behavior in a sand-water system

This study focused on the impact of microalgal biomass on the oil behavior in a sand-water system. The microalgal biomass was characterized, and the interaction between microalgal biomass and oil was analyzed through Fourier transform infrared (FTIR) spectroscopy. The effects of different conditions including microalgal biomass dose, pH, temperature, and salinity on the oil behavior were investigated. A two-level factorial analysis was also used to further explore the interactions of these conditions. The microalgal biomass was found to be the most influential parameter for the residual crude oil on sand. Higher microalgal biomass dose resulted in less residual oil on sand. The remaining oil decreased with increasing solution pH from 4 to 7, and an increase of remaining oil was observed when the pH was further increased above 7. In addition, temperature and salinity could affect the removal of crude oil in the presence of microalgal biomass. Increasing the temperature could result in less residual oil on sand and there was higher oil removal at the high salinity. The effects of microalgal biomass on the oil behavior could also be impacted by environmental conditions. The results of this study indicate that the presence of algae in the oiled shoreline can be considered in the comprehensive evaluation of spill risk and prediction of oil fate.

Combined toxicity characteristics and regulation of residual quinolone antibiotics in water environment

In this study, the mixture toxicity index method was used to evaluate the combined toxicity of residual Quinolones (QNs) on algae in twelve groups of water environment reported in the literature. The selected three sets of data (II, Ⅺ, and Ⅻ) combined with full factorial design method were used to analyze the significance of the combined toxicity. Subsequently, molecular docking was used to reveal the significant mechanism of the primary effect of the combined toxicity. Finally, based on the sensitivity analysis method, the acid-base conditions affecting the combined toxicity were screened, and molecular dynamics simulation was used to control the combined toxicity in the water environment. The results of the mixture toxicity index method showed that the combined toxicity in all the twelve groups of water environments was synergistic. The full factorial design method revealed that ciprofloxacin, norfloxacin, enrofloxacin, lomefloxacin, and their binary combinations from the combined toxicity system of QNs, were the significant factors that caused the synergistic toxicity of QN_S on algae. Molecular docking confirmed that the total number of amino acids, the number of significant amino acids, and hydrogen bonds of QNs toxic targets were significantly related to the synergistic effect of the combined toxicity. In addition, the molecular dynamics simulation showed that the binding energy of residual QNs and toxic targets changes with the acid-base conditions of the water environment. Thus, the combined toxicity can be slowed down or reduced by adequately adjusting the acid-base condition of the water.