Sesamin induces A549 cell mitophagy and mitochondrial apoptosis via a reactive oxygen species-mediated reduction in mitochondrial membrane potential.

Sesamin, a lipid-soluble lignin originally isolated from sesame seeds, which induces cancer cell apoptosis and autophagy. In the present study, has been reported that sesamin induces apoptosis via several pathways in human lung cancer cells. However, whether mitophagy is involved in sesamin induced lung cancer cell apotosis remains unclear. This study, the anticancer activity of sesamin in lung cancer was studied by reactive oxygen species (ROS) and mitophagy. A549 cells were treated with sesamin, and cell viability, migration ability, and cell cycle were assessed using the CCK8 assay, scratch-wound test, and flow cytometry, respectively. ROS levels, mitochondrial membrane potential, and apoptosis were examined by flow cytometric detection of DCFH-DA fluorescence and by using JC-1 and TUNEL assays. The results indicated that sesamin treatment inhibited the cell viability and migration ability of A549 cells and induced G0/G1 phase arrest. Furthermore, sesamin induced an increase in ROS levels, a reduction in mitochondrial membrane potential, and apoptosis accompanied by an increase in cleaved caspase-3 and cleaved caspase-9. Additionally, sesamin triggered mitophagy and increased the expression of PINK1 and translocation of Parkin from the cytoplasm to the mitochondria. However, the antioxidant N-acetyl-L-cysteine clearly reduced the oxidative stress and mitophagy induced by sesamin. Furthermore, we found that cyclosporine A (an inhibitor of mitophagy) decreased the inhibitory effect of sesamin on A549 cell viability. Collectively, our data indicate that sesamin exerts lethal effects on lung cancer cells through the induction of ROS-mediated mitophagy and mitochondrial apoptosis.

A noise-immune reinforcement learning method for early diagnosis of neuropsychiatric systemic lupus erythematosus.

The neuropsychiatric systemic lupus erythematosus (NPSLE), a severe disease that can damage the heart, liver, kidney, and other vital organs, often involves the central nervous system and even leads to death. Magnetic resonance spectroscopy (MRS) is a brain functional imaging technology that can detect the concentration of metabolites in organs and tissues non-invasively. However, the performance of early diagnosis of NPSLE through conventional MRS analysis is still unsatisfactory. In this paper, we propose a novel method based on genetic algorithm (GA) and multi-agent reinforcement learning (MARL) to improve the performance of the NPSLE diagnosis model. Firstly, the proton magnetic resonance spectroscopy (1H-MRS) data from 23 NPSLE patients and 16 age-matched healthy controls (HC) were standardized before training. Secondly, we adopt MARL by assigning an agent to each feature to select the optimal feature subset. Thirdly, the parameter of SVM is optimized by GA. Our experiment shows that the SVM classifier optimized by feature selection and parameter optimization achieves 94.9% accuracy, 91.3% sensitivity, 100% specificity and 0.87 cross-validation score, which is the best score compared with other state-of-the-art machine learning algorithms. Furthermore, our method is even better than other dimension reduction ones, such as SVM based on principal component analysis (PCA) and variational autoencoder (VAE). By analyzing the metabolites obtained by MRS, we believe that this method can provide a reliable classification result for doctors and can be effectively used for the early diagnosis of this disease.

Long-term hyperglycemia aggravates α-synuclein aggregation and dopaminergic neuronal loss in a Parkinson's disease mouse model.

