ABSTRACT
Early and prolonged exposure to anesthetic agents could cause neurodevelopmental disorders in children. Astrocytes, heavily outnumber neurons in the brain, are crucial regulators of synaptic formation and function during development. However, how general anesthetics act on astrocytes and the impact on cognition are still unclear. In this study, we investigated the role of ferroptosis and GPX4, a major hydroperoxide scavenger playing a pivotal role in suppressing the process of ferroptosis, and their underlying mechanism in isoflurane-induced cytotoxicity in astrocytes and cognitive impairment. Our results showed that early 6 h isoflurane anesthesia induced cognitive impairment in mice. Ferroptosis-relative genes and metabolic changes were involved in the pathological process of isoflurane-induced cytotoxicity in astrocytes. The level of GPX4 was decreased while the expression of 4-HNE and generation of ROS were elevated after isoflurane exposure. Selectively blocking ferroptosis with Fer-1 attenuated the abovementioned cytotoxicity in astrocytes, paralleling with the reverse of the changes in GPX4, ROS and 4-HNE secondary to isoflurane anesthesia. Fer-1 attenuated the cognitive impairment induced by prolonged isoflurane exposure. Thus, ferroptosis conduced towards isoflurane-induced cytotoxicity in astrocytes via suppressing GPX4 and promoting lipid peroxidation. Fer-1 was expected to be an underlying intervention for the neurotoxicity induced by isoflurane in the developing brain, and to alleviate cognitive impairment in neonates.
Subject(s)
Animals, Newborn , Astrocytes , Cognitive Dysfunction , Ferroptosis , Isoflurane , Animals , Astrocytes/drug effects , Astrocytes/metabolism , Isoflurane/toxicity , Ferroptosis/drug effects , Ferroptosis/physiology , Cognitive Dysfunction/chemically induced , Cognitive Dysfunction/prevention & control , Cognitive Dysfunction/metabolism , Mice , Anesthetics, Inhalation/toxicity , Mice, Inbred C57BL , Neuroprotective Agents/pharmacology , Phospholipid Hydroperoxide Glutathione Peroxidase/metabolism , Reactive Oxygen Species/metabolismABSTRACT
Identifying the potential bacteriophages (phage) candidate to treat bacterial infections plays an essential role in the research of human pathogens. Computational approaches are recognized as a valid way to predict bacteria and target phages. However, most of the current methods only utilize lower-order biological information without considering the higher-order connectivity patterns, which helps to improve the predictive accuracy. Therefore, we developed a novel microbial heterogeneous interaction network (MHIN)-based model called PTBGRP to predict new phages for bacterial hosts. Specifically, PTBGRP first constructs an MHIN by integrating phage-bacteria interaction (PBI) and six bacteria-bacteria interaction networks with their biological attributes. Then, different representation learning methods are deployed to extract higher-level biological features and lower-level topological features from MHIN. Finally, PTBGRP employs a deep neural network as the classifier to predict unknown PBI pairs based on the fused biological information. Experiment results demonstrated that PTBGRP achieves the best performance on the corresponding ESKAPE pathogens and PBI dataset when compared with state-of-art methods. In addition, case studies of Klebsiella pneumoniae and Staphylococcus aureus further indicate that the consideration of rich heterogeneous information enables PTBGRP to accurately predict PBI from a more comprehensive perspective. The webserver of the PTBGRP predictor is freely available at http://120.77.11.78/PTBGRP/.
