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N- glycoproteomic profiling revealing novel coronavirus therapeutic targets potentially involved in Cepharanthine's intervention.

ABSTRACT: The Coronavirus disease 2019 (COVID-19) has posed a serious threat to global health and the world economy. Antiviral therapies targeting coronavirus are urgently required. The Cepharanthine (CEP) is a traditional Chinese herbal extract. Our previous research revealed that CEP has a very potent anti-coronavirus effect, but its mechanism of action was not fully understood. To investigate the effect of novel coronavirus on protein glycosylation in infected cells and to further investigate the mechanism of action of CEP against coronavirus, a cellular model using coronavirus GX_P2V infection of Vero E6 cells was established. The effect of coronavirus GX_P2V on host cell protein glycosylation was investigated by N-glycoproteomic analysis, and the antagonistic effect of CEP on the abnormal protein glycosylation caused by coronavirus was analyzed. The results showed that GX_P2V could cause abnormal changes in protein glycosylation levels in host cells, while CEP could partially antagonize the abnormal protein glycosylation caused by GX_P2V. In addition, we also found that CEP could regulate the glycosylation level of coronavirus S protein. In conclusion, this article provides important ideas about the infection mechanism of novel coronaviruses, providing evidence for CEP as a promising therapeutic option for coronavirus infection.

SUBMITTER: An W

PROVIDER: S-EPMC9301903 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objectives: DFU is a serious chronic disease with high disability and fatality rates, yet there is no completely effective therapy. While ferroptosis is integrated to inflammation and infection, its involvement in DFU is still unclear. The study aimed to identify ferroptosis-related genes in DFU, providing potential therapeutic targets. Methods: In the GEO database, two DFU microarray datasets (GSE147890 and GSE80178) were collected. WGCNA was conducted to identify the modular genes most involved in DFU. Subsequently, enrichment analysis and PPI analysis were performed. To yield the DFU-associated ferroposis genes, the ferroposis genes were retrieved from the FerrDb database and overlapped with the modular genes. Eventually, an optimal DFU prediction model was created by combining multiple machine learning algorithms (LASSO, SVM-RFE, Boruta, and XGBoost) to detect ferroposis genes most closely associated with DFU. The accuracy of the model was verified by utilizing external datasets (GSE7014) based on ROC curves. Results: WGCNA yielded seven modules in all, and 1223 DFU-related modular genes were identified. GO analysis revealed that inflammatory response, decidualization, and protein binding were the most highly enriched terms. These module genes were also enriched in the ErbB signaling, IL-17 signaling, MAPK signaling, growth hormone synthesis, secretion and action, and tight junction KEGG pathways. Twenty-five DFU-associated ferroposis genes were obtained by cross-linking with modular genes, which could distinguish DFU patients from controls. Ultimately, the prediction model based on machine learning algorithms was well established, with high AUC values (0.79 of LASSO, 0.80 of SVM, 0.75 of Boruta, 0.70 of XGBoost). MAFG and MAPK3 were identified by the prediction model as the most highly associated ferroposis-genes in DFU. Furthermore, the external dataset (GSE29221) validation revealed that MAPK3 (AUC = 0.81) had superior AUC values than MAFG (AUC = 0.62). Conclusion: As the most related ferroptosis-genes with DFU, MAFG and MAPK3 may be employed as potential therapeutic targets for DFU patients. Moreover, MAPK3, with higher accuracy, could be the more potential ferroptosis-related biomarker for further experimental validation.

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N- glycoproteomic profiling revealing novel coronavirus therapeutic targets potentially involved in Cepharanthine's intervention.

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