Objective:To construct SET gene recombinant adenovirus vector and SET gene small interfering RNA(SiRNA) recombinant adenovirus vector for over-expression or knock-down of SET levels. Methods:The cDNA sequence of SET w...Objective:To construct SET gene recombinant adenovirus vector and SET gene small interfering RNA(SiRNA) recombinant adenovirus vector for over-expression or knock-down of SET levels. Methods:The cDNA sequence of SET was cloned by reverse transcriptive polymerase chain reaction(RT-PCR) and the SET gene fragment was subcloned into adenovirus shuttle plasmid pAdTrack-CMV to construct the shuttle plasmid pAdTrack-SET.The shuttle plasmid pAdtrack-SET was transformed into BJ5183 cells with the adenoviral backbone pAdEasy-1 to obtain the homologous recombinant Ad-CMV-SET and the recombinant Ad-CMV-SET was packaged and amplified in the AD293 cells.The expression of SET in AD293 cells was detected by Western blot.In addition,we constructed SET gene SiRNA recombinant adenovirus vector(Ad-H1-SiRNA/SET) and its efficacy of knockdown of SET protein was detected in infected GC-2spd(ts) cells by Western blot. Results:The recombinant adenovirus vectors,both SET gene recombinant adenovirus vector Ad-CMV-SET and SET gene SiRNA recombinant adenovirus vector Ad-H1-SiRNA/SET,were proven to be constructed successfully by the evidence of endonulease digestion and sequencing.AD293 cells infected with either recombinant adenovirus vector of Ad-CMV-SET or Ad-H1-SiRNA/SET were observed to express GFP.The expression of SET protein was up-regulated significantly in AD293 cells infected with SET gene recombinant adenovirus vector.On the contrast, SET protein was significantly down-regulated in the GC-2spd(ts) cells infected with Ad-H1-SiRNA/SET (P<0.05) and the knockdown efficiency was approximately 50%-70%. Conclusion:The recombinant adenovirus vector Ad-CMV-SET and Ad-H1-SiRNA/SET were successfully constructed and effectively expressed in germ cells and somatic cells.It provides an experimental tool for further study of SET gene in the physiological and pathophysiological mechanism of reproduction-related diseases.展开更多
Gene set enrichment（GSE） analyses play an important role in the interpretation of large-scale transcriptome datasets. Multiple GSE tools can be integrated into a single method as obtaining optimal results is challen...Gene set enrichment（GSE） analyses play an important role in the interpretation of large-scale transcriptome datasets. Multiple GSE tools can be integrated into a single method as obtaining optimal results is challenging due to the plethora of GSE tools and their discrepant performances. Several existing ensemble methods lead to different scores in sorting pathways as integrated results; furthermore, it is difficult for users to choose a single ensemble score to obtain optimal final results. Here, we develop an ensemble method using a machine learning approach called Combined Gene set analysis incorporating Prioritization and Sensitivity（CGPS） that integrates the results provided by nine prominent GSE tools into a single ensemble score（R score） to sort pathways as integrated results. Moreover, to the best of our knowledge, CGPS is the first GSE ensemble method built based on a priori knowledge of pathways and phenotypes. Compared with 10 widely used individual methods and five types of ensemble scores from two ensemble methods, we demonstrate that sorting pathways based on the R score can better prioritize relevant pathways, as established by an evaluation of 120 simulated datasets and 45 real datasets.Additionally, CGPS is applied to expression data involving the drug panobinostat, which is an anticancer treatment against multiple myeloma. The results identify cell processes associated with cancer, such as the p53 signaling pathway（hsa04115）; by contrast, according to two ensemble methods（EnrichmentBrowser and EGSEA）, this pathway has a rank higher than 20, which may cause users to miss the pathway in their analyses. We show that this method, which is based on a priori knowledge, can capture valuable biological information from numerous types of gene set collections, such as KEGG pathways, GO terms, Reactome, and BioCarta. CGPS is publicly available as a standalone source code at ftp：//ftp.cbi.pku.edu.cn/pub/CGPS_download/cgps-1.0.0.tar.gz.展开更多
