ABSTRACT
Aim To explore the mechanism of Polygonum capitatum(PC)in the treatment of Helicobacter Pylori associated gastritis(HAG). Methods The databases were used to identify the target of PC active compounds and HAG-related genes,and the intersection was taken to obtain the potential targets of PC treatment of HAG. The interaction network diagram of “drug-active compound-target-disease” and the protein-protein interaction(PPI)network of potential target protein interaction in HAG treated by PC were constructed by software Cytoscape 3.6.0. The important nodes in the network were screened by several topological indexes,and the GO and KEGG enrichment were analyzed by STRING database to obtain the potential signaling pathway of PC in the treatment of HAG. The binding ability of PC active components with key target proteins was observed by molecular docking method. On this basis,the related targets of PC in the treatment of HAG were verified in vivo and in vitro experiments. Results The PC active compounds and targets were identified through the database,and the “drug-active compound-target-disease” network diagram and the PPI network of potential target proteins were constructed. Combined with several topological indexes,the PPI network of potential target-protein interaction was analyzed,and 52 hub genes were screened. Further bioinformatics analysis and high-throughput sequencing revealed that PC exerted an effect on HAG through the Akt/NF-κB/NLRP3 pathway. Based on this,it was found that PC could reduce IL-18 and IL-1β in HAG GES-1 cells and HAG SD rats,up-regulate Akt and its phosphorylation level and reduce NF-κB expression,inhibit the activation of NLRP3 inflammatory body,so as to improve HAG inflammatory response. Conclusions PC could exert a therapeutic effect on HAG by activating Akt and its phosphorylation level,and inhibiting the expression of NF-κB and NLRP3 inflammasome related factors. This study provides a theoretical basis for explaining the mechanism of PC in the treatment of HAG.
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Objective To explore the application of auto regressive time varying models in network building of time se-ries microarray data.Methods We used actual data to carry out a preliminary discussion about the properties of auto regressive time varying models.Results Analysis results of actual data suggested that auto regressive time varying models can perform well whether the number of timepoint is large or small,and it can recognize the network’s dynamic variation rule.Conclusion Auto regressive time varying models is applicable to network building of time series microarray data.
ABSTRACT
arrayImpute is a software for exploratory analysis of missing data and imputation of missing values in microarray data. It also provides a comparative analysis of the imputed values obtained from various imputation methods. Thus, it allows the users to choose an appropriate imputation method for microarray data. It is built on R and provides a user-friendly graphical interface. Therefore, the users can easily use arrayImpute to explore, estimate missing data, and compare imputation methods for further analysis.
ABSTRACT
Objective To deduce the interactions between genes from time series microarray data.Methods We used inter-transaction association rules mining technique and GO (Gene Ontology) annotation to analyze the microarray data. Results Using 2-fold-change method, 119 differential expression genes were identified from total 10 080 genes or ESTs, whose expression levels varied significantly on 6 periods of fetus cerebellar development. As a result, about 1 300 inter-transaction association rules were extracted and 10 top rules were kept for their maximum J-measure values. A genes association network graph was deduced based on the 10 top rules. Conclusion Inter-transaction association rules are able to deduce the interactions between genes from time series microarray data and the gene expression status can be predicted based on the association rules.