BACKGROUND: Growing evidence suggests an association between Parkinson's disease (PD) and diabetes mellitus (DM). At the cellular level, long-term elevated levels of glucose have been shown to lead to nigrostriatal degeneration in PD models. However, the underlying mechanism is still unclear. Previously, we have elucidated the potential of type 2 diabetes mellitus (T2DM) in facilitating PD progression, involving aggregation of both alpha-synuclein (α-syn) and islet amyloid polypeptide in the pancreatic and brain tissues. However, due to the complicated effect of insulin resistance on PD onset, the actual mechanism of hyperglycemia-induced dopaminergic degeneration remains unknown. METHODS: We employed the type 1 diabetes mellitus (T1DM) model induced by streptozotocin (STZ) injection in a transgenic mouse line (BAC-α-syn-GFP) overexpressing human α-syn, to investigate the direct effect of elevated blood glucose on nigrostriatal degeneration. RESULTS: STZ treatment induced more severe pathological alterations in the pancreatic islets and T1DM symptoms in α-syn-overexpressing mice than in wild-type mice, at one month and three months after STZ injections. Behavioral tests evaluating motor performance confirmed the nigrostriatal degeneration. Furthermore, there was a marked decrease in dopaminergic profiles and an increase of α-syn accumulation and Serine 129 (S129) phosphorylation in STZ-treated α-syn mice compared with the vehicle-treated mice. In addition, more severe neuroinflammation was observed in the brains of the STZ-treated α-syn mice. CONCLUSION: Our results solidify the potential link between DM and PD, providing insights into how hyperglycemia induces nigrostriatal degeneration and contributes to pathogenic mechanisms in PD.

Transfer inhibitory potency prediction to binary classification: A model only needs a small training set.

One of the most laborious for drug discovery is to select compounds from a library for experimental evaluation. Hence, we propose a machine learning model only needs to be trained on a small dataset to predict the inhibition constant (Ki) and half maximal inhibitory concentration (IC50) for a compound. We transfer the prediction task to a simpler binary classification task based on a naive but effective idea that we only need the related rank of a compound to determine whether to take it for further examination. To achieve this, we design a data augmentation strategy to effectively leverage the relationship between the compounds in the training set. After that, we formulate a new reward function for deep reinforcement learning to balance the feature selection and the accuracy. We employ a particle swarm optimized support vector machine for the binary classification task. Finally, a soft voting mechanism is introduced to solve the contradiction from the binary classification. Sufficient experiments show that our model achieves high and reliable accuracy, and is capable of ranking compounds based on a selected set of molecular descriptors. The current results show that our model provides a potential ligand-based in silico approach for prioritizing chemicals for experimental studies.

Sesamin induces ER stress-mediated apoptosis and activates autophagy in cervical cancer cells.

AIMS: Sesamin, a major lignan of sesame oil, has demonstrated anticancer properties. However, its anticancer effects on cervical cancer have not been studied. Here, we investigated the effects of sesamin on cervical cancer (HeLa) cell line and explored the underlying mechanisms. MAIN METHODS: HeLa cells were cultured with sesamin. CCK-8 and scratch wound test were applied to detect the proliferation and migration ability, while flow cytometry and TUNEL staining were applied to detect apoptosis. The expression of Bax and Bcl-2 was assessed by Western blotting. Further observe the ultrastructure using transmission electron microscopy (TEM) and detect the expression of caspase-12, GRP78, GADD153, IRE1α, p-IRE1α, JNK, p-JNK, LC3I/II and beclin-1. In addition, HeLa cells were treated with 3-MA (an autophagy inhibitor) and/or sesamin. Then detect the expression of LC3I/II and cell viability. KEY FINDINGS: CCK-8 and scratch wound test revealed that sesamin inhibits HeLa cells proliferation and migration, while flow cytometry and TUNEL staining indicated that sesamin induces apoptosis in these cells. In sesamin group, the expression of Bax, caspase-12, GRP78, GADD153, p-IRE1α, p-JNK, LC3I/II and beclin-1 was up-regulated while Bcl-2 was down-regulated compared to control group. Further research revealed that sesamin also induces Hela cells autophagy and inhibition of autophagy increases cell viability of sesamin-treated HeLa cells. SIGNIFICANCE: Sesamin inhibits proliferation/migration of HeLa cells and induces ER stress-mediated apoptosis through IRE1α/JNK pathway, and that it activates autophagy and autophagic death in these cells, further validate the anticancer effect of sesamin.