Identification of Apocarotenoids as Chemical Markers of In Vitro Anti-Inflammatory Activity for Spirulina Supplements

Discovering the Active Ingredients of Medicine and Food Homologous Substances for Inhibiting the Cyclooxygenase-2 Metabolic Pathway by Machine Learning Algorithms

Cyclooxygenase-2 (COX-2) and microsomal prostaglandin E2 synthase (mPGES-1) are two key targets in anti-inflammatory therapy. Medicine and food homology (MFH) substances have both edible and medicinal properties, providing a valuable resource for the development of novel, safe, and efficient COX-2 and mPGES-1 inhibitors. In this study, we collected active ingredients from 503 MFH substances and constructed the first comprehensive MFH database containing 27,319 molecules. Subsequently, we performed Murcko scaffold analysis and K-means clustering to deeply analyze the composition of the constructed database and evaluate its structural diversity. Furthermore, we employed four supervised machine learning algorithms, including support vector machine (SVM), random forest (RF), deep neural networks (DNNs), and eXtreme Gradient Boosting (XGBoost), as well as ensemble learning, to establish 640 classification models and 160 regression models for COX-2 and mPGES-1 inhibitors. Among them, ModelA_ensemble_RF_1 emerged as the optimal classification model for COX-2 inhibitors, achieving predicted Matthews correlation coefficient (MCC) values of 0.802 and 0.603 on the test set and external validation set, respectively. ModelC_RDKIT_SVM_2 was identified as the best regression model based on COX-2 inhibitors, with root mean squared error (RMSE) values of 0.419 and 0.513 on the test set and external validation set, respectively. ModelD_ECFP_SVM_4 stood out as the top classification model for mPGES-1 inhibitors, attaining MCC values of 0.832 and 0.584 on the test set and external validation set, respectively. The optimal regression model for mPGES-1 inhibitors, ModelF_3D_SVM_1, exhibited predictive RMSE values of 0.253 and 0.35 on the test set and external validation set, respectively. Finally, we proposed a ligand-based cascade virtual screening strategy, which integrated the well-performing supervised machine learning models with unsupervised learning: the self-organized map (SOM) and molecular scaffold analysis. Using this virtual screening workflow, we discovered 10 potential COX-2 inhibitors and 15 potential mPGES-1 inhibitors from the MFH database. We further verified candidates by molecular docking, investigated the interaction of the candidate molecules upon binding to COX-2 or mPGES-1. The constructed comprehensive MFH database has laid a solid foundation for the further research and utilization of the MFH substances. The series of well-performing machine learning models can be employed to predict the COX-2 and mPGES-1 inhibitory capabilities of unknown compounds, thereby aiding in the discovery of anti-inflammatory medications. The COX-2 and mPGES-1 potential inhibitor molecules identified through the cascade virtual screening approach provide insights and references for the design of highly effective and safe novel anti-inflammatory drugs.

Mechanism of antifungal activity and therapeutic action of β-ionone on Aspergillus fumigatus keratitis via suppressing LOX1 and JNK/p38 MAPK activation

PURPOSE

To investigate the anti-inflammatory and antifungal role of β-ionone (BI) in fungal keratitis (FK).

METHODS

In vitro antifungal activity of BI against Aspergillus fumigatus (A. fumigatus) was evaluated by using minimum inhibitory concentration (MIC), crystal violet staining, biofilm biomass measurement, propidium iodide uptake test, and adherence assay. And RT-PCR was carried out to measure the levels of RodA, RodB, Rho, FKs, CshA-D, RlmA, Cyp51A-B and Cdr1B. Network pharmacology analysis was applied to predict the relationship between BI and FK. Cell Count Kit-8 (CCK8) assay was utilized to detect the cytotoxicity of BI to RAW264.7 and immortalized human corneal epithelial cells (HCECs). The underlying mechanism of BI at regulating the level of inflammatory factors in FK was assessed by RT-PCR, ELISA and Western blot in vitro and in vivo. The therapeutic effect of BI has investigated in A. fumigatus keratitis by employing the clinical score, pathological examination, plate count, immunofluorescence and myeloperoxidase (MPO) assay. We also used the slit-lamp microscopy, clinical scores, and HE staining to assess the effect of natamycin compared with BI treatment in vivo.

RESULTS

BI suppressed the growth of A. fumigatus and had a significant effect on A. fumigatus biofilms and membrane permeability. RT-PCR demonstrated that exposure of A. fumigatus to BI inhibited the expression of genes that function in hydrophobin (RodA, RodB), cell wall integrity (Rho, FKs, CshA-D, RlmA), azole susceptibility (Cyp51A-B, Cdr1B). Network pharmacology showed that the effects of BI in FK implicate with C-type lectin receptor signaling pathway. In vivo, after A. fumigatus infection, BI treatment markedly reduced the severity of FK by decreasing clinical score, neutrophil recruitment, and fungal load. And BI treatment also obviously reduced the expression of inflammatory cytokines, Lectin-like oxidized LDL receptor (LOX-1), phosphorylation of p38MAPK and p-JNK versus the DMSO-treated group. BI and natamycin both significantly increased corneal transparency and decreased inflammatory cell recruitment in the FK in the mice model.

CONCLUSION

These results indicated that BI had fungicidal activities against A. fumigatus. It also ameliorated FK in mice by reducing inflammation, which was regulated by LOX-1, p-p38MAPK and p-JNK.