Hierarchical multi-task deep learning-assisted construction of human gut microbiota reactive oxygen species-scavenging enzymes database.

In the process of oxygen reduction, reactive oxygen species (ROS) are generated as intermediates, including superoxide anion (O2-), hydrogen peroxide (H2O2), and hydroxyl radicals (OH-). ROS can be destructive, and an imbalance between oxidants and antioxidants in the body can lead to pathological inflammation. Inappropriate ROS production can cause oxidative damage, disrupting the balance in the body and potentially leading to DNA damage in intestinal epithelial cells and beneficial bacteria. Microorganisms have evolved various enzymes to mitigate the harmful effects of ROS. Accurately predicting the types of ROS-scavenging enzymes (ROSes) is crucial for understanding the oxidative stress mechanisms and formulating strategies to combat diseases related to the "gut-organ axis." Currently, there are no available ROSes databases (DBs). In this study, we propose a systematic workflow comprising three modules and employ a hierarchical multi-task deep learning approach to collect, expand, and explore ROSes-related entries. Based on this, we have developed the human gut microbiota ROSes DB (http://39.101.72.186/), which includes 7,689 entries. This DB provides user-friendly browsing and search features to support various applications. With the assistance of ROSes DB, various communication-based microbial interactions can be explored, further enabling the construction and analysis of the evolutionary and complex networks of ROSes DB in human gut microbiota species.IMPORTANCEReactive oxygen species (ROS) is generated during the process of oxygen reduction, including superoxide anion, hydrogen peroxide, and hydroxyl radicals. ROS can potentially cause damage to cells and DNA, leading to pathological inflammation within the body. Microorganisms have evolved various enzymes to mitigate the harmful effects of ROS, thereby maintaining a balance of microorganisms within the host. The study highlights the current absence of a ROSes DB, emphasizing the crucial importance of accurately predicting the types of ROSes for understanding oxidative stress mechanisms and developing strategies for diseases related to the "gut-organ axis." This research proposes a systematic workflow and employs a multi-task deep learning approach to establish the human gut microbiota ROSes DB. This DB comprises 7,689 entries and serves as a valuable tool for researchers to delve into the role of ROSes in the human gut microbiota.

ROSes-FINDER: a multi-task deep learning framework for accurate prediction of microorganism reactive oxygen species scavenging enzymes.

Reactive oxygen species (ROS) are highly reactive molecules that play important roles in microbial biological processes. However, excessive accumulation of ROS can lead to oxidative stress and cellular damage. Microorganism have evolved a diverse suite of enzymes to mitigate the harmful effects of ROS. Accurate prediction of ROS scavenging enzymes classes (ROSes) is crucial for understanding the mechanisms of oxidative stress and developing strategies to combat related diseases. Nevertheless, the existing approaches for categorizing ROS-related proteins exhibit certain drawbacks with regards to their precision and inclusiveness. To address this, we propose a new multi-task deep learning framework called ROSes-FINDER. This framework integrates three component methods using a voting-based approach to predict multiple ROSes properties simultaneously. It can identify whether a given protein sequence is a ROSes and determine its type. The three component methods used in the framework are ROSes-CNN, which extracts raw sequence encoding features, ROSes-NN, which predicts protein functions based on sequence information, and ROSes-XGBoost, which performs functional classification using ensemble machine learning. Comprehensive experiments demonstrate the superior performance and robustness of our method. ROSes-FINDER is freely available at https://github.com/alienn233/ROSes-Finder for predicting ROSes classes.

[Cytotoxicity of Karenia mikimotoi extracts].

OBJECTIVE: To investigate the potential threat of K. mikimotoi on human health by analyzing the negative effects of K. mikimotoi extract on three cancer cells, HepG2, HeLa and A549. METHODS: Inhibitory effect of K. mikimotoi extract on the proliferation of three cancer cells was observed by MTT assay and the relative amount of GM1 in cancer cell membrane was detected by immunofluorescence method. RESULTS: The extracts of K. mikimotoi significantly inhibited the proliferation of HeLa, HepG2 and A549 cells in a dose-and time-dependent manner. HeLa and A549 cells were more sensitive than HepG2 cell to the toxicity of K. mikimotoi extracts. However, there was no evident correlation between the proliferative inhibition and the amount of GM1 in cancer cell membrane. CONCLUSION: There was a significant cytotoxicity of K. mikimotoi extracts to mammalian cells, which suggested that their potential threats might be existed to human health. However, the cytotoxic targets of K. mikimotoi extract on cell membrane were complex.

[Effects of some membrane lipids on the hemolysis induced by hemolytic toxin from Karenia mikimotoi].

OBJECTIVE: To explore the effects of some membrane lipids on the hemolysis induced by hemolytic toxin from Karenia mikimotoi. METHODS: Effects of exogenous membrane lipids such as lecithin, sphingomyelin, L-alpha-phosphatidic acid,cholesterol and gangliosides on the hemolysis induced by the hemolytic toxin were observed. The sensitivities of some erythrocytes from different animals such as rabbit, rat and fish to the hemolytic toxin were evaluated. The total gangliosides in different erythrocytes membrane were detected by colorimetry. RESULTS: Only gangliosides significantly inhibited the hemolysis of the hemolytic toxin from K. mikimotoi (P <0.05). Hemolytic percentages decreased to 16.05% after 10 min addition of ganglioside, while those of control were 35.65%. The rabbit red blood cell was the most sensitive to the hemolytic toxin. The hemolytic percentages of rabbit erythrocyte were higher than those of rat (P < 0.05) and fish (P < 0.01). The amounts of lipid-bind sialic acid (LBSA) on frozen dried membrane of rabbit were 672.08 microg/g,and were higher than those of rat (585.97 microg/g) (P < 0.05) and that of fish (431.52 microg/g) (P < 0.01). CONCLUSION: Exogenous gangliosides could have a potent inhibition on the hemolysis induced by hemolytic toxin from K. mikimotoi. There was a significant correlation between the sensitivities of different erythrocytes to the hemolytic toxin and the amount of ganglioside on different erythrocytes membrane.