ABSTRACT
This study investigated the mechanism by which fucoxanthin acts as a novel ferroptosis inducer to inhibit tongue cancer. The MTT assay was used to detect the inhibitory effects of fucoxanthin on SCC-25 human tongue squamous carcinoma cells. The levels of reactive oxygen species (ROS), mitochondrial membrane potential (MMP), glutathione (GSH), superoxide dismutase (SOD), malondialdehyde (MDA), and total iron were measured. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blotting were used to assess glutathione peroxidase 4 (GPX4), nuclear factor erythroid 2-related factor 2 (Nrf2), Keap1, solute carrier family 7 member 11 (SLC7A11), transferrin receptor protein 1 (TFR1), p53, and heme oxygenase 1 (HO-1) expression. Molecular docking was performed to validate interactions. Compared with the control group, the activity of fucoxanthin-treated SCC-25 cells significantly decreased in a dose- and time-dependent manner. The levels of MMP, GSH, and SOD significantly decreased in fucoxanthin-treated SCC-25 cells; the levels of ROS, MDA, and total iron significantly increased. mRNA and protein expression levels of Keap1, GPX4, Nrf2, and HO-1 in fucoxanthin-treated cells were significantly decreased, whereas levels of TFR1 and p53 were significantly increased, in a concentration-dependent manner. Molecular docking analysis revealed that binding free energies of fucoxanthin with p53, SLC7A11, GPX4, Nrf2, Keap1, HO-1, and TFR1 were below -5 kcal/mol, primarily based on active site hydrogen bonding. Our findings suggest that fucoxanthin can induce ferroptosis in SCC-25 cells, highlighting its potential as a treatment for tongue cancer.
Subject(s)
Ferroptosis , Heme Oxygenase-1 , Molecular Docking Simulation , NF-E2-Related Factor 2 , Phospholipid Hydroperoxide Glutathione Peroxidase , Xanthophylls , Humans , NF-E2-Related Factor 2/metabolism , Ferroptosis/drug effects , Xanthophylls/pharmacology , Xanthophylls/chemistry , Heme Oxygenase-1/metabolism , Heme Oxygenase-1/genetics , Cell Line, Tumor , Phospholipid Hydroperoxide Glutathione Peroxidase/metabolism , Reactive Oxygen Species/metabolism , Signal Transduction/drug effects , Tongue Neoplasms/drug therapy , Tongue Neoplasms/metabolism , Tongue Neoplasms/pathology , Receptors, Transferrin/metabolism , Membrane Potential, Mitochondrial/drug effects , Kelch-Like ECH-Associated Protein 1/metabolism , Gene Expression Regulation, Neoplastic/drug effects , Amino Acid Transport System y+/metabolism , Amino Acid Transport System y+/genetics , Superoxide Dismutase/metabolism , Down-Regulation/drug effects , Antigens, CDABSTRACT
Thermoelectric generators (TEGs) are environmentally friendly energy harvesting technologies that hold great promise in the field of self-powered electronics and sensing. However, the current development of thermoelectric (TE) devices has largely lagged behind the development of thermoelectric materials, especially in the preparation of thermoelectric components with customizable shapes and excellent properties, which largely limits their practical applications. These issues can be effectively addressed by using 3D printing technology. Here, we print multiple p-type thermoelectric legs (pins) consecutively with this simple technique, and the printed TEGs have excellent thermal potential (288 µV K-1 at room temperature) and excellent temperature response properties, which exhibited an output voltage of 127.94 mV at a temperature difference (ΔT) of 40 K. The 3D-printed thermoelectric generator enables the collection of thermal energy. In addition, the device has excellent temperature sensing characteristics, and this temperature signal to electrical signal conversion is very rapid, which enables temperature sensing alarms in a wide temperature domain. Combining these features, an energy harvesting and electrical alarm concept for home-scale applications is proposed, which is expected to provide a diverse research idea for the application of next-generation thermoelectric devices.
ABSTRACT
RNA modification plays an indispensable role in the regulation of organisms. RNA modification site prediction offers an insight into diverse cellular processing. Regarding different types of RNA modification site prediction, it is difficult to tell the most relevant feature combinations from a variant of RNA properties. Thereby, the performance of traditional machine learning based predictors relied on the skill of feature engineering. As a data-driven approach, deep learning can detect optimal feature patterns to represent input data. In this study, we developed a predictor for multiple types of RNA modifications method called DeepMRMP (Multiple Types RNA Modification Sites Predictor), which is based on the bidirectional Gated Recurrent Unit (BGRU) and transfer learning. DeepMRMP makes full use of multiple RNA site modification data and correlation among them to build predictor for different types of RNA modification sites. Through 10-fold cross-validation of the RNA sequences of H. sapiens, M. musculus and S. cerevisiae, DeepMRMP acted as a reliable computational tool for identifying N1-methyladenosine (m1A), pseudouridine (Ψ), 5-methylcytosine (m5C) modification sites.