Lentinan,a polysaccharide derived from Lentinula edodes,has garnered attention for its anti-inflammatory and immunomodulatory properties,yet its therapeutic mechanisms in asthma remain inadequately characterized.Our s...Lentinan,a polysaccharide derived from Lentinula edodes,has garnered attention for its anti-inflammatory and immunomodulatory properties,yet its therapeutic mechanisms in asthma remain inadequately characterized.Our study employs a novel integrative pipeline combining network pharmacology,machine learning,Mendelian randomization(MR),molecular docking,and in vitro experimental validation to unravel lentinan’s molecular mechanisms and therapeutic potential in asthma.Initial bioinformatics analyses identified seven hub genes through protein-protein interaction(PPI)network construction,which were further refined to four candidate targets using LASSO regression,random forest(RF),and support vector machine-recursive feature elimination(SVM-RFE).Subsequent MR analysis prioritized STAT3 and TP53 as key therapeutic targets in asthma.Molecular docking and dynamics simulations revealed strong binding affinities of lentinan to STAT3 and TP53,corroborating their functional relevance.Experimental validation using LPS-induced RAW264.7 macrophage cells demonstrated that lentinan significantly downregulated phosphorylation levels of STAT3 and TP53,inhibited macrophage activation,and suppressed TNF-α,IL-6,IL-1βsecretion.Additionally,lentinan modulated the PI3K/Akt signaling pathway,further substantiating its anti-inflammatory effects.This comprehensive computationalexperimental framework highlights lentinan’s therapeutic potential in asthma by targeting critical inflammatory pathways,warranting further investigation into its clinical applications.展开更多
Animal experiments traditionally identify sensitizers in cosmetic materials.However,with growing concerns over animal ethics and bans on such experiments globally,alternative methods like machine learning are gaining ...Animal experiments traditionally identify sensitizers in cosmetic materials.However,with growing concerns over animal ethics and bans on such experiments globally,alternative methods like machine learning are gaining prominence for their efficiency and cost-effectiveness.In this study,to develop a robust sensitizer detector model,we first constructed benchmark data sets using data from previous studies and a public database,then 589 sensitizers and 831 nonsensitizers were collected.In addition,a graph-based autoencoder and Mondrian conformal prediction(MCP)were combined to build a robust sensitizer detector,iSKIN.In the independent test set,the Matthews correlation coefficient(MCC)and the area under the receiver operating characteristic curve(ROCAUC)values of the iSKIN model without MCP were 0.472 and 0.804,respectively,which are higher than those of the three baseline models.When setting the significance level in MCP at 0.7,the MCC and ROCAUC values of iSKIN could achieve 0.753 and 0.927,respectively.Regrouping experiments proved that the MCP method is robust in the improvement of model performance.Through key structure analysis,seven key substructures in sensitizers were identified to guide cosmetic material design.Notably,long chains with halogen atoms and phenyl groups with two chlorine atoms at ortho-positions were potential sensitizers.Finally,a userfriendly web tool(http://www.iskin.work/)of the iSKIN model was deployed to be used by other researchers.In summary,the proposed iSKIN model has achieved state-of-the-art performance so far,which can contribute to the safety evaluation of cosmetic raw materials and provide a reference for the chemical structure design of these materials.展开更多
基金supported by the K.Albin Johansson Stiftelse Foun-dation(2023)the Maud Kuistila Memorial Foundation sr(No.2024-0147B)the Finnish Cultural Foundation(No.00241240)。
文摘Lentinan,a polysaccharide derived from Lentinula edodes,has garnered attention for its anti-inflammatory and immunomodulatory properties,yet its therapeutic mechanisms in asthma remain inadequately characterized.Our study employs a novel integrative pipeline combining network pharmacology,machine learning,Mendelian randomization(MR),molecular docking,and in vitro experimental validation to unravel lentinan’s molecular mechanisms and therapeutic potential in asthma.Initial bioinformatics analyses identified seven hub genes through protein-protein interaction(PPI)network construction,which were further refined to four candidate targets using LASSO regression,random forest(RF),and support vector machine-recursive feature elimination(SVM-RFE).Subsequent MR analysis prioritized STAT3 and TP53 as key therapeutic targets in asthma.Molecular docking and dynamics simulations revealed strong binding affinities of lentinan to STAT3 and TP53,corroborating their functional relevance.Experimental validation using LPS-induced RAW264.7 macrophage cells demonstrated that lentinan significantly downregulated phosphorylation levels of STAT3 and TP53,inhibited macrophage activation,and suppressed TNF-α,IL-6,IL-1βsecretion.Additionally,lentinan modulated the PI3K/Akt signaling pathway,further substantiating its anti-inflammatory effects.This comprehensive computationalexperimental framework highlights lentinan’s therapeutic potential in asthma by targeting critical inflammatory pathways,warranting further investigation into its clinical applications.
基金the Finland EDUFI Foundation(J.Z.),the Finland Biomedicum Foundation 2022(W.K.)the K.Albin Johansson Stiftelse Founda-tion 2022(J.Z.and W.K.)+1 种基金the Youth Innovation Team of Shandong Province(No.2022KJ145,S.Z.)the Ningxia Hui Autonomous Region Key Research and Development Project(No.2022BEG02042,Z.H.)。
文摘Animal experiments traditionally identify sensitizers in cosmetic materials.However,with growing concerns over animal ethics and bans on such experiments globally,alternative methods like machine learning are gaining prominence for their efficiency and cost-effectiveness.In this study,to develop a robust sensitizer detector model,we first constructed benchmark data sets using data from previous studies and a public database,then 589 sensitizers and 831 nonsensitizers were collected.In addition,a graph-based autoencoder and Mondrian conformal prediction(MCP)were combined to build a robust sensitizer detector,iSKIN.In the independent test set,the Matthews correlation coefficient(MCC)and the area under the receiver operating characteristic curve(ROCAUC)values of the iSKIN model without MCP were 0.472 and 0.804,respectively,which are higher than those of the three baseline models.When setting the significance level in MCP at 0.7,the MCC and ROCAUC values of iSKIN could achieve 0.753 and 0.927,respectively.Regrouping experiments proved that the MCP method is robust in the improvement of model performance.Through key structure analysis,seven key substructures in sensitizers were identified to guide cosmetic material design.Notably,long chains with halogen atoms and phenyl groups with two chlorine atoms at ortho-positions were potential sensitizers.Finally,a userfriendly web tool(http://www.iskin.work/)of the iSKIN model was deployed to be used by other researchers.In summary,the proposed iSKIN model has achieved state-of-the-art performance so far,which can contribute to the safety evaluation of cosmetic raw materials and provide a reference for the chemical structure design of these materials.
