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Nitrate contamination from human activities (

Photochemical reactions that widely occur in aquatic environments play important roles in carbon fate (e.g., carbon conversion and storage from organic matter) in ecosystems. Aquatic microbes and natural minerals further regulate carbon fate, but the processes and mechanisms remain largely unknown. Herein, the interaction between Escherichia coli and pyrite and its influence on the fate of carbon in water were investigated at the microscopic scale and molecular level. The results showed that saccharides and phenolic compounds in microbial extracellular polymeric substances helped remove pyrite surface oxides via electron transfer. After the removal of surface oxides on pyrite, the photochemical properties under visible-light irradiation were significantly decreased, such as reactive oxygen species and electron transfer capacity. Unlike the well-accepted theory of minerals protecting organic matter in the soil, the organic matter adsorbed on minerals preferred degradation due to the enhanced photochemical reactions in water. In contrast, the minerals transformed by microbes suppressed the decomposition of organic matter due to the passivation of the chemical structure and activity. These results highlight the significance of mineral chemical activity on organic matter regulated by microbes and provide insights into organic matter conversion in water.

Significance The protein corona affects the clinical applications, organ targeting, and safety assessment of nanomaterials, and prediction of the protein corona would be valuable for the design of ideal nanomaterials. However, no methods to predict the protein corona are available. Overcoming the numerous quantitative and qualitative factors influencing corona formation, the present work builds models that precisely predict the functional composition of the protein corona and the cell recognition of nanoparticles (NPs) integrating machine learning and meta-analysis. This workflow provides an effective method to predict the functional composition of the protein corona that determines cell recognition to guide the synthesis and applications of NPs., Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by R2 (less than 0.40). Here, the performance with R2 over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good R2 (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty.

Elucidation of the relationships between nanoparticle properties and ecotoxicity is a fundamental issue for environmental applications and risk assessment of nanoparticles. However, effective strategies to connect the various properties of nanoparticles with their ecotoxicity remain largely unavailable. Herein, an untargeted metabolic pathway analysis was employed to investigate the environmental risk posed by 10 typical nanoparticles (AgNPs, CuNPs, FeNPs, ZnONPs, SiO2NPs, TiO2NPs, GO, GOQDs, SWCNTs, and C60) to rice (a staple food for half of the world's population). Downregulation of carbohydrate metabolism and upregulation of amino acid metabolism were the two dominant metabolic effects induced by all tested nanoparticles. Partial least-squares regression analysis indicated that a zerovalent metal and high specific surface area positively contributed to the downregulation of carbohydrate metabolism, indicating strong abiotic stress. In contrast, the carbon type, the presence of a spherical or sheet shape, and the absence of oxygen functional groups in the nanoparticles positively contributed to the upregulation of amino acid metabolism, indicating adaptation to abiotic stress. Moreover, network relationships among five properties of nanoparticles were established for these metabolic pathways. The results of the present study will aid in the understanding and prediction of environmental risks and in the design of environmentally friendly nanoparticles.

Soil microbial assemblages play a critical role in biogeochemical cycling processes in terrestrial ecosystems. Dynamic global information for these assemblages considering multiple factors is critical for predicting ecological safety concerns but remains unpredictable. Here, we collected microbial data from soil datasets worldwide and used a feature-explicable machine learning (FEML) approach to address this problem. Multiple-factor and factor interaction network analysis based on FEML can be used to visualize the restrictive relationships among multiple factors (e.g., fertilizer application, land use, and changing global climate and natural environments), which are difficult to explore based on limited experimental data and traditional machine learning methods. The FEML approach predicted that areas of bacterial hotspots in South America and Africa will expand by approximately 27% and 83%, respectively, in scenario RCP8.5 in 2100. In contrast, the areas of fungal hotspots in Asia and North America will decline by approximately 34% and 62%, respectively, under RCP8.5. The unbalanced ratios of bacteria to fungi affect the soil ecosystem, and bacterial-dominated communities contribute to the reduction of easily decomposing nutrients, the growth of the bacterivore community and a high proportion of microaggregates in the soil. Therefore, mitigating climate change is critical to reduce the remarkable imbalance between soil bacteria and fungi and predict risks to soil microbial assemblages based on multiple factors.

