Microplastics in the marine environment have been paid more and more attention by researchers, and the impact of these substances on marine microorganisms can not be ignored. This study aims to pay attention to the effect and mechanism of microplastics (80 nm PS-COOH) on Antarctic marine yeast, Rhodotorula mucilaginosa AN5. In our work, Scanning Electron Microscopy (SEM) was used to observe the morphology of yeast cells under microplastic stress, and metabolite analysis was used to explore the possible mechanism of yeast cell damage. The results showed that: (1) a certain concentration of PS-COOH could inhibit 40% growth of yeast cells and destroy the cell morphology. (2) Physiological and biochemical changes showed that under PS-COOH stress, the level of reactive oxygen species (ROS), malondialdehyde (MDA) content and the activities of antioxidant enzymes such as catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) of R. mucilaginosa AN5 increased from 34–66%. It can be guessed that yeast can eliminate excess ROS in cells by the activity of oxidative kinases increased and maintain the balance of reactive oxygen species in cells.

Microplastics in the marine environment have been paid more and more attention by researchers, and the impact of these substances on marine microorganisms can not be ignored. This study aims to pay attention to the effect and mechanism of microplastics (80 nm PS-COOH) on Antarctic marine yeast, Rhodotorula mucilaginosa AN5. In our work, Scanning Electron Microscopy (SEM) was used to observe the morphology of yeast cells under microplastic stress, and metabolite analysis was used to explore the possible mechanism of yeast cell damage. The results showed that: (1) a certain concentration of PS-COOH could inhibit 40% growth of yeast cells and destroy the cell morphology. (2) Physiological and biochemical changes showed that under PS-COOH stress, the level of reactive oxygen species (ROS), malondialdehyde (MDA) content and the activities of antioxidant enzymes such as catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) of R. mucilaginosa AN5 increased from 34–66%. It can be guessed that yeast can eliminate excess ROS in cells by the activity of oxidative kinases increased and maintain the balance of reactive oxygen species in cells.

Background: Glutamine synthetases are considered to be an important enzyme class for microbial adaptation to extreme environments, and studying the Antarctic sea ice bacterial glutamine synthetases can be used to explore the molecular mechanisms of psychrophilic microorganisms. Objective: This work use bioinformatics tools to predict structure and function of Glutamine synthetase (GS) from Pseudoalteromonas sp., and to provide a theoretical basis for further study. Methods: Open reading frame (ORF) of GS sequence from Pseudoalteromonas sp. was obtained by ORF finder and was translated into amino acid residue. The structure domain was analyzed by Blast. By the method of analysis tools: Protparam, ProtScale, SignalP-4.0, TMHMM, SOPMA, SWISS-MODEL, NCBI SMART-BLAST and MAGA 7.0, the structure and function of the protein were predicted and analyzed. Results: The results showed that the sequence was GS with 468 amino acid residues, theoretical molecular weight was 51986.64 Da. The protein has the closest evolutionary status with Shewanella oneidensis. Then it had no signal peptide site and transmembrane domain. Secondary structure of GS contained 35.04% α-helix, 16.67% Extended chain, 5.34% β-turn, 42.95% RandomCoil. Conclusions: This GS was a variety of biological functions of protein that may be used as a molecular samples of microbial nitrogen metabolism in extreme environments.

Metal-organic frameworks (MOF) have garnered much attention as promising catalysts due to their tunable porosity, high surface area, and diversity of catalytic metal clusters and organic linkers as building blocks. The presence of open metal sites (OMS) significantly influences the catalytic, adsorption, and separation capabilities of MOFs. However, common laboratory methods are indirect and can suffer from structural heterogeneity. Computational methods, including machine learning, play a central role in the rational design of MOFs, yet in silico detection of OMS still relies heavily on computationally expensive simulations. In this work, we use extreme gradient boosting (XGboost) and random forest (RF) methods to predict the existence of OMS in various MOF compounds based on structural and chemical features. RF provided a higher prediction accuracy of 0.891 compared to 0.865 of XGBoost. Average ionization energy, average electron affinity, and fraction of electrons in d orbitals exhibited the highest importance scores across the two models. These prediction models not only provide novel insights into the structural-property relationship between MOFs and OMS, but also would enable accurate and efficient exploration of MOFs that would give rise to OMS, facilitating the engineering of sorption, separation, and catalytic properties.

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