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Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases. Therefore, it is important to obtain reliable similarity information by integrating multiple types of similarity information. However, existing similarity fusion methods do not consider multi-order fusion of similarity networks. To address this problem, a novel method of linear neighborhood label propagation with multi-order similarity fusion learning (MOSFL-LNP) is proposed to predict potential microbe-disease associations. Multi-order fusion learning comprises two parts: low-order global learning and high-order feature learning. Low-order global learning is used to obtain common latent features from multiple similarity sources. High-order feature learning relies on the interactions between neighboring nodes to identify high-order similarities and learn deeper interactive network structures. Coefficients are assigned to different high-order feature learning modules to balance the similarities learned from different orders and enhance the robustness of the fusion network. Overall, by combining low-order global learning with high-order feature learning, multi-order fusion learning can capture both the shared and unique features of different similarity networks, leading to more accurate predictions of microbe-disease associations. In comparison to six other advanced methods, MOSFL-LNP exhibits superior prediction performance in the leave-one-out cross-validation and 5-fold validation frameworks. In the case study, the predicted 10 microbes associated with asthma and type 1 diabetes have an accuracy rate of up to 90% and 100%, respectively. [ABSTRACT FROM AUTHOR]

Background: Liver fibrosis can progress to cirrhosis and hepatic carcinoma without treatment. CircDCBLD2 was found to be downregulated in liver fibrosis. However, the precise underlying mechanism requires further investigation. Methods: qRT-PCR, Western blot, and immunohistochemistry assays were used to detect the related molecule levels. HE, Masson's trichrome, and Sirius Red staining were used to assess the pathological changes in mice's liver tissues. Flow cytometric analysis and commercial kit were used to assess the levels of lipid reactive oxygen species (ROS), malonaldehyde (MDA), glutathione (GSH), and iron. Cell viability was assessed by MTT. Immunoprecipitation was used to study the ubiquitination of PARK7. Mitophagy was determined by immunostaining and confocal imaging. RIP and Co-IP assays were used to assess the interactions of circDCBLD2/HuR, HuR/STUB1, and STUB1/PARK7. Fluorescence in situ hybridization and immunofluorescence staining were used to assess the co-localization of circDCBLD2 and HuR. Results: CircDCBLD2 was downregulated, whereas PARK7 was upregulated in liver fibrosis. Ferroptosis activators increased circDCBLD2 while decreasing PARK7 in hepatic stellate cells (HSCs) and mice with liver fibrosis. CircDCBLD2 overexpression reduced cell viability and GSH, PARK7, and GPX4 expression in erastin-treated HSCs while increasing MDA and iron levels, whereas circDCBLD2 knockdown had the opposite effect. CircDCBLD2 overexpression increased STUB1-mediated PARK7 ubiquitination by promoting HuR-STUB1 binding and thus increasing STUB1 mRNA stability. PARK7 overexpression or HuR knockdown reversed the effects of circDCBLD2 overexpression on HSC activation and ferroptosis. CircDCBLD2 reduced liver fibrosis in mice by inhibiting PARK7. Conclusion: CircDCBLD2 overexpression increased PARK7 ubiquitination degradation by upregulating STUB1 through its interaction with HuR, inhibiting HSC activation and promoting HSC ferroptosis, ultimately enhancing liver fibrosis. [ABSTRACT FROM AUTHOR]