Objective:To identify alpha-glucosidase inhibitors from Ficus benghalensis and analyze gene set enrichment of regulated protein molecules.Methods:The phytoconstituents of Ficu.s benghalen.sis were queried for inhibito...Objective:To identify alpha-glucosidase inhibitors from Ficus benghalensis and analyze gene set enrichment of regulated protein molecules.Methods:The phytoconstituents of Ficu.s benghalen.sis were queried for inhibitors of alphaglucosidase,also identified as aldose reductase inhibitors.Druglikeness score,absorption,distribution,metabolism,excretion and toxicity profile,biological spectrum,and gene expression were predicated for each compound.Docking study was performed to predict the binding affinity with alpha-glucosidase and aldose reductase and compared with clinically proven molecules.Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed for the regulated genes to identify the modulated pathways.Results:Apigenin,3,4’,5,7-tetrahydroxy-3’-methoxyflavone,and kaempferol were identified as inhibitors of alpha-glucosidase and aldose reductase.Kaempferol was predicted to possess the highest binding affinity with both targets.The p53 signaling pathway was predicted to modulate the majority of protein molecules in diabetes mellitus.All the alpha-glucosidase inhibitors were also predicted as membrane integrity agonist and anti-mutagenic compounds.Conclusions:The current study indicates alpha-glucosidase inhibitors from Ficus benghale,nsis can act as aldose reductase inhibitors after absorption from the intestinal tract.Furthermore,these phytoconstituents are involved in the regulation of numerous protein molecules and pathways.Hence,the anti-diabetic efficacies of these compounds are due to their action on multiple protein molecules and synergistic effects which should be confirmed by future investigations.展开更多
Objective:Based on bioinformatics,gene set enrichment analysis(GSEA)and immune infiltration analysis were carried out on the microarray data of psoriasis expression profile to further understand the pathogenesis of ps...Objective:Based on bioinformatics,gene set enrichment analysis(GSEA)and immune infiltration analysis were carried out on the microarray data of psoriasis expression profile to further understand the pathogenesis of psoriasis.Methods:GSE6710 chip data were obtained from gene expression database(GEO),and gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were performed using GSEA software.22 kinds of immune cell gene expression matrices and R packages were downloaded from CIBERSOFT official website,and the immune cell infiltration matrix was obtained by R software and related graphs were drawn.Results:The pathways related to cell proliferation and innate immunity were highly expressed in psoriatic lesions,and some cancer-related pathways were highly expressed in psoriatic lesions.Immunized cell infiltration analysis showed that activated memory T cells,follicular helper T cells,M0 macrophages and activated dendritic cells were up-regulated in psoriatic skin lesion group,and inactive mast cells were down-regulated in psoriatic skin lesion group.Activated dendritic cells are positively correlated with follicular helper T cells,activated mast cells are positively correlated with M0 macrophages.Inactivated mast cells are negatively correlated with activated memory T cells,M1 macrophages are negatively correlated with regulatory T cells,M0 macrophages are negatively correlated with inactive mast cells.Conclusion:Cell proliferation and innate immunity are of great significance in the pathogenesis of psoriasis.Immune cell infiltration analysis is generally consistent with the current psoriasis pathogenesis model.Macrophages and mast cells also play a certain role in psoriasis.展开更多
It is very important in the field of bioinformatics to apply computer to perform the function annotation for new sequenced bio-sequences. Based on GO database and BLAST program, a novel method for the function annotat...It is very important in the field of bioinformatics to apply computer to perform the function annotation for new sequenced bio-sequences. Based on GO database and BLAST program, a novel method for the function annotation of new biological sequences is presented by using the variable-precision rough set theory. The proposed method is applied to the real data in GO database to examine its effectiveness. Numerical results show that the proposed method has better precision, recall-rate and harmonic mean value compared with existing methods.展开更多
Gene Set Analysis (GSA) is a framework for testing the association of a set of genes and the outcome, e.g. disease status or treatment group. The method replies on computing a maxmean statistic and estimating the null...Gene Set Analysis (GSA) is a framework for testing the association of a set of genes and the outcome, e.g. disease status or treatment group. The method replies on computing a maxmean statistic and estimating the null distribution of the maxmean statistics via a restandardization procedure. In practice, the pre-determined gene sets have stronger intra-correlation than genes across sets. This may result in biases in the estimated null distribution. We derive an asymptotic null distribution of the maxmean statistics based on sparsity assumption. We propose a flexible two group mixture model for the maxmean statistics. The mixture model allows us to estimate the null parameters empirically via maximum likelihood approach. Our empirical method is compared with the restandardization procedure of GSA in simulations. We show that our method is more accurate in null density estimation when the genes are strongly correlated within gene sets.展开更多
