Procyanidin alleviates ferroptosis and inflammation of LPS-induced RAW264.7 cell via the Nrf2/HO-1 pathway

Inflammation is a common occurrence in many medical conditions and is a natural defense mechanism of the human body. Ferroptosis, an iron-dependent form of cell death related to lipid peroxide build-up, has been found to be involved in inflammation. The anti-inflammatory effects of procyanidin, however, are not yet fully understood. Through network pharmacology and bioinformatics analysis, it was suggested that procyanidin could modulate ferroptosis and cause anti-inflammatory effects on RAW264.7 cells. This was further evidenced through molecular docking, molecular dynamics, and in vitro experiments. The results indicated that procyanidin could diminish inflammation in LPS-induced RAW264.7 cells by regulating ferroptosis via the Nrf2/HO-1/Keap-1 pathway. In conclusion, procyanidin supplementation might be an effective way to reduce inflammation by decreasing the release of inflammatory cytokines and suppressing ferroptosis.

New Alkaloids and Steroids from Hydranth of Goniopora columna Corals and Their Inhibiting Lung Cancer Cell Activities

A new alkaloids, aplysingoniopora A (1), and new configuration pregnane type steroid compound, 9,17-α-pregn-1,4,20-en-3-one (2), and two known pregnane type steroid compounds (3 and 4) were isolated from hydranth of Goniopora columna corals. The compounds structures and absolute configurations were determined by extensive spectroscopic analysis, MS data, single-crystal X-ray diffraction analysis and quantum chemical calculation. The anticancer effect of the compounds were explored in human non-small-cell lung cancer (NSCLC) A549 cell lines. As the results, the compound 3 and 4 induces toxicity and has proliferation inhibitory effects on A549 cells (IC50=58.99 μM and 58.77 μM, respectively) in vitro.

mPGES-1 Inhibitor Discovery Based on Computer-Aided Screening: Pharmacophore Models, Molecular Docking, ADMET, and MD Simulations

mPGES-1 is an enzyme, which, when activated by inflammatory factors, can cause prostaglandin E synthesis. Traditional non-steroidal anti-inflammatory drugs are capable of inhibiting prostaglandin production, yet they can also cause gastrointestinal reactions and coagulation disorders. mPGES-1, the enzyme at the conclusion of prostaglandin production, does not cause any adverse reactions when inhibited. Numerous studies have demonstrated that mPGES-1 is more abundant in cancerous cells than in healthy cells, indicating that decreasing the expression of mPGES-1 could be a potential therapeutic strategy for cancer. Consequently, the invention of mPGES-1 inhibitors presents a fresh avenue for the treatment of inflammation and cancer. Incorporating a database of TCM compounds, we collected a batch of compounds that had an inhibitory effect on mPGES-1 and possessed IC50 value. Firstly, a pharmacophore model was constructed, and the TCM database was screened, and the compounds with score cut-off values of more than 1 were retained. Then, the compounds retained after being screened via the pharmacodynamic model were screened for docking at the mPGES-1 binding site, followed by high-throughput virtual screening [HTVS] and standard precision [SP] and super-precision [XP] docking, and the compounds in the top 20% of the XP docking score were selected to calculate the total free binding energy of MM-GBSA. The best ten compounds were chosen by comparing their score against the reference ligand 4U9 and the MM-GBSA_dG_Bind score. ADMET analysis resulted in the selection of ten compounds, three of which had desirable medicinal properties. Finally, the binding energy of the target protein mPGES-1 and the candidate ligand compound was analyzed using a 100 ns molecular dynamics simulation of the reference ligand 4U9 and three selected compounds. After a gradual screening study and analysis, we identified a structure that is superior to the reference ligand 4U9 in all aspects, namely compound 15643. Taken together, the results of this study reveal a structure that can be used to inhibit mPGES-1 compound 15643, thereby providing a new option for anti-inflammatory and anti-tumor drugs.

The combination of machine learning and untargeted metabolomics identifies the lipid metabolism -related gene CH25H as a potential biomarker in asthma

BACKGROUND

Lipids, significant signaling molecules, regulate a multitude of cellular responses and biological pathways in asthma which are closely associated with disease onset and progression. However, the characteristic lipid genes and metabolites in asthma remain to be explored. It is also necessary to further investigate the role of lipid molecules in asthma based on high-throughput data.

OBJECTIVE

To explore the biomarkers and molecular mechanisms associated with lipid metabolism in asthma.

METHODS

In this study, we selected three mouse-derived datasets and one human dataset (GSE41665, GSE41667, GSE3184 and GSE67472) from the GEO database. Five machine learning algorithms, LASSO, SVM-RFE, Boruta, XGBoost and RF, were used to identify core gene. Additionally, we used non-negative matrix breakdown (NMF) clustering to identify two lipid molecular subgroups and constructed a lipid metabolism score by principal component analysis (PCA) to differentiate the subtypes. Finally, Western blot confirmed the altered expression levels of core genes in OVA (ovalbumin) and HDM+LPS (house dust mite+lipopolysaccharide) stimulated and challenged BALB/c mice, respectively. Results of non-targeted metabolomics revealed multiple differentially expressed metabolites in the plasma of OVA-induced asthmatic mice.

RESULTS

Cholesterol 25-hydroxylase (CH25H) was finally localized as a core lipid metabolism gene in asthma and was verified to be highly expressed in two mouse models of asthma. Five-gene lipid metabolism constructed from CYP2E1, CH25H, PTGES, ALOX15 and ME1 was able to distinguish the subtypes effectively. The results of non-targeted metabolomics showed that most of the aberrantly expressed metabolites in the plasma of asthmatic mice were lipids, such as LPC 16:0, LPC 18:1 and LPA 18:1.

CONCLUSION

Our findings imply that the lipid-related gene CH25H may be a useful biomarker in the diagnosis of asthma.