Although increasing attention has been paid to the nanotoxicity of graphene oxide quantum dots (GOQDs) due to their broad range of applications, the persistence and recoverability associated with GOQDs had been widely ignored. Interestingly, stress-response hormesis for algal growth was observed for Chlorella vulgaris as a single-celled model organism. Few physiological parameters, such as algal density, plasmolysis, and levels of reactive oxygen species, exhibited facile recovery. In contrast, the effects on chlorophyll a levels, permeability, and starch grain accumulation exhibited persistent toxicity. In the exposure stage, the downregulation of genes related to unsaturated fatty acid biosynthesis, carotenoid biosynthesis, phenylpropanoid biosynthesis, and binding contributed to toxic effects on photosynthesis. In the recovery stage, downregulation of genes related to the cis-Golgi network, photosystem I, photosynthetic membrane, and thylakoid was linked to the persistence of toxic effects on photosynthesis. The upregulated galactose metabolism and downregulated aminoacyl-tRNA biosynthesis also indicated toxicity persistence in the recovery stage. The downregulation and upregulation of phenylalanine metabolism in the exposure and recovery stages, respectively, reflected the tolerance of the algae to GOQDs. The present study highlights the importance of studying nanotoxicity by elucidation of stress and recovery patterns with metabolomics and transcriptomics.

Microplastics (MPs, sizes5 mm) have been found to be widely distributed in various environments, such as marine, freshwater, terrestrial and atmospheric systems. Machine learning provides a potential solution for evaluating the ecological risks of MPs based on big data. Compared with traditional models, data-driven machine learning can accelerate the realization of the control of hazardous MPs and reduce the impact of MPs at both local and global scales. However, there are some urgent issues that should be resolved. For example, lack of MP databases and incomparable literatures causing the current MP data cannot fully support big data research. Therefore, it is imperative to formulate a set of standard and universal MP collection and testing protocols. For machine learning, predictions of large-scale MP distribution and the corresponding environmental risks remain lacking. To accelerate studies of MPs in the future, the methods and theories achieved for other particle pollutants, such as nanomaterials and aerosols, can be referenced. Beyond predication alone, the improvement of causality and interpretability of machine learning deserves attention in the studies of MP risks. Overall, this perspective paper provides insights for the development of machine learning methods in research on the environmental risks of MPs.

Predicting the biological responses to engineered nanoparticles (ENPs) is critical to their environmental health assessment. The disturbances of metabolic pathways reflect the global profile of biological responses to ENPs but are difficult to predict due to the highly heterogeneous data from complicated biological systems and various ENP properties. Herein, integrating multiple machine learning models and metabolomics enabled accurate prediction of the disturbance of metabolic pathways induced by 33 ENPs. Screening nine typical properties of ENPs identified type and size as the top features determining the effects on metabolic pathways. Similarity network analysis and decision tree models overcame the highly heterogeneous data sources to visualize and judge the occurrence of metabolic pathways depending on the sorting priority features. The model accuracy was verified by animal experiments and reached 75%-100%, even for the prediction of ENPs outside of databases. The models also predicted metabolic pathway-related histopathology. This work provides an approach for the quick assessment of environmental health risks induced by known and unknown ENPs.

Exploring the interactions between the immune system and nanomaterials (NMs) is critical for designing effective and safe NMs, but large knowledge gaps remain to be filled prior to clinical applications (e.g., immunotherapy). The lack of databases on interactions between the immune system and NMs affects the discovery of new NMs for immunotherapy. Complement activation and inhibition by NMs have been widely studied, but the general rules remain unclear. Biomimetic nanocoating to promote the clearance of NMs by the immune system is an alternative strategy for the immune response mediation of the biological corona. Immune response predictions based on NM properties can facilitate the design of NMs for immunotherapy, and artificial intelligences deserve much attention in the field. This review addresses the knowledge gaps regarding immune response and immunotherapy in relation to NMs, effective immunotherapy and material design without adverse immune responses.

Sediments are major sinks for organic pollutants and therefore can be used as archives of pollutant history. Methoxylated (MeO-) and hydroxylated (OH-) polybrominated diphenyl ethers (PBDE) are of increasing concern because of their potential toxicity and worldwide distribution. However, there is limited information on these compounds in marine environments. Here we determined the contents of nine MeO-PBDE, ten OH-PBDE and total organic carbon in a sediment core collected from the inner shelf of the East China Sea in 2009. The historical trends of 6-MeO-BDE47, 6-OH-BDE47 and total organic carbon were reconstructed. Our results show that 6-MeO-BDE47 concentrations present no significant trend from 1987 to 2005 and then increase from 2005 to 2008. 6-OH-BDE47 concentrations generally increase slightly from 1987 to the end of the 1990s and then decrease. Comparisons between 6-OH-BDE47 and 6-MeO-BDE47 as well as total organic carbon suggest that 6-OH-BDE47 is mainly from terrigenous inputs.