基金Projects from China National Department of Science and Technology(81070465)a project from China Jiangsu Province (BM2008151)
文摘Objective:To construct SET gene recombinant adenovirus vector and SET gene small interfering RNA(SiRNA) recombinant adenovirus vector for over-expression or knock-down of SET levels. Methods:The cDNA sequence of SET was cloned by reverse transcriptive polymerase chain reaction(RT-PCR) and the SET gene fragment was subcloned into adenovirus shuttle plasmid pAdTrack-CMV to construct the shuttle plasmid pAdTrack-SET.The shuttle plasmid pAdtrack-SET was transformed into BJ5183 cells with the adenoviral backbone pAdEasy-1 to obtain the homologous recombinant Ad-CMV-SET and the recombinant Ad-CMV-SET was packaged and amplified in the AD293 cells.The expression of SET in AD293 cells was detected by Western blot.In addition,we constructed SET gene SiRNA recombinant adenovirus vector(Ad-H1-SiRNA/SET) and its efficacy of knockdown of SET protein was detected in infected GC-2spd(ts) cells by Western blot. Results:The recombinant adenovirus vectors,both SET gene recombinant adenovirus vector Ad-CMV-SET and SET gene SiRNA recombinant adenovirus vector Ad-H1-SiRNA/SET,were proven to be constructed successfully by the evidence of endonulease digestion and sequencing.AD293 cells infected with either recombinant adenovirus vector of Ad-CMV-SET or Ad-H1-SiRNA/SET were observed to express GFP.The expression of SET protein was up-regulated significantly in AD293 cells infected with SET gene recombinant adenovirus vector.On the contrast, SET protein was significantly down-regulated in the GC-2spd(ts) cells infected with Ad-H1-SiRNA/SET (P<0.05) and the knockdown efficiency was approximately 50%-70%. Conclusion:The recombinant adenovirus vector Ad-CMV-SET and Ad-H1-SiRNA/SET were successfully constructed and effectively expressed in germ cells and somatic cells.It provides an experimental tool for further study of SET gene in the physiological and pathophysiological mechanism of reproduction-related diseases.
基金supported by the National Key Research and Development Program of China (2017YFC1201200,2017YFC0908404,2016YFC0901603,2016YFB0201700)National High-tech R&D Program of China (863 Program) (2015AA020108)the State Key Laboratory of Protein and Plant Gene Research
文摘Gene set enrichment（GSE） analyses play an important role in the interpretation of large-scale transcriptome datasets. Multiple GSE tools can be integrated into a single method as obtaining optimal results is challenging due to the plethora of GSE tools and their discrepant performances. Several existing ensemble methods lead to different scores in sorting pathways as integrated results; furthermore, it is difficult for users to choose a single ensemble score to obtain optimal final results. Here, we develop an ensemble method using a machine learning approach called Combined Gene set analysis incorporating Prioritization and Sensitivity（CGPS） that integrates the results provided by nine prominent GSE tools into a single ensemble score（R score） to sort pathways as integrated results. Moreover, to the best of our knowledge, CGPS is the first GSE ensemble method built based on a priori knowledge of pathways and phenotypes. Compared with 10 widely used individual methods and five types of ensemble scores from two ensemble methods, we demonstrate that sorting pathways based on the R score can better prioritize relevant pathways, as established by an evaluation of 120 simulated datasets and 45 real datasets.Additionally, CGPS is applied to expression data involving the drug panobinostat, which is an anticancer treatment against multiple myeloma. The results identify cell processes associated with cancer, such as the p53 signaling pathway（hsa04115）; by contrast, according to two ensemble methods（EnrichmentBrowser and EGSEA）, this pathway has a rank higher than 20, which may cause users to miss the pathway in their analyses. We show that this method, which is based on a priori knowledge, can capture valuable biological information from numerous types of gene set collections, such as KEGG pathways, GO terms, Reactome, and BioCarta. CGPS is publicly available as a standalone source code at ftp：//ftp.cbi.pku.edu.cn/pub/CGPS_download/cgps-1.0.0.tar.gz.
