The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration,at which a drastic and qualitative shift occurs.The deve...The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration,at which a drastic and qualitative shift occurs.The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration,which allows the timely implementation of appropriate measures to prevent a catastrophic transition.However,identifying the pre-deterioration stage is a challenging task in clinical medicine,especially when only a single sample is available for most patients,which is responsible for the failure of most statistical methods.In this study,a novel computational method,called single-sample network module biomarkers(sNMB),is presented to predict the pre-deterioration stage or critical point using only a single sample.Specifically,the proposed single-sample index effectively quantifies the disturbance caused by a single sample against a group of given reference samples.Our method successfully detected the early warning signal of the critical transitions when applied to both a numerical simulation and four real datasets,including acute lung injury,stomach adenocarcinoma,esophageal carcinoma,and rectum adenocarcinoma.In addition,it provides signaling biomarkers for further practical application,which helps to discover prognostic indicators and reveal the underlying molecular mechanisms of disease progression.展开更多
Background:Ketamine is a non-competitive N-methyl-D-aspartate(NMDA)receptor antagonist.It has attracted considerable attention for its rapid antidepressant effects in recent years,but ketamine-induced psychotic-like s...Background:Ketamine is a non-competitive N-methyl-D-aspartate(NMDA)receptor antagonist.It has attracted considerable attention for its rapid antidepressant effects in recent years,but ketamine-induced psychotic-like symptoms limit its clinical application.The molecular mechanisms and key targets underlying ketamine-induced psychiatric disorders remain unclear.Aims and Objectives:In this study,we utilized multi-brain region transcriptome data and bioinformatics methods to identify the key genes and pathways involved.Materials and Methods:First,we obtained transcriptome data of ketamine-treated and control brain tissues(including frontal cortex,hippocampus,striatum,and amygdala)from public databases(GEO).Simultaneously,we retrieved psychiatric disorder-related gene sets from the GeneCards database.For each brain region sample,we performed single-sample gene set enrichment analysis(ssGSEA)to calculate enrichment scores for the psychiatric disorder gene set and assess differences between groups.We applied Weighted Gene Co-expression Network Analysis(WGCNA)to identify gene modules associated with the high-expression phenotype and conducted Gene Ontology(GO)functional annotation.In each brain region,differentially expressed genes(DEGs)between the high-expression and control groups were identified and intersected with WGCNA modules to obtain candidate key genes.Based on these candidates,we used three machine learning algorithms(least absolute shrinkage and selection operator(LASSO)regression,support vector machine recursive feature elimination(SVM-RFE),and Random Forest)to obtain 12 sets of candidate feature genes,comparing model performance using receiver operating characteristic(ROC)curves and area under the curve(AUC).Results:The results indicated that the LASSO model for the frontal cortex exhibited the best performance,identifying nine feature genes(Galr1,Cbr3,Crem,Fosl2,Mypn,Maff,Rhbg,Tslp,Klra2).Further GO/KEGG enrichment analysis and protein-protein interaction(PPI)network analysis highlighted the close association of Fosl2 and Maff with ketamine-induced psychiatric disorders.Comparison with our prior proteomic data on the prefrontal cortex of a ketamine model revealed a markedly downregulated protein Cbr3.Subsequent quantitative polymerase chain reaction(qPCR)assays in a ketamine-induced psychiatric disorder mouse model confirmed these findings:Cbr3 was significantly downregulated,while Fosl2 and Maff were significantly upregulated in the prefrontal cortex,consistent with our analysis.Thus,Cbr3,Fosl2,and Maff were identified as core genes in ketamine-induced psychiatric disorders.Finally,we evaluated the correlation between these core genes and immune cell infiltration,and analyzed their functions in humans using Genotype-Tissue Expression(GTEx)data and genome-wide association study(GWAS)loci.Conclusion:This study comprehensively applied gene set enrichment,WGCNA,and machine learning to multi-brain region transcriptomes to systematically screen for potential core genes of ketamine-induced psychiatric disorders,with preliminary qPCR validation.These findings provide new insights into molecular markers and mechanisms in this field.展开更多
Skin,as the outmost layer of human body,is frequently exposed to environmental stressors including pollutants and ultraviolet(UV),which could lead to skin disorders.Generally,skin response process to ultraviolet B(UVB...Skin,as the outmost layer of human body,is frequently exposed to environmental stressors including pollutants and ultraviolet(UV),which could lead to skin disorders.Generally,skin response process to ultraviolet B(UVB)irradiation is a nonlinear dynamic process,with unknown underlying molecular mechanism of critical transition.Here,the landscape dynamic network biomarker(lDNB)analysis of time series transcriptome data on 3D skin model was conducted to reveal the complicated process of skin response to UV irradiation at both molecular and network levels.The advanced l-DNB analysis approach showed that:(i)there was a tipping point before critical transition state during pigmentation process,validated by 3D skin model;(ii)13 core DNB genes were identified to detect the tipping point as a network biomarker,supported by computational assessment;(iii)core DNB genes such as COL7A1 and CTNNB1 can effectively predict skin lightening,validated by independent human skin data.Overall,this study provides new insights for skin response to repetitive UVB irradiation,including dynamic pathway pattern,biphasic response,and DNBs for skin lightening change,and enables us to further understand the skin resilience process after external stress.展开更多
基金supported by the National Natural Science Foundation of China(12026608,62172164,12131020,and 12271180)the Natural Science Foundation of Guangdong Province(2021A1515012317).
文摘The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration,at which a drastic and qualitative shift occurs.The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration,which allows the timely implementation of appropriate measures to prevent a catastrophic transition.However,identifying the pre-deterioration stage is a challenging task in clinical medicine,especially when only a single sample is available for most patients,which is responsible for the failure of most statistical methods.In this study,a novel computational method,called single-sample network module biomarkers(sNMB),is presented to predict the pre-deterioration stage or critical point using only a single sample.Specifically,the proposed single-sample index effectively quantifies the disturbance caused by a single sample against a group of given reference samples.Our method successfully detected the early warning signal of the critical transitions when applied to both a numerical simulation and four real datasets,including acute lung injury,stomach adenocarcinoma,esophageal carcinoma,and rectum adenocarcinoma.In addition,it provides signaling biomarkers for further practical application,which helps to discover prognostic indicators and reveal the underlying molecular mechanisms of disease progression.
基金funded by the National Natural Science Foundation of China(No.82030057 and 82072111)Sichuan Provincial Department of Science and Technology Project(No.2024NSFSC0531).
文摘Background:Ketamine is a non-competitive N-methyl-D-aspartate(NMDA)receptor antagonist.It has attracted considerable attention for its rapid antidepressant effects in recent years,but ketamine-induced psychotic-like symptoms limit its clinical application.The molecular mechanisms and key targets underlying ketamine-induced psychiatric disorders remain unclear.Aims and Objectives:In this study,we utilized multi-brain region transcriptome data and bioinformatics methods to identify the key genes and pathways involved.Materials and Methods:First,we obtained transcriptome data of ketamine-treated and control brain tissues(including frontal cortex,hippocampus,striatum,and amygdala)from public databases(GEO).Simultaneously,we retrieved psychiatric disorder-related gene sets from the GeneCards database.For each brain region sample,we performed single-sample gene set enrichment analysis(ssGSEA)to calculate enrichment scores for the psychiatric disorder gene set and assess differences between groups.We applied Weighted Gene Co-expression Network Analysis(WGCNA)to identify gene modules associated with the high-expression phenotype and conducted Gene Ontology(GO)functional annotation.In each brain region,differentially expressed genes(DEGs)between the high-expression and control groups were identified and intersected with WGCNA modules to obtain candidate key genes.Based on these candidates,we used three machine learning algorithms(least absolute shrinkage and selection operator(LASSO)regression,support vector machine recursive feature elimination(SVM-RFE),and Random Forest)to obtain 12 sets of candidate feature genes,comparing model performance using receiver operating characteristic(ROC)curves and area under the curve(AUC).Results:The results indicated that the LASSO model for the frontal cortex exhibited the best performance,identifying nine feature genes(Galr1,Cbr3,Crem,Fosl2,Mypn,Maff,Rhbg,Tslp,Klra2).Further GO/KEGG enrichment analysis and protein-protein interaction(PPI)network analysis highlighted the close association of Fosl2 and Maff with ketamine-induced psychiatric disorders.Comparison with our prior proteomic data on the prefrontal cortex of a ketamine model revealed a markedly downregulated protein Cbr3.Subsequent quantitative polymerase chain reaction(qPCR)assays in a ketamine-induced psychiatric disorder mouse model confirmed these findings:Cbr3 was significantly downregulated,while Fosl2 and Maff were significantly upregulated in the prefrontal cortex,consistent with our analysis.Thus,Cbr3,Fosl2,and Maff were identified as core genes in ketamine-induced psychiatric disorders.Finally,we evaluated the correlation between these core genes and immune cell infiltration,and analyzed their functions in humans using Genotype-Tissue Expression(GTEx)data and genome-wide association study(GWAS)loci.Conclusion:This study comprehensively applied gene set enrichment,WGCNA,and machine learning to multi-brain region transcriptomes to systematically screen for potential core genes of ketamine-induced psychiatric disorders,with preliminary qPCR validation.These findings provide new insights into molecular markers and mechanisms in this field.
基金partially supported by the National Natural Science Foundation of China(31930022,31771476,12026608,12042104,and 11871456)the Strategic Priority Project of CAS(XDB38040400)+1 种基金the National Key R&D Program of China(2017YFA0505500)JST Moonshot R&D program(JP MJMS2021 to L.C.).
文摘Skin,as the outmost layer of human body,is frequently exposed to environmental stressors including pollutants and ultraviolet(UV),which could lead to skin disorders.Generally,skin response process to ultraviolet B(UVB)irradiation is a nonlinear dynamic process,with unknown underlying molecular mechanism of critical transition.Here,the landscape dynamic network biomarker(lDNB)analysis of time series transcriptome data on 3D skin model was conducted to reveal the complicated process of skin response to UV irradiation at both molecular and network levels.The advanced l-DNB analysis approach showed that:(i)there was a tipping point before critical transition state during pigmentation process,validated by 3D skin model;(ii)13 core DNB genes were identified to detect the tipping point as a network biomarker,supported by computational assessment;(iii)core DNB genes such as COL7A1 and CTNNB1 can effectively predict skin lightening,validated by independent human skin data.Overall,this study provides new insights for skin response to repetitive UVB irradiation,including dynamic pathway pattern,biphasic response,and DNBs for skin lightening change,and enables us to further understand the skin resilience process after external stress.