文摘Objective:To identify alpha-glucosidase inhibitors from Ficus benghalensis and analyze gene set enrichment of regulated protein molecules.Methods:The phytoconstituents of Ficu.s benghalen.sis were queried for inhibitors of alphaglucosidase,also identified as aldose reductase inhibitors.Druglikeness score,absorption,distribution,metabolism,excretion and toxicity profile,biological spectrum,and gene expression were predicated for each compound.Docking study was performed to predict the binding affinity with alpha-glucosidase and aldose reductase and compared with clinically proven molecules.Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed for the regulated genes to identify the modulated pathways.Results:Apigenin,3,4’,5,7-tetrahydroxy-3’-methoxyflavone,and kaempferol were identified as inhibitors of alpha-glucosidase and aldose reductase.Kaempferol was predicted to possess the highest binding affinity with both targets.The p53 signaling pathway was predicted to modulate the majority of protein molecules in diabetes mellitus.All the alpha-glucosidase inhibitors were also predicted as membrane integrity agonist and anti-mutagenic compounds.Conclusions:The current study indicates alpha-glucosidase inhibitors from Ficus benghale,nsis can act as aldose reductase inhibitors after absorption from the intestinal tract.Furthermore,these phytoconstituents are involved in the regulation of numerous protein molecules and pathways.Hence,the anti-diabetic efficacies of these compounds are due to their action on multiple protein molecules and synergistic effects which should be confirmed by future investigations.
基金Beijing Key Laboratory of Clinical Basic Research on Psoriasis of Traditional Chinese Medicine(No.BZ0375-KF201602)。
文摘Objective:Based on bioinformatics,gene set enrichment analysis(GSEA)and immune infiltration analysis were carried out on the microarray data of psoriasis expression profile to further understand the pathogenesis of psoriasis.Methods:GSE6710 chip data were obtained from gene expression database(GEO),and gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were performed using GSEA software.22 kinds of immune cell gene expression matrices and R packages were downloaded from CIBERSOFT official website,and the immune cell infiltration matrix was obtained by R software and related graphs were drawn.Results:The pathways related to cell proliferation and innate immunity were highly expressed in psoriatic lesions,and some cancer-related pathways were highly expressed in psoriatic lesions.Immunized cell infiltration analysis showed that activated memory T cells,follicular helper T cells,M0 macrophages and activated dendritic cells were up-regulated in psoriatic skin lesion group,and inactive mast cells were down-regulated in psoriatic skin lesion group.Activated dendritic cells are positively correlated with follicular helper T cells,activated mast cells are positively correlated with M0 macrophages.Inactivated mast cells are negatively correlated with activated memory T cells,M1 macrophages are negatively correlated with regulatory T cells,M0 macrophages are negatively correlated with inactive mast cells.Conclusion:Cell proliferation and innate immunity are of great significance in the pathogenesis of psoriasis.Immune cell infiltration analysis is generally consistent with the current psoriasis pathogenesis model.Macrophages and mast cells also play a certain role in psoriasis.
基金the support of the National Natural Science Foundation of China under Grant No.60673023,60433020,10501017,3040016the European Commission for TH/Asia Link/010 under Grant No.111084.
文摘It is very important in the field of bioinformatics to apply computer to perform the function annotation for new sequenced bio-sequences. Based on GO database and BLAST program, a novel method for the function annotation of new biological sequences is presented by using the variable-precision rough set theory. The proposed method is applied to the real data in GO database to examine its effectiveness. Numerical results show that the proposed method has better precision, recall-rate and harmonic mean value compared with existing methods.
文摘Gene Set Analysis (GSA) is a framework for testing the association of a set of genes and the outcome, e.g. disease status or treatment group. The method replies on computing a maxmean statistic and estimating the null distribution of the maxmean statistics via a restandardization procedure. In practice, the pre-determined gene sets have stronger intra-correlation than genes across sets. This may result in biases in the estimated null distribution. We derive an asymptotic null distribution of the maxmean statistics based on sparsity assumption. We propose a flexible two group mixture model for the maxmean statistics. The mixture model allows us to estimate the null parameters empirically via maximum likelihood approach. Our empirical method is compared with the restandardization procedure of GSA in simulations. We show that our method is more accurate in null density estimation when the genes are strongly correlated within gene